GigaOm Radar for High-Performance Storage Optimized for AI Workloads v1

High-performance storage optimized for AI workloads signifies a crucial evolution in storage technology, specifically engineered to address the rigorous demands of artificial intelligence (AI) and machine learning (ML) applications. These solutions surpass traditional storage systems by delivering the high throughput, low latency, and massive concurrency necessary for data-intensive AI/ML operations. Their importance lies in enabling organizations to accelerate model training, improve prediction accuracy, enhance the efficiency of expensive compute resources like GPUs, and ultimately derive greater value from AI investments. This technology matters to a broad audience, including data scientists, ML engineers, IT operations teams, and C-level executives, all of whom are invested in the success of AI/ML initiatives.

From a CxO perspective, adopting high-performance storage optimized for AI workloads is a strategic business imperative, not merely a technical upgrade. These systems directly influence an organization's capacity to compete effectively in the AI era by speeding up the development and deployment of AI-powered applications, leading to faster innovation and quicker time to market. Furthermore, they enhance the return on AI investments by optimizing workflows, reducing operational costs, and maximizing the utilization of valuable compute and human resources. They underpin large-scale, data-driven decision-making, allowing businesses to extract actionable insights, make informed choices rapidly, and leverage advancements like Generative AI for training and inferencing.  

Inclusion in this GigaOm Radar report requires solutions to meet the foundational table stakes outlined in the companion Key Criteria report, establishing essential functionality for AI workloads. Our research also highlighted the growing importance of specific industry certifications, such as those offered by NVIDIA for its DGX ecosystem, which vendors are increasingly pursuing to validate performance in demanding AI environments. While these specific certifications are not universal vendor inclusion criteria for this initial report, their maturation and adoption will be considered for formal evaluation in future iterations. For this report, no additional non-table stake inclusion criteria were applied across all vendors.

This is our first year evaluating the high-performance storage for AI workloads space in the context of our Key Criteria and Radar reports. This GigaOm Radar report examines 12 of the top storage solutions for AI workloads and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading storage solutions for AI workloads, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The GigaOm Key Criteria report provides a detailed decision framework for IT and executive leadership assessing enterprise technologies. Each report defines relevant functional and nonfunctional aspects of solutions in a sector. The Key Criteria report informs the GigaOm Radar report, which provides a forward-looking assessment of vendor solutions in the sector.

To help prospective customers find the best fit for their use case and business requirements, we assess how well high-performance storage solutions for AI workloads are designed to serve specific target markets and deployment models (Table 1).

For this report, we recognize the following market segments:

• Small-to-medium business (SMB): SMBs venturing into AI require storage solutions that are not only easy to deploy and manage but also capable of handling the performance demands of AI tasks like model training on smaller datasets, inferencing, and retrieval-augmented generation (RAG). They often seek cost-effective, preconfigured appliances or simplified cloud storage services with a clear path to scale as their AI initiatives grow, especially when IT resources are limited.

• Large enterprise: Large enterprises need high-performance storage solutions offering extreme throughput, low latency, and massive scalability to support demanding AI workloads, including training large language models (LLMs) and deep learning algorithms on petabyte-scale datasets. Robust security for sensitive AI data, flexible deployment options to integrate with complex MLOps pipelines and existing GPU-accelerated infrastructure, multiprotocol support (such as NFS, S3, and GPUDirect Storage), and advanced data management capabilities are critical.

• Cloud service provider (CSP): CSPs offering AI platforms and services require underlying storage infrastructure that delivers exceptional performance and scalability tiers to underpin diverse AI workloads for their customers, from data preparation to intensive model training and high-throughput inferencing. Key requirements include robust multitenancy, seamless integration with their proprietary and third-party AI/ML frameworks and compute instances (especially GPU and other accelerators), efficient automation, comprehensive monitoring, and sophisticated chargeback mechanisms.

• Managed service provider (MSP): MSPs delivering managed AI services or AI-ready infrastructure need versatile storage solutions that are performant, scalable, and easily manageable to cater to a diverse client base with varying AI workloads. They require flexible deployment and licensing models, strong integration capabilities with a wide array of AI tools and MLOps platforms, and the ability to provide predictable performance and data governance for their clients' AI initiatives.

In addition, we recognize the following deployment models:

• Physical appliance: Physical appliances for AI workloads offer a turnkey solution with pre-integrated and optimized hardware (often including high-speed NVMe drives and RDMA networking) and software stacks designed for specific AI performance characteristics. This approach simplifies deployment and provides predictable performance for organizations seeking on-premises AI infrastructure for tasks like model training or high-performance data analytics with minimal setup and tuning effort.

• Virtual appliance: Virtual appliances for AI storage provide deployment flexibility within existing virtualized environments, potentially serving development, testing, or less I/O-intensive AI workloads like model inferencing or data preprocessing. While offering easier integration with virtualized infrastructure, careful consideration of performance limitations for highly demanding AI training tasks is necessary for this deployment model compared to bare metal or dedicated physical appliance solutions.

• Public cloud image: Public cloud images offer a cloud-native, optimized deployment pathway for AI storage, available directly from cloud provider marketplaces and often tightly integrated with their AI/ML platforms and GPU/TPU compute instances. This model simplifies procurement and deployment, allows for consumption-based pricing, and enables rapid scaling to meet the fluctuating demands of AI projects.

• Software only: Software-only solutions grant the flexibility to deploy high-performance AI storage on the customer's choice of qualified commodity or specialized hardware and preferred operating system/hypervisor. This enables deep customization and optimization for specific AI workload needs and can be cost-effective for organizations with existing hardware infrastructure or specific architectural requirements for their AI data pipelines.

• SaaS (Storage-as-a-Service for AI): AI-focused SaaS storage solutions provide a fully managed service in which the provider handles all aspects of the storage infrastructure, including hardware, software, performance tuning for AI, security, and maintenance. This model allows data science and MLOps teams to focus on AI model development and deployment rather than infrastructure management, offering predictable performance SLAs and operational efficiency.

• Self-managed: Self-managed deployments provide organizations with complete control over their AI storage infrastructure, including hardware selection, software stack, network configuration, and data placement strategies. This model, deployable on-premises or in a private cloud, allows for maximum customization and optimization to meet specific AI workload requirements but demands significant in-house expertise in storage, networking, and AI infrastructure management.

Table 1. Vendor Positioning: Target Market and Deployment Model

Table 1 components are evaluated in a binary yes/no manner and do not factor into a vendor’s designation as a Leader, Challenger, or Entrant on the Radar chart (Figure 1). 

“Target market” reflects which use cases each solution is recommended for, not simply whether that group can use it. For example, if an SMB could use a solution but doing so would be cost-prohibitive, that solution would be rated “no” for SMBs.

All solutions included in this Radar report meet the following table stakes—capabilities widely adopted and well implemented in the sector:

• High throughput

• Low latency

• Support for AI/ML frameworks

• Data locality and tiering

• Flexible deployment options

• Concurrent access and shared file system support

• Data durability and availability

Tables 2, 3, and 4 summarize how each vendor in this research performs in the areas we consider differentiating and critical in this sector. The objective is to give the reader a snapshot of the technical capabilities of available solutions, define the perimeter of the relevant market space, and gauge the potential impact on the business.

• Key features differentiate solutions, highlighting the primary criteria to be considered when evaluating a storage solution for AI workloads.

• Emerging features show how well each vendor implements capabilities that are not yet mainstream but are expected to become more widespread and compelling within the next 12 to 18 months. 

• Business criteria provide insight into the nonfunctional requirements that factor into a purchase decision and determine a solution’s impact on an organization.

These decision criteria are summarized below. More detailed descriptions can be found in the corresponding report, “GigaOm Key Criteria for Evaluating Storage for AI Workloads Solutions.”

Key Features

• NVMe/NVMe-oF support: Support for NVMe (non-volatile memory express) and NVMe-oF (over fabric) protocols provides high-bandwidth, low-latency storage access by leveraging the NVMe protocol end-to-end from compute to storage media. This is critical for AI/ML workloads because it ensures rapid data delivery to processing units, significantly accelerating model training and the inference process.

• GPU-direct storage (GDS) integration: GDS enables a direct data path between storage and GPU memory, bypassing the CPU and reducing latency and overhead. This is crucial for accelerating GPU-intensive AI/ML workloads by streamlining data movement and maximizing GPU utilization.

• AI-optimized data layout and management: AI-optimized data layout and management leverages intelligent algorithms to automatically organize, tier, and manage data based on the specific needs of AI/ML workloads. This functionality improves performance, reduces manual effort, and optimizes resource utilization.

• Data reduction techniques optimized for AI/ML: Data reduction techniques such as compression and deduplication, optimized for AI/ML, minimize the storage footprint of massive datasets without impacting performance. This is essential for managing the cost and complexity of storing and processing the vast amounts of data used in AI/ML.

• Quality of service (QoS) and workload isolation: Quality of service (QoS) and workload isolation capabilities ensure that multiple AI/ML workloads can share the same storage infrastructure without interference, guaranteeing predictable performance for critical applications. This capability is vital for organizations running diverse AI/ML workloads with varying performance requirements.

• Metadata management and acceleration: Efficient metadata management and acceleration are essential for AI/ML workloads, enabling rapid data discovery, access, and processing. Solutions that excel in this area significantly improve the productivity of data scientists and the efficiency of AI/ML pipelines.

• Integrated data pipeline support: Integrated data pipeline support streamlines the movement and processing of data across the various stages of the AI/ML lifecycle, from ingestion and preparation to training and deployment. This accelerates the development and deployment of AI/ML applications.

• Security and data integrity for AI/ML: Robust security and data integrity features are crucial for protecting sensitive AI/ML data and ensuring the trustworthiness of models. This includes protecting data at rest, in transit, and during processing. Solutions that offer comprehensive security controls and data validation mechanisms are essential for maintaining data privacy, regulatory compliance, and model reliability.

Table 2. Key Features Comparison 

Emerging Features

• Industry certifications and validation: Industry certifications, like NVIDIA-Certified Storage, validate that storage solutions meet specific performance, reliability, and scalability standards for demanding AI workloads. This feature ensures enterprises can confidently deploy optimized infrastructure for AI factories, reducing integration risks and accelerating deployment.

• AI-driven autonomous storage management: AI-driven autonomous storage management leverages machine learning to automate and optimize storage operations, such as data placement, tiering, performance tuning, and anomaly detection. This automated optimization reduces administrative overhead, improves efficiency, and ensures optimal performance for AI/ML workloads.

• Computational storage integration: Computational storage integration brings compute capacity closer to the data by offloading processing tasks from the host CPU to storage devices or controllers. For AI/ML, this can significantly accelerate data preprocessing, feature extraction, and model inferencing, improving efficiency and reducing latency.

• Specialized hardware acceleration for AI/ML: Specialized hardware acceleration for AI/ML involves using purpose-built hardware like FPGAs, ASICs, or specialized processing units to accelerate storage-related operations specific to AI/ML workloads. This accelerated processing can dramatically improve performance and efficiency for tasks like data compression, encryption, and transformation.

Table 3. Emerging Features Comparison 

Business Criteria

• Scalability: Scalability in the context of AI storage refers to the system's ability to seamlessly expand both capacity and performance in response to the growing demands of AI/ML workloads. This is crucial for organizations that aim to avoid storage bottlenecks as their AI/ML initiatives mature and data volumes increase.

• Flexibility: Flexibility refers to the storage solution's ability to support diverse deployment models, integrate with various AI/ML frameworks and tools, and adapt to evolving workload requirements. This is important because it allows organizations to tailor their storage infrastructure to their specific needs and avoid vendor lock-in.

• Performance: In the context of AI storage, performance encompasses consistent low latency, high throughput, and high concurrency to accelerate data-intensive AI/ML workloads. It's a critical factor that directly impacts model training times, inference speeds, and overall AI/ML productivity.

• Manageability: Manageability refers to the ease with which a storage solution can be deployed, configured, monitored, and maintained, minimizing administrative overhead and operational complexity. This is important for reducing the burden on IT staff and enabling organizations to focus on their AI/ML objectives.

• Ecosystem: Ecosystem refers to the breadth and depth of a vendor's partnerships, integrations, community support, and complementary solutions. A strong ecosystem enhances the value of the core storage solution by ensuring interoperability, simplifying deployment, and providing access to a wider range of tools and expertise.

• Cost transparency: Cost considerations for AI storage solutions encompass not only upfront acquisition costs but also ongoing operational expenses, scalability costs, and the impact on overall infrastructure efficiency. Organizations must consider the total cost of ownership (TCO) when evaluating solutions.

Table 4. Business Criteria Comparison 

The GigaOm Radar plots vendor solutions across a series of concentric rings with those set closer to the center judged to be of higher overall value. The chart characterizes each vendor on two axes—balancing Maturity versus Innovation and Feature Play versus Platform Play—while providing an arrowhead that projects each solution’s evolution over the coming 12 to 18 months.

Figure 1. GigaOm Radar for Storage for AI Workloads

The GigaOm Radar for storage for AI workloads reveals a dynamic and rapidly evolving market landscape. This is the first iteration of this specific Radar report, so we cannot yet analyze year-over-year vendor movement, but the current positioning offers valuable insights into the state of the market.

As you can see in Figure 1, there is a strong trend toward Platform Plays, with nearly all vendors situated on the right side of the Radar. For many established vendors, this positioning reflects a strategy of enhancing and adapting their existing, broad enterprise storage platforms, integrating new functionalities specifically to meet the demanding requirements of AI workloads. This approach allows organizations to address AI storage needs within potentially familiar ecosystems. In contrast is the Feature Play approach, represented by a single vendor on the left side of the chart. While it offers a capable platform, it currently focuses its go-to-market strategy on specific niche industries, leading to our classification of it as a Feature Play.  

The distribution across the Maturity and Innovation halves is more balanced, though with a notable concentration in the Maturity hemisphere. This reflects the significant disruption AI is causing in the storage sector while traditional storage vendors, with more mature business models, have answered the call. Vendors in the Innovation half are characterized by flexibility, responsiveness to market shifts, and often aggressive roadmaps, which may involve changes to the product or interface during the contract lifecycle. Conversely, vendors positioned in Maturity emphasize stability, continuity, and incremental improvements on proven architectures, offering a consistent user experience even as they adapt to dynamic AI demands.

Several patterns emerge from the spatial distribution of vendors:

• There is a significant cluster of vendors designated as Leaders, primarily residing in the Platform Play half of the chart. This density points to a competitive top tier where multiple solutions meet a high standard across key features and business criteria for AI workloads.

• The Innovation/Platform Play quadrant is particularly active, housing numerous Leaders and Challengers marked as Fast Movers with a few Outperformers. This highlights the intense development focus on creating comprehensive yet cutting-edge platforms specifically for AI.

• Several Challengers, particularly those demonstrating rapid innovation (Fast Movers/Outperformers), are positioned close to the Leaders circle. This suggests the potential for shifts in leadership positions in the coming 12 to 18 months as these solutions gain broader adoption and feature parity.

• Outperformers, displaying the fastest projected forward movement, are predominantly found in the Innovation/Platform Play quadrant, reinforcing the idea that rapid development is a key characteristic of vendors challenging the status quo in the AI storage space.

In reviewing solutions, it’s important to keep in mind that there are no universal “best” or “worst” offerings; every solution has aspects that might make it a better or worse fit for specific customer requirements. Prospective customers should consider their current and future needs when comparing solutions and vendor roadmaps.

INSIDE THE GIGAOM RADAR

To create the GigaOm Radar graphic, key features, emerging features, and business criteria are scored and weighted. Key features and business criteria receive the highest weighting and have the most impact on vendor positioning on the Radar graphic. Emerging features receive a lower weighting and have a lower impact on vendor positioning on the Radar graphic. The resulting chart is a forward-looking perspective on all the vendors in this report, based on their products’ technical capabilities and roadmaps.

Note that the Radar is technology-focused, and business considerations such as vendor market share, customer share, spend, recency or longevity in the market, and so on are not considered in our evaluations. As such, these factors do not impact scoring and positioning on the Radar graphic.

For more information, please visit our Methodology.

Dell Technologies: PowerScale

Solution Overview
Dell Technologies is a global provider of enterprise technology solutions. Relevant to this report is its PowerScale portfolio, focused on scale-out NAS solutions for modern unstructured data workloads, including AI. PowerScale uses the OneFS operating system across various hardware node types (all-flash, hybrid, archive) that can be integrated within a single cluster, presenting a unified namespace accessible via multiple protocols, including NFS, SMB, HDFS, and S3. Dell also offers PowerScale capabilities via software-defined public cloud deployments (Dell PowerScale for AWS/Azure) and as-a-service models. 

Dell's strategy centers on providing a flexible, scalable, and high-performance platform capable of handling diverse workloads. The solution prioritizes stability and incremental improvement, leveraging its established OneFS foundation while incorporating enhancements in performance, networking (like 200 GbE, RDMA), security, and hardware density, aligning it with the Maturity classification. Dell continues to evolve the platform with roadmap items like Project Lightning for parallel file system capabilities and denser hardware options.  

Dell Technologies is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Dell Technologies scored well on a number of decision criteria, including:

• GPU-direct storage (GDS) integration: PowerScale provides strong GDS integration by leveraging NFS over RDMA combined with NVIDIA GPUDirect technologies, enabling a direct data path between storage and GPU memory that bypasses the CPU to reduce latency and accelerate training.

• Metadata management and acceleration: PowerScale shows strength due to its distributed metadata architecture, use of B-trees for efficient lookups, global namespace acceleration (GNA) for hybrid nodes, and the separate MetadataIQ solution for indexing and search across potentially multiple clusters. 

• QoS and workload isolation: PowerScale performs well by implementing Class of Service (CoS) and Quality of Service (QoS) tagging via DSCP support, along with SmartQoS features allowing fine-grained limits based on various parameters like directories, users, and access zones to manage shared AI/ML environments effectively.

Opportunities
Dell Technologies has room for improvement in a few decision criteria, including:

• Specialized hardware acceleration for AI/ML: Dell PowerScale enhances performance through features like NFS over RDMA, which offloads CPU processing to benefit GPU-based servers, and offers the PA110 Performance Accelerator for certain workloads. While these provide valuable hardware-assisted acceleration, there's an opportunity to further advance by incorporating more specialized hardware, such as FPGAs or ASICs, explicitly dedicated to accelerating AI/ML-specific storage operations like advanced data compression, encryption, or complex data transformations.

• Data reduction techniques optimized for AI/ML: While PowerScale includes standard data reduction features like inline compression and post-process deduplication, further optimization specifically tuned for the unique characteristics and access patterns common in AI/ML datasets could enhance efficiency.

• Integrated data pipeline support: Dell PowerScale, when part of the Dell Data Lakehouse architecture, delivers robust data pipeline functionality, featuring tight integration with Apache Spark for large-scale ETL and AI workloads within Kubernetes-managed environments. This existing architecture effectively supports parallel data access and processing directly on PowerScale. To further enhance its appeal and streamline an even wider array of MLOps practices, this vendor could expand its portfolio of specific, prebuilt integrations or deeper native connections for other widely adopted workflow orchestration tools, such as Apache Airflow or more specialized Kubeflow Pipeline components, that go beyond the current robust Spark and Kubernetes foundations. 

Purchase Considerations
Dell Technologies PowerScale targets a wide range of customers, including large enterprises and service providers, with workloads spanning from general file sharing to demanding AI/ML and high performance computing (HPC). Licensing is capacity-based per node type, with optional licenses for advanced software features like SmartPools and CloudPools. The ability to mix node types (all-flash, hybrid, archive) provides flexibility for cost and performance optimization but may add complexity to configuration. Dell offers the solution as physical appliances and through various APEX consumption models, catering to different financial and operational preferences. Given its broad feature set supporting multiple protocols and data management capabilities within a single system, PowerScale operates as a Platform Play, suitable for organizations looking to consolidate unstructured data storage. Deployment complexity varies, with appliances likely being simpler than software-defined cloud instances.

Use Cases
As a Platform Play solution, Dell Technologies PowerScale supports most industry verticals and a wide array of use cases for unstructured data. It is particularly well suited for large-scale AI model training and inferencing, data lakes supporting analytics and AI pipelines, high-performance computing (HPC) workloads (implied by performance features), media and entertainment workflows, and life sciences research, due to its scalability, performance characteristics (including GDS support), and multiprotocol access (NFS, SMB, S3, HDFS) allowing diverse tools to access a common data repository. Its tiering capabilities also make it suitable for large archives.

Hammerspace: Hammerspace Global Data Platform 

Solution Overview
Hammerspace provides a software-defined Global Data Platform, centered around a high-performance Parallel Global File System designed to unify unstructured data access across disparate storage types, vendors, locations, and clouds. Its core function is presenting a single, global namespace accessible via standard NFS, SMB, and S3 protocols, eliminating data silos and enabling automated data orchestration without user disruption. Key components include Anvil metadata nodes and DSX data services nodes, deployable on commodity hardware, VMs, or cloud instances. Hammerspace focuses on solving distributed data challenges, particularly for high-performance workloads like AI, leveraging standards-based pNFS v4.2 for parallel access and unique features like Tier 0, which activates GPU server-local NVMe as shared, ultra-fast storage. With significant recent feature introductions and a focus on performance and flexibility through software innovation, Hammerspace aligns strongly with the Innovation hemisphere.

Hammerspace is positioned as a Challenger and Outperformer in the Innovation/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Hammerspace scored well on a number of decision criteria, including:

• Metadata management and acceleration: Hammerspace excels with its architecture that separates metadata and data paths using pNFS, storing metadata (including user-defined custom tags with inheritance) in a replicated database for rapid, global access across all storage and locations, which is crucial for AI data discovery and governance.

• NVMe/NVMe-oF support: Hammerspace provides effective support by enabling direct connectivity via NFS-RDMA over RoCE and InfiniBand, delivering high performance that rivals NVMe-oF in many AI/ML use cases. Its innovative Tier 0 capability also leverages local NVMe storage within GPU servers for ultrafast access.

• GPU-direct storage (GDS) integration: The platform integrates well with GDS, shipping with necessary RDMA drivers and automatically using RDMA paths when available. Notably, its Tier 0 capability extends GDS access to local NVMe storage within GPU servers.

Hammerspace is classified as an Outperformer because of its rapid pace of innovation over the last 12 to 18 months, including the introduction of its Hyperscale NAS architecture, Tier 0 capabilities, EC-Groups, and S3 interface, addressing key challenges in high-performance and distributed AI workloads.

Opportunities
Hammerspace has room for improvement in a few decision criteria, including:

• Data reduction techniques optimized for AI/ML: Hammerspace offers native data reduction capabilities, including compression and deduplication, which can be applied when data is moved or tiered, such as to object storage. While these features contribute to overall storage efficiency, there is an opportunity to further emphasize or develop specific optimizations of these techniques tailored to the diverse and unique data patterns commonly found in AI/ML workloads, beyond general-purpose effectiveness.

• AI-optimized data layout and management: While Hammerspace employs service-level objectives (SLOs) for automated data placement based on policies and includes a cost-based arbitrage algorithm representing intelligent automation, the platform currently does not leverage AI/ML technology directly for optimizing data layout or for predictive analytics, representing an area for potential enhancement.

• Security and data integrity for AI/ML: Although Hammerspace offers a robust set of general security features, including encryption, RBAC, and audit logging, there's an opportunity to enhance the platform by adding security capabilities specifically designed for AI/ML models, such as model versioning, lineage tracking, or built-in mechanisms for detecting adversarial attacks or ensuring model integrity.

Purchase Considerations
Hammerspace offers its Global Data Platform via a software subscription licensed by the volume of data under management, which includes all features and support, providing predictable cost scaling. It targets organizations of all sizes, particularly large enterprises and research institutions with demanding AI/ML or HPC workloads. As a software-defined solution, it runs on commodity hardware (servers, storage from any vendor), VMs, or public cloud instances (AWS, Azure, GCP), offering deployment flexibility but requiring customers to provide or procure the underlying infrastructure unless they opt for integrated appliances offered through multiple industry leading partners. Its ability to assimilate existing storage nondisruptively and rapidly can lower initial costs and migration complexity. Given its comprehensive feature set covering data access, orchestration, and protection across diverse environments, it functions as a Platform Play.

Use Cases
As a Platform Play, Hammerspace supports most industry verticals and is particularly adept at use cases involving distributed data environments and high-performance requirements. Key use cases include large-scale AI model training and inferencing (leveraging Hyperscale NAS and Tier 0), burst-to-cloud compute for GPUs, bridging multivendor storage silos within a data center, enabling multisite collaboration with global file access, and facilitating hybrid and multicloud data strategies. Its standards-based approach (NFS, SMB, S3) ensures broad compatibility with AI frameworks, HPC applications, and enterprise tools without proprietary clients.

Hitachi Vantara: Hitachi iQ

Solution Overview
Hitachi Vantara offers Hitachi iQ, a comprehensive, turnkey platform introduced in 2024 specifically for demanding AI workloads. It integrates accelerated compute (via NVIDIA DGX BasePOD certification and Hitachi's own HGX offerings), high-bandwidth networking (Infiniband, Ethernet), and NVIDIA AI Enterprise software. The core storage component is Hitachi Content Software for File (HCSF), a high-performance, software-defined parallel filesystem designed to leverage NVMe Flash and provide integrated tiering to object storage (like Hitachi VSP One Object). Hitachi iQ supports on-premises and hybrid deployments, offering components individually or as a full stack. With its recent entry focused specifically on AI, partnerships with NVIDIA and Hammerspace, and a roadmap including new hardware and AI solutions, Hitachi iQ demonstrates significant focus on rapid advancement, aligning it with the Innovation hemisphere.

Hitachi Vantara is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Hitachi Vantara scored well on a number of decision criteria, including:

• Quality of service (QoS) and workload isolation: Hitachi iQ offers world-class QoS and workload isolation capabilities. The combination of filesystem-level policies and the highly granular composable cluster option with dynamic adjustability provides exceptional control over resource allocation and ensures predictable performance for critical AI/ML applications in shared environments.

• GPU-direct storage (GDS) integration: The platform provides advanced GDS integration. Support for GDS with optimized drivers provides significant performance benefits for AI/ML workloads by enabling direct data transfer between storage and GPU memory, with claimed substantial improvements in throughput and IOPS underscoring the effectiveness.

• AI-optimized data layout and management: Hitachi iQ demonstrates advanced capabilities in this area. The intelligent and automated data placement across different storage tiers (TLC/QLC NVMe and object storage) based on real-time monitoring and user-defined policies ensures optimal performance and cost efficiency for AI/ML workloads with varying data access patterns.

Opportunities
Hitachi Vantara has room for improvement in a few decision criteria, including:

• NVMe/NVMe-oF support: Hitachi iQ supports standard NVMe-oF protocols (RoCEv2, iWARP, TCP) for broad workload compatibility. Additionally, it features a POSIX-based user-space driver designed to optimize performance for parallel GPU workloads, an approach that aligns with certified AI architectures like NVIDIA DGX BasePOD/SuperPOD that emphasize filesystem protocols. An opportunity exists for this vendor to clearly articulate guidance on when customers should leverage the standard NVMe-oF path versus the optimized POSIX driver, ensuring this dual-capability is recognized as a flexible strength tailored to diverse AI operational needs rather than a simple divergence from other NVMe-oF-centric approaches.

• Data reduction techniques optimized for AI/ML: Hitachi iQ offers deduplication on both its flash and object storage tiers, and provides FPGA-accelerated compression on VSP One Block, which is primarily targeted at colder data by design to minimize performance impact on latency-sensitive AI workloads. This intentional approach balances performance with storage efficiency. While this strategy is sound, further differentiation could be achieved by exploring or more visibly promoting advanced AI/ML-specific data reduction algorithms that could be applied effectively across a wider range of data temperatures, including warmer tiers where feasible, without compromising the performance demanded by AI applications. This would complement the existing 4:1 compression guarantee on VSP One Block.

• AI-driven autonomous storage management: Hitachi iQ leverages AI/ML automation via the integrated Hitachi Ops Center for generally available capabilities such as resource scaling, aspects of performance tuning, and automated data placement (like policy-driven tiering), which reduce manual intervention and adapt to workload shifts. Further advancement in this evolving area could focus on developing or more explicitly promoting deeper AI-driven predictive analytics for AI-specific workload optimization—like forecasting resource needs or enabling more granular self-adjusting performance parameters—ideally surfaced directly within the main iQ platform interface.

Purchase Considerations
Hitachi iQ is positioned as a turnkey, high-performance solution primarily targeting large enterprises undertaking significant AI initiatives. Licensing is flexible, with perpetual, subscription, and consumption models available. While offered as an integrated stack, components like HCSF can be purchased separately. The platform supports on-premises, cloud (via HCSF software), and hybrid deployments. Given its comprehensive nature, integrating compute, storage, networking, and AI software, Hitachi iQ is a Platform Play. The turnkey approach aims to simplify deployment, but the inherent complexity of integrating a full AI stack likely necessitates professional services or significant in-house expertise. The ability to tier to VSP One Object and potential compression on block storage offer avenues for cost optimization.

Use Cases
As a Platform Play solution, Hitachi iQ is engineered for demanding, large-scale AI and GenAI workloads across the entire AI pipeline—from foundational model training and fine-tuning to extensive inferencing, retrieval-augmented generation (RAG), and agentic AI solutions. Its high-performance architecture, enabling linear scaling and workload orchestration, also effectively supports traditional HPC and advanced analytics requiring rapid data access. These capabilities are generally available, with Hitachi Vantara additionally providing vertical-specific AI solutions for sectors including manufacturing, energy, transportation, and BFSI (banking, financial services, and insurance).

HPE: HPE GreenLake for File Storage

Solution Overview
Hewlett Packard Enterprise (HPE) delivers HPE GreenLake for File Storage, a high-performance file storage solution built on HPE Alletra Storage MP hardware and leveraging VAST Data's Disaggregated Shared-Everything (DASE) software architecture. Managed via the HPE GreenLake cloud platform, it provides an on-premises solution with a cloud operational experience. The DASE architecture separates compute and storage, interconnected by an NVMe fabric, enabling independent scaling. The platform uses all-NVMe storage, including storage class memory (SCM) for metadata and writes, aiming for high throughput and low latency. Key integrations include a robust NVIDIA partnership, evidenced by DGX BasePOD and OVX certifications. Recent updates focused on write performance and density. HPE positions the solution for accelerating AI and data-intensive applications, emphasizing efficiency, simplified management via GreenLake, and scalability. Its modern architecture and focus on AI integrations place it in theMaturity hemisphere but show that they are innovating in this space .

HPE is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
HPE scored well on a number of decision criteria, including:

• Data reduction techniques optimized for AI/ML: HPE offers a comprehensive suite, including adaptive chunking, global deduplication, Zstandard compression, and, notably, similarity reduction, which is highly effective for reducing redundancy in common AI/HPC datasets, providing significant storage efficiency. 

• Industry certifications and validation: HPE possesses a critical NVIDIA DGX BasePOD certification and NVIDIA OVX storage validation. These demonstrate proven performance and interoperability within demanding NVIDIA AI ecosystems, providing significant customer confidence.

• Quality of service (QoS) and workload isolation: HPE provides granular QoS controls applicable per view or tenant, alongside server pool capabilities and secure multitenancy features, enabling predictable performance management in shared AI environments.

Opportunities
HPE has room for improvement in a few decision criteria, including:

• Metadata management and acceleration: While HPE uses a distributed metadata system with hardware acceleration and emerging AI-driven tagging capabilities, there is an opportunity to further mature and broaden the application of AI-driven techniques for more comprehensive metadata enrichment and faster data discovery across diverse AI workloads.

• Integrated data pipeline support: The platform integrates with various pipeline tools and frameworks. However, enhancing support with deeper native connections or prebuilt integrations for a wider range of popular orchestration tools like Apache Airflow or Kubeflow could further streamline complex AI workflow deployment compared to relying primarily on protocol-level access.

• AI-optimized data layout and management: This vendor uses a high-performance single-tier architecture designed to eliminate data movement complexities but lacks specific details on advanced AI/ML-driven techniques for dynamic data layout optimization beyond caching. 

Purchase Considerations
HPE GreenLake for File Storage primarily targets large enterprises needing high-performance file storage for demanding AI workloads, especially those aligned with the NVIDIA ecosystem. It's deployed on-premises using HPE Alletra MP hardware but managed via the HPE GreenLake cloud platform, offering cloud-like operations. Licensing typically involves hardware purchase plus a per-terabyte software subscription, though GreenLake also offers consumption models. The platform's reliance on VAST Data software for its core file system is a key consideration regarding the long-term roadmap. Its DASE architecture allows independent scaling, offering flexibility, but the single, all-flash tier, while performant and simple, may present TCO challenges for datasets with large amounts of cold data compared to tiered systems; cost-effectiveness hinges significantly on data reduction effectiveness. Given its comprehensive nature and GreenLake integration, it's a Platform Play.

Use Cases
As a Platform Play solution, HPE GreenLake for File Storage is well suited for large-scale AI model training and inferencing, particularly within NVIDIA environments validated by DGX BasePOD and OVX certifications. Its performance and scalability also lend themselves to demanding HPC simulations and data analytics requiring rapid access to large file-based datasets. The GreenLake management model facilitates deployment within hybrid cloud strategies, supporting use cases for which consistent management across on-premises high-performance storage and cloud resources is desired.

IBM: IBM Storage Scale/IBM Cloud Object Storage*

Solution Overview
IBM offers a comprehensive storage portfolio for AI workloads, primarily featuring IBM Storage Scale (software and the integrated Storage Scale System appliance) for high-performance file and object access, and IBM Cloud Object Storage for scalable AI data lakes. Storage Scale is a software-defined global data platform supporting diverse hardware and deployment models (on-premises, cloud, hybrid), designed for AI/ML and HPC with features like content-aware data processing using natural language processing (NLP). The Storage Scale System leverages NVMe and NVIDIA GPUDirect Storage (GDS) for optimized performance. Cloud Object Storage integrates tightly with IBM's watsonx.ai platform and offers flexible, cost-effective tiers. IBM's strategy focuses on providing a flexible, scalable, high-performance foundation across the AI data pipeline, from data lakes to high-speed training. While built on mature core technologies like Storage Scale (GPFS), IBM demonstrates significant ongoing innovation by integrating AI-specific features, supporting modern deployment patterns, and fostering key partnerships (NVIDIA, Red Hat).

IBM is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
IBM scored well on a number of decision criteria, including:

• NVMe/NVMe-oF support: The IBM Storage Scale System incorporates NVMe flash technology and supports NVMe-oF, providing the essential low latency and high throughput needed for feeding demanding AI compute cycles.

• GPU-direct storage (GDS) integration: IBM supports NVIDIA GDS within the Storage Scale System. This enables a crucial direct data path between storage and GPU memory, enhancing efficiency and accelerating AI training processes.

• Integrated data pipeline support: IBM Storage Scale uniquely offers capabilities to embed compute and data pipeline functionalities within the storage layer, complemented by Cloud Object Storage's optimized connectivity to Apache Spark, thereby streamlining AI workflows.

Opportunities
IBM has room for improvement in a few decision criteria, including:

• Data reduction techniques optimized for AI/ML: While policy-driven compression is available in IBM Storage Scale, it lacks algorithms or techniques specifically optimized for diverse AI/ML data types beyond general compression.

• Security and data integrity for AI/ML: IBM provides strong foundational security features, but the research did not reveal advanced capabilities explicitly designed for AI-specific threats like model vulnerability scanning or adversarial attack defenses.

• AI-driven autonomous storage management: Although IBM is incorporating AI for enhanced monitoring and support (anomaly detection, proactive ticketing) and uses AI in content-aware features, the research suggests the platform is still developing toward fully autonomous storage management for AI/ML workloads.

Purchase Considerations
IBM targets large enterprises and research institutions requiring high performance and scalability for AI, HPC, and data lake workloads. Storage Scale offers deployment flexibility (software on various hardware, integrated appliance via Scale System), while Cloud Object Storage is a cloud service with tiered pricing. Licensing likely involves capacity-based subscriptions or traditional enterprise agreements; Cloud Object Storage offers usage-based tiers. The portfolio breadth requires careful selection between the high-performance Scale System for active workloads and the cost-effective Cloud Object Storage for large data repositories. Given the comprehensive nature of covering file, object, cloud, and integrated AI features, IBM operates as a Platform Play. Deployment complexity varies, with Scale System appliances likely simpler than large software-defined Scale deployments.

Use Cases
As a Platform Play, IBM's storage portfolio supports a wide range of AI/ML and HPC use cases. IBM Storage Scale System excels at performance-intensive tasks like large-scale model training and complex simulations requiring high throughput and low latency via file access. IBM Cloud Object Storage is ideal for building massive, cost-effective AI data lakes used for data preparation, analytics (via Spark integration), and feeding models, especially within the IBM Cloud and watsonx.ai ecosystem. The content-aware features in Storage Scale enable advanced use cases involving semantic understanding and data organization based on content.

NetApp: AFF A-Series

Solution Overview
NetApp targets the AI storage market by extending its established enterprise storage platform, ONTAP, running on AFF A-Series all-flash hardware. This approach appeals to enterprise IT organizations seeking to manage AI workloads using familiar tools and protocols. The solution provides unified access (NFS/pNFS, SMB, iSCSI, FC, NVMe/TCP, NVMe/FC, and S3) and leverages the BlueXP control plane for hybrid multicloud management, integrating with native cloud services. NetApp emphasizes its NVIDIA SuperPOD "Enhanced" certification, achieved using standard pNFS over Ethernet, as validation of its high-performance capabilities that don’t require specialized clients. This strategy, focusing on the stability, proven features, and incremental enhancements (security, protocols, management) of ONTAP, places NetApp firmly in the Maturity half. It operates as a Platform Play, offering broad functionality suitable for consolidating diverse workloads. 

NetApp is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
NetApp scored well on a number of decision criteria, including:

• NVMe/NVMe-oF support: NetApp has early adoption and mature end-to-end support for both NVMe/FC and NVMe/TCP alongside legacy protocols on the same hardware, facilitating enterprise transition to lower-latency fabrics.  

• Quality of service (QoS) and workload isolation: NetApp’s robust, granular QoS (including adaptive policies) and secure multitenancy via storage virtual machines (SVMs), role-based access control (RBAC), and attribute-based access control (ABAC) are crucial for predictable performance in shared enterprise or CSP environments running mixed workloads.  

• Security and data integrity for AI/ML: NetApp integrates strong security measures like AI-driven autonomous ransomware protection (ARP/AI), tamperproof snapshots, comprehensive encryption, and ABAC support relevant for RAG, extending enterprise-grade security to AI pipelines.

Opportunities
NetApp has room for improvement in a few decision criteria, including:

• Metadata management and acceleration: NetApp has functional capabilities via pNFS separation but lacks a currently shipping onboard metadata index optimized for AI-specific discovery, although this is planned for the future AI data platform.  

• Integrated data pipeline support: NetApp's ONTAP platform simplifies data access in AI pipelines by enabling multiprotocol support (NFS, SMB, S3) to the same dataset without needing data copies, streamlining the flow between stages like object-based data ingest and file-based model training. While this unified access provides flexibility, the platform does not currently offer specific prebuilt software integrations or dedicated connectors for some popular data pipeline orchestration tools such as Apache Spark or Kubeflow. Consequently, users will typically leverage NetApp's robust protocol-level access and APIs for these integrations, which may require more direct configuration effort for those specific tools compared to solutions with out-of-the-box connectors.

• Specialized hardware acceleration for AI/ML: NetApp incorporates Intel QAT for encryption/compression offload, which benefits overall performance, but it doesn't use more AI-specific accelerators like FPGAs/ASICs for storage operations, as do some emerging approaches.

Purchase Considerations
NetApp offers flexible licensing, including the comprehensive ONTAP One license for appliances, per-GB licensing for ONTAP Select (virtual), and subscription-based Keystone STaaS. Cloud offerings like CVO have capacity-based PAYGO options. Pricing is generally transparent, though upfront costs for high-performance AFF A-series might be higher than some alternatives; however, features like BlueXP Tiering/FabricPool optimize TCO by automating data movement to lower cost tiers. The solution is effectively productized within the ONTAP ecosystem. NetApp AFF A-Series is suitable for large enterprises and service providers running demanding AI workloads, though different ONTAP deployment models cater to various organization sizes. It functions as a Platform Play, offering a wide range of integrated data management, security, and multiprotocol features within the ONTAP OS, suitable for displacing or consolidating various storage solutions rather than just providing niche features. Professional services are available, but ONTAP and BlueXP focus on manageability for IT generalists. Deployment complexity is reduced through wizards and automation, and upgrades are typically nondisruptive.

Use Cases
The NetApp AFF A-Series platform is most strongly validated for large-scale model training within enterprise settings, backed by its standards-based NVIDIA SuperPOD certification. Its robust multitenancy (SVMs) and QoS features make it suitable for GPU-as-a-service deployments. For RAG inferences, its support for granular security (ABAC) is a relevant capability. General data preparation and data lake use cases leverage its core scale-out NAS and object storage functionality. Its ability to consolidate traditional enterprise workloads alongside AI is a key aspect of its platform value proposition.

Pure Storage: FlashBlade/FlashArray*

Solution Overview
Pure Storage is a prominent all-flash data storage vendor offering solutions highly relevant to AI/ML workloads, primarily through its FlashBlade (unified fast file and object) and FlashArray (block and file) platforms. Both run the Purity operating environment and are managed via the Pure1 cloud-based AIOps platform and Pure Fusion automation layer. Pure emphasizes delivering high performance with simplicity, leveraging technologies like DirectFlash Modules, end-to-end NVMe/NVMe-oF, and integration with key ecosystem partners, particularly NVIDIA (evidenced by AIRI solutions and multiple certifications). Its Evergreen subscription model provides flexible consumption and nondisruptive upgrades. It has a strong focus on cutting-edge flash technology (like FlashBlade//EXA), AI-driven management (Pure1 AIOps, AI copilot), and continuous performance enhancements.

Pure Storage is positioned as a Leader and Outperformer in the Maturity/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Pure Storage scored well on a number of decision criteria, including:

• Metadata management and acceleration: Pure Storage FlashBlade architecture is purpose-built to handle massive metadata volumes and high concurrency inherent in AI workloads, featuring independent metadata scaling (FlashBlade//EXA) and tools like RapidFile Toolkit to accelerate file operations.

• Integrated data pipeline support: Pure Storage offers robust support through its unified Purity platform, a pre-validated full-stack AIRI solution (with NVIDIA), strong Portworx integration for Kubernetes pipelines, and validated designs addressing the end-to-end AI workflow.

• Industry certifications and validation: Pure Storage possesses a comprehensive portfolio of highly relevant NVIDIA certifications (DGX SuperPOD, DGX BasePOD, NVIDIA-Certified Storage Partner, HPS for NCP, OVX-ready) and security validations (FIPS 140-2), confirming performance and interoperability for demanding AI environments.

Pure Storage is classified as an Outperformer due to its rapid innovation cadence driven by the Evergreen subscription model, a strong focus on cutting-edge flash technology like FlashBlade//EXA, continuous performance enhancements, advanced AI-driven management via Pure1 AIOps and its AI copilot, and a deep partnership with NVIDIA evidenced by numerous certifications.

Opportunities
Pure Storage has room for improvement in a few decision criteria, including:

• Data reduction techniques optimized for AI/ML: While Pure Storage offers a robust set of data reduction techniques enhanced by machine learning, there is an opportunity for further optimization specific to the unique characteristics and diverse data types commonly found in AI/ML workloads.

• Security and data integrity for AI/ML: Pure Storage provides a strong security foundation, including encryption and access controls. It could be enhanced by incorporating features explicitly designed to address advanced AI-specific security threats, such as model vulnerability scanning or defenses against adversarial attacks.

• Quality of service (QoS) and workload isolation: Pure Storage offers comprehensive QoS and workload isolation capabilities essential for managing shared environments. Opportunity exists to further enhance these features with potentially even more granular controls or proactive adaptations to meet the increasing complexity and dynamism of future multitenant AI workload demands.

Purchase Considerations
Pure Storage targets enterprises requiring high-performance storage for demanding workloads like AI/ML, analytics, and databases. Its platforms (FlashBlade for file/object, FlashArray for block/file) are known for ease of management and deployment simplicity, often requiring less administrative overhead than competitors. The Evergreen subscription model is central to its strategy, offering predictable costs, nondisruptive upgrades, and flexible consumption options (Evergreen//One for AI providing SLA-backed service). This focus on TCO and operational simplicity, combined with advanced data reduction, makes its all-flash solutions cost-competitive. Given the unified platform approach and broad feature set, these solutions function as a Platform Play. Validated designs like AIRI and FlashStack further simplify deployment for AI use cases.

Use Cases
As a Platform Play, Pure Storage supports a wide spectrum of high-performance AI/ML use cases, including large-scale model training (validated via DGX certifications), real-time inferencing, data preparation, analytics, and GenAI RAG pipelines. Its high throughput and low latency solutions excel in verticals like financial services (trading), life sciences (genomics, medical imaging), and media and entertainment. The integration with Portworx makes it highly suitable for containerized AI/ML applications orchestrated via Kubernetes. The unified platform approach simplifies data management across the entire AI project lifecycle.

Quantum: Myriad

Solution Overview
Quantum is a technology provider with a focus on data management and storage solutions. Myriad is Quantum's high-performance, all-flash, software-defined storage solution designed for modern unstructured data and AI/ML workloads. It features a cloud-native architecture orchestrated by Kubernetes, utilizing NVMe storage, dynamic erasure coding, and intelligent networking. Key components include storage server nodes, load balancer nodes, a deployment node, a transactional key-value store, and the Myriad file system. Quantum's strategy with Myriad is to offer a simplified, scalable, and high-performance storage environment, initially focusing on the Life Sciences AI/ML and Animation/VFX markets, with plans to expand to other segments. Given its recent introduction and ongoing development of key features like S3 support and direct GPU integration, Myriad aligns with the Innovation hemisphere, emphasizing rapid advancement and new feature development.

Quantum is positioned as a Challenger and Fast Mover alone in the Innovation/Feature Play quadrant of the storage for AI workloads Radar chart.

Strengths
Quantum scored well on a number of decision criteria, including:

• NVMe/NVMe-oF support: The solution uses NVMe flash drives internally and leverages RDMA over Converged Ethernet (RoCE) for efficient node-to-node communication, providing a foundation for high performance, although it does not currently offer standard end-to-end NVMe-oF for client access, relying instead on standard file protocols over its high-performance back end.

• AI-optimized data layout and management: Myriad incorporates features beneficial for AI/ML through its all-NVMe design, a large global read cache, and a distributed key-value store. While lacking specific AI-driven predictive placement, its architecture and automatic inline data reduction contribute to efficient data handling for performance-sensitive tasks.

• Data reduction techniques optimized for AI/ML: Myriad offers inline deduplication and compression (compaction) capabilities. These standard techniques help reduce the storage footprint for various datasets, including those used in AI/ML, although specific optimizations tuned for the unique characteristics of diverse AI data types were not detailed.

Opportunities
Quantum has room for improvement in a few decision criteria, including:

• GPU-direct storage (GDS) integration: Support for NVIDIA GDS, crucial for optimizing the data path directly to GPU memory and maximizing training efficiency, is currently unavailable. It remains a roadmap item planned for a future direct client release (targeted for the second half of 2025).

• Security and Data Integrity for AI/ML: While providing foundational security like snapshots and access control via standard protocols, Myriad lacks advanced features specifically tailored to AI/ML security threats, such as integrated model vulnerability scanning, adversarial attack detection, or MLOps-focused data lineage tracking.

• Integrated data pipeline support: Myriad offers basic integration capabilities through standard protocols (NFS, SMB) and features like FlexSync for data movement. However, it currently lacks specific prebuilt connectors or deeper integrations with widely used data pipeline orchestration tools like Apache Spark, Airflow, or Kubeflow.

Purchase Considerations
Myriad is sold as a turnkey appliance solution with a capacity-based software subscription and tiered support contracts. This simplifies initial deployment but restricts hardware choice. The focus on specific high-performance use cases (AI/ML, life sciences, and media and entertainment) aligns with its Feature Play positioning. While manageability aims for simplicity, the on-premises limitation and dependency on future roadmap features (GDS, S3) are significant factors for potential buyers evaluating long-term fit and flexibility. TCO calculations should factor in the subscription model and claimed storage efficiencies.

Use Cases
Myriad's high-performance architecture targets specific, data-intensive workloads. It is most suitable for AI/ML model training and inference, life sciences research, data science operations, and media and entertainment workflows for which its throughput and latency capabilities are advantageous. Its applicability to broader enterprise needs like data lakes or diverse workload consolidation depends heavily on the future delivery of S3 support and potentially wider deployment models.

Qumulo: Qumulo Cloud Data Platform

Solution Overview
Qumulo provides the Qumulo Cloud Data Platform, focused on hybrid cloud data management for unstructured data workloads. The platform is built on Qumulo Core software, designed as a "Run Anywhere" architecture deployable on-premises using standard x86/ARM hardware (all-flash, dual flash, or hybrid) or in the public cloud. Cloud options include Cloud Native Qumulo (CNQ) for customer-managed deployments on AWS and Azure (with GCP and OCI planned), and Azure Native Qumulo (ANQ), a fully managed SaaS offering. Key components include the Cloud Data Fabric for unifying data across environments under a single namespace and NeuralCache for AI-driven performance optimization via predictive caching and prefetching. Qumulo employs an API-first architecture managed via WebUI or CLI, supplemented by the Qumulo Nexus SaaS portal for multi-instance monitoring. With frequent monthly updates and a focus on cloud-native capabilities, data fabric, and performance innovations like NeuralCache, Qumulo leans towards an Innovation strategy, delivering an aggressive roadmap and rapid advancement.

Qumulo is positioned as a Challenger and Fast Mover in the Innovation/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Qumulo scored well on a number of decision criteria, including:

• AI-optimized data layout and management: Qumulo scored well here due to its NeuralCache feature, which uses AI/ML models to analyze access patterns, automatically prefetching and caching data to optimize performance for active workloads. This adaptive caching, combined with the Cloud Data Fabric, aims to make remote data feel local, streamlining data access across hybrid environments.  

• Integrated data pipeline support: The platform facilitates pipeline integration through its Cloud Data Fabric, which simplifies data ingestion and movement among edge, core, and cloud endpoints under a global namespace. It also offers a change notify API to trigger processing pipelines upon data ingest and supports standard protocols (NFS, SMB, S3) used by many pipeline tools.  

• Metadata management and acceleration: Qumulo excels by leveraging its Cloud Data Fabric and NeuralCache to prioritize metadata handling for performance. Metadata is used as the object index, aggregated for real-time analytics, cached efficiently, and replicated quickly across the fabric to speed up data discovery and access. Support for custom key-value metadata tags is also included.

Opportunities
Qumulo has room for improvement in a few decision criteria, including:

• GPU-direct storage (GDS) integration: Qumulo currently lacks support for GDS, instead focusing on optimizing standard protocols like NFS, SMB, and S3. While NFS over RDMA is planned for 2025, the absence of GDS integration today could be a limitation for specific GPU-intensive AI workloads seeking the lowest possible latency via direct data paths.  

• NVMe/NVMe-oF support: The solution does not focus on end-to-end NVMe-oF, operating primarily at the unstructured data protocol layer (NFS, SMB, S3). While leveraging NVMe for caching, the lack of NVMe-oF support means it may not fully exploit the potential performance gains of this fabric for block-level access patterns sometimes used in complex AI pipelines.  

• Data reduction techniques optimized for AI/ML: While compression is utilized in cloud deployments leveraging underlying object storage, the platform lacks broader, AI-specific data reduction techniques like advanced deduplication applied across the dataset. Qumulo notes that much AI data is non-compressible, but more sophisticated reduction techniques could offer benefits for certain large datasets.

Purchase Considerations
Qumulo licenses its software (Qumulo Core and Cloud Data Fabric add-on) based on capacity (per TB per month), offering a software-defined approach. Cloud products are sold via marketplaces with metered billing, and this capacity-based subscription model provides transparency. The "Run Anywhere" strategy supports flexible deployments across a variety of hardware vendors, on hypervisors, and in the cloud via Cloud Native Qumulo (CNQ) on AWS or Azure, along with the fully managed Azure Native Qumulo (ANQ) service. This primarily caters to large enterprises, MSPs, and CSPs. The unified architecture and feature set across these deployment types simplify management. While this offers flexibility, deployment does require selecting appropriate hardware or cloud instances. 

Qumulo's API-first design and real-time analytics further aid manageability. Qumulo offers a suite of professional services to help customers deploy and operationalize their clusters, including site evaluations, installation, orientation sessions, and specialized services for node additions and cloud implementations. Migration ease depends on the source system and protocols. This is a comprehensive data platform applicable across environments.

Use Cases
As a Platform Play, Qumulo's Cloud Data Platform is designed for broad applicability across industries leveraging data-intensive workflows. It suits organizations adopting hybrid or public cloud strategies for unstructured data, especially where performance is critical. Specific use cases include AI research, media production/archives (MAM), healthcare (PACS VNA), pharmaceutical discovery, genomics, financial services, and national intelligence, leveraging its support for NFS, SMB, and S3 protocols. The Cloud Data Fabric and NeuralCache specifically enable use cases requiring geographically distributed data access with high performance, such as multisite collaboration or processing data near its point of origin (the network edge) while managing it centrally.

Scality: RING XP

Solution Overview
Scality offers RING XP, a high-performance, all-flash configuration of its established RING software-defined object storage platform, specifically optimized for demanding AI workloads. Designed for on-premises deployment on certified hardware from vendors like HPE, Dell, Supermicro, and Lenovo, RING XP targets the entire AI data pipeline, from managing exabyte-scale AI data lakes to delivering microsecond-level latencies required for model training, fine-tuning, and inferencing. It employs a dual-access strategy: a streamlined AI API for ultra-low latency (~500µs) access to small objects, and a "fast S3 mode" emphasizing throughput and compatibility for data lake access. The distributed, disaggregated architecture ensures high availability and scalability. While primarily a software-only solution for customer hardware, its focus on performance, a roadmap including AI-driven management tools (Guardian, Insight), and addressing of emerging AI needs positions RING XP strongly in the Innovation hemisphere.

Scality is positioned as a Challenger and Fast Mover in the Innovation/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
Scality scored well on a number of decision criteria, including:

• NVMe/NVMe-oF support: Scality's RING XP platform is explicitly built and optimized to run on all-NVMe flash server infrastructure. This design choice directly leverages the substantial performance benefits inherent in NVMe technology, such as significantly reduced latency and increased bandwidth, which are critical factors for efficiently feeding data to compute-intensive AI training and inference processes and alleviating storage bottlenecks.

• Integrated data pipeline support: The platform demonstrates extensive integration capabilities, supporting key AI/ML frameworks (like TensorFlow and PyTorch) and boasting validation with over 150 ISV applications relevant to various stages of the AI data pipeline, including data processing, orchestration, RAG frameworks, and MLOps tools.  

• Security and data integrity for AI/ML: Scality emphasizes cyber resilience through its multilayered CORE5 framework, providing robust protection against ransomware and other threats via features like S3 Object Lock immutability, encryption, distributed erasure coding, architectural safeguards, and geo-replication options.

Opportunities
Scality has room for improvement in a few decision criteria, including:

• GPU-direct storage (GDS) integration: Scality RING XP supports GDS via its file system interfaces. However, direct GDS integration with its native object storage is not yet a generally available feature, largely reflecting the current market status of enabling technologies from GPU vendors (like NVIDIA's cuObject) as still emerging. While Scality is exploring this capability with partners, users specifically requiring a direct GDS path to object storage will need to await broader ecosystem and technology maturation.

• Data reduction techniques optimized for AI/ML: RING XP offers integrated compression via its storage accelerator feature but lacks specific details on other common techniques like deduplication or compaction, particularly whether they are optimized for the unique characteristics and access patterns of diverse AI/ML data types.

• AI-optimized data layout and management: While configurable object splitting and chunk sizing offer some optimization potential, the platform lacks specific details on broader AI-driven techniques for dynamically optimizing data layout based on workload analysis, representing an area for potential enhancement.

Purchase Considerations
Scality RING XP is licensed as software via a capacity-based OpEx subscription model (1-, 3-, or 5-year terms). This model includes all features (file/object) and baseline support; premium support (scale care services) is extra. Primarily deployed on-premises using customer-provided, certified all-flash hardware, it targets large enterprises, service providers, and specialized research and government institutions with demanding AI workloads. The software-defined nature of the solution allows hardware flexibility within reference architectures. Manageability is facilitated by REST APIs, GUI/CLI, and upcoming AI tools. Its comprehensive feature set addressing the end-to-end AI pipeline positions it as a Platform Play. Deployment complexity involves integrating with existing networks and potentially acquiring certified hardware.

Use Cases
RING XP targets demanding, large-scale AI use cases. Its high performance makes it suitable for AI model training, fine-tuning, and inferencing, especially where low latency for small object access is critical. It also excels in supporting massive AI data lakes and lake houses (proven at 100 PB+ scale) requiring high throughput and robust S3 compatibility for data aggregation, curation, and processing via integrated tools (like Spark, Trino, and Splunk). Specific verticals include financial services (fraud detection), life sciences/genomics, research, service providers building AI clouds, transportation, and government/intelligence. The integrated file services also support HPC archive and analytics use cases.

VAST Data: VAST Data Platform

Solution Overview
VAST Data offers the VAST Data Platform, described as a vertically integrated platform designed to store and analyze both structured and unstructured data, particularly for HPC and AI workloads. Built on its Disaggregated Shared-Everything (DASE) architecture, the platform aims to provide high performance, massive scalability, and simplified management. It comprises several components: DataStore (multiprotocol storage—NFS, SMB, S3, NVMe/TCP), DataSpace (includes replication as well as the global namespace), DataBase (transactional data lake with vector support), and DataEngine (workflow automation/event broker). VAST emphasizes delivering all-flash performance at archive economics, largely eliminating traditional tiering. With frequent releases (a major release every four months), recent additions like a global namespace and vector database, and a focus on integrating compute functions via its DataEngine, VAST demonstrates a strong Innovation focus.

VAST Data is positioned as a Leader and Outperformer in the Innovation/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
VAST Data scored well on a number of decision criteria, including:

• GPU-direct storage (GDS) integration: VAST Data provides highly optimized support for NVIDIA GDS. This enables direct data transfer between GPUs and VAST storage over RDMA, bypassing CPU overhead and allowing data streaming at full NVMe speeds crucial for accelerating AI training.

• Security and data integrity for AI/ML: VAST Data offers comprehensive and robust security features suitable for demanding AI environments. It employs a unified security model within a zero trust framework, providing granular permissions (RBAC/ABAC), strong encryption options, immutable snapshots, object versioning, and proactive threat protection capabilities.

• Data reduction techniques optimized for AI/ML: The platform uses advanced and highly effective similarity-based data reduction alongside efficient erasure coding. This approach delivers significant storage efficiency, often crucial for large AI datasets, without compromising performance.

VAST Data is classified as an Outperformer due to its notably rapid pace of development and innovation over the past 6 to 12 months. The vendor has maintained a higher release cadence than many competitors, delivering significant enhancements to its object storage capabilities. This includes a 4x improvement in write bandwidth, the introduction of synchronous replication for active-active clusters, and expanded cloud integrations with AWS and GCP, all within the last year. Furthermore, VAST Data has a strong roadmap for the coming year, with plans for features like Archive Cloud clusters, enhanced S3 security (STS, SSE-C, SSE-KMS), virtual Parquet/Iceberg presentation, and extensions to multitenancy. These ongoing developments and ambitious future plans position VAST Data to potentially leap forward in the object storage market.

Opportunities
VAST Data has room for improvement in a few decision criteria, including:

• NVMe/NVMe-oF support: While VAST Data provides advanced support with internal NVMe over RDMA and client-facing NVMe/TCP, the current lack of client-side NVMe over RDMA, though planned, represents an opportunity to further optimize performance for specific ultra-low latency AI workload requirements.

• AI-optimized data layout and management: While functional, the platform's capabilities here rely mainly on the inherent benefits of the DASE architecture and metadata cataloging. It lacks more explicit, adaptive AI techniques specifically for optimizing data layout based on dynamic AI workload patterns, such as intelligent caching or predictive prefetching beyond standard protocol mechanisms.

• Quality of service (QoS) and workload isolation: The platform offers advanced capabilities suitable for AI/ML, including CNode pools, tenant-level QoS limits, and secure multitenancy. Opportunity exists to ensure these controls continue evolving to meet the increasingly complex and fine-grained isolation demands anticipated in future large-scale, dynamic multitenant AI environments.

Purchase Considerations
VAST Data offers subscription licensing primarily based on usable capacity (in 100 TB units/year), with an additional compute (vCPU) license for CNodes exceeding an included allowance, relevant for DataEngine/DataBase workloads. The "Gemini" model for direct customers involves purchasing hardware (often at a discount) via VAST partners, with VAST providing software/support licenses (transferable, up to 10-year hardware lifespan). VAST Data's software can also be consumed through HPE GreenLake for Files, which offers customers a more traditional appliance model. Software-only options exist for certified servers (Supermicro, Cisco, Lenovo). Pricing aims for all-flash at archive economics (<$0.01/GB/mo reported by some customers over 5 years). Primarily targeting large enterprises and service providers, VAST requires significant scale; cloud options are currently less mature than on-premises deployment. Its comprehensive nature makes it a Platform Play.

Use Cases
The VAST Data Platform supports demanding, large-scale use cases, particularly in AI/ML and HPC. It excels in deep learning model training and inference, GenAI/LLMs, autonomous systems/computer vision, and data analytics. Its high throughput and low latency, combined with native GDS support, make it ideal for feeding large GPU clusters. The integrated DataBase with vector support caters to RAG and other AI applications requiring structured and unstructured data convergence. The platform's scalability suits large enterprises, research institutions, CSPs building AI clouds (like Coreweave, Lambda), and organizations consolidating multiple storage silos into a single, high-performance data lake or lakehouse accessible via multiple protocols (NFS, SMB, S3, NVMe/TCP).

WEKA: WEKA Data Platform

Solution Overview
WEKA offers the WEKA Data Platform, a software-defined, cloud-native solution designed for high-performance workloads like AI/ML, HPC, and HPDA. It uses a distributed, parallel file system architecture (WekaFS) that aims to deliver exceptional performance (low latency, high throughput, high IOPS) across a unified namespace spanning NVMe flash and scalable object storage tiers. WEKA supports multiprotocol access (POSIX, NFS, SMB, S3) and runs on standard server hardware on-premises (including a certified WEKApod appliance for NVIDIA DGX SuperPOD) or natively in major public clouds (AWS, Azure, GCP, OCI), enabling flexible hybrid deployments. Its containerized microservices architecture and kernel bypass technologies are key to its performance claims. With a focus on performance, broad deployment flexibility, a monthly release cadence, and ongoing innovation targeting areas like AI inference optimization (augmented memory grid), WEKA strongly aligns with an Innovation strategy.

WEKA is positioned as a Challenger and Outperformer in the Innovation/Platform Play quadrant of the storage for AI workloads Radar chart.

Strengths
WEKA scored well on a number of decision criteria, including:

• GPU-direct storage (GDS) integration: WEKA provides robust, mature GDS integration, supporting direct data transfer between GPUs and storage over both Infiniband and Ethernet (RoCEv2). As an early GDS adopter, WEKA shows significant throughput improvements, especially with large IO requests and in congested networks, which is critical for maximizing GPU utilization during training.

• Integrated data pipeline support: The platform excels in supporting end-to-end AI/ML data pipelines. Its strong multiprotocol support (POSIX, S3, NFS, SMB) allows seamless data access across different pipeline stages, while its containerized architecture and high-performance Kubernetes CSI plugin streamline integration with modern, containerized AI workflows.

• Quality of service (QoS) and workload isolation: WEKA offers effective QoS capabilities, allowing users to set preferred and maximum throughput limits per application for performance management. The platform architecture is also designed to prevent "noisy neighbor" issues, helping to ensure predictable performance in shared AI/ML environments.

WEKA is classified as an Outperformer given its demonstrated rapid pace of development over the past year, evidenced by significant enhancements such as composable multitenancy features for GPUaaS environments, expanded cloud integrations (including AWS SageMaker HyperPod, AWS ParallelCluster, Azure CycleCloud), and notable performance improvements like single-hop writes and high-performance S3 access. The company also presented a strong forward-looking roadmap targeting critical AI challenges, including inference optimization through its augmented memory grid concept, enhanced RAG pipeline support (WARRP), and substantial future scaling improvements planned for its next LTS release cycle.

Opportunities
WEKA has room for improvement in a few decision criteria, including:

• AI-optimized data layout and management: While WEKA's architecture inherently optimizes data layout through features like 4K granularity and distributed metadata, it currently lacks explicit AI/ML-driven techniques for predictive data placement or automated prefetching based on dynamic workload analysis, representing an area for future enhancement.

• Data reduction techniques optimized for AI/ML: The platform offers block-variable differential compression and advanced deduplication. However, the effectiveness varies significantly depending on the workload, and specific optimizations tuned for the unique characteristics of diverse AI/ML data types could offer more consistent efficiency gains.

• Security and data integrity for AI/ML: WEKA provides a robust foundational security posture with encryption, checksums, RBAC, and Active Directory integration. There’s an opportunity to build on this by incorporating features explicitly addressing advanced AI-specific threats, such as model vulnerability scanning, or creating integrated defenses against adversarial attacks.

Purchase Considerations
WEKA is licensed as software based on usable capacity managed, with differentiated pricing for the performance (NVMe) tier and the capacity (object storage) tier. This allows cost optimization based on data access patterns. Licenses include standard support; managed services are available at a premium. Deployment options are highly flexible: software-only on any standard servers (Dell, HPE, Supermicro), via a pre-integrated WEKApod appliance (certified for NVIDIA DGX SuperPOD), or natively in major public clouds (AWS, Azure, GCP, OCI), supporting on-premises deployment and several Neoclouds (Yotta) and hybrid models. Its comprehensive capabilities position it as a Platform Play. The software-defined nature simplifies deployment and upgrades, often handled via its Kubernetes Operator.

Use Cases
As a high-performance Platform Play, the WEKA Data Platform excels in demanding AI/ML use cases requiring high throughput, low latency, and massive concurrency. It is well suited for large-scale model training (LLMs, computer vision), AI inferencing, RAG pipelines, simulations/digital twins, and GPU-accelerated analytics (RAPIDS). Its robust multiprotocol support (POSIX, NFS, SMB, S3) and GDS integration make it ideal for complex data pipelines. Target verticals include life sciences (genomics, Cryo-EM), financial services (quantitative trading), media and entertainment, manufacturing, automotive, and public sector/research. Its ability to run converged on GPU servers or as dedicated infrastructure, across on-premises and cloud environments, supports diverse deployment needs for large enterprises and service providers (GPUaaS).

Storage infrastructure selection fundamentally impacts AI workload performance, efficiency, and overall project success. This critical component directly affects data flow, compute resource utilization, and the timeline for realizing business value from AI initiatives. The market for AI storage solutions is expanding rapidly, driven primarily by the massive data requirements of large language models and generative AI applications, along with their intensive processing needs.

The current market offers several distinct approaches to AI storage. Parallel file systems, often adhering to POSIX standards for broad compatibility and robust multi-node orchestration that’s crucial in distributed AI environments, excel at handling large-scale training workloads through high-performance distributed architectures. Scale-out NAS solutions, frequently providing POSIX-compliant access via protocols like NFS, offer incremental growth capabilities with simplified management interfaces that appeal to many organizations looking to support their AI pipelines. Object storage systems deliver cost-effective foundations for data lakes, with newer offerings incorporating performance-optimized tiers. Purpose-built AI data platforms combine specialized hardware and software stacks optimized for specific tasks like inferencing or retrieval-augmented generation. Notably, these previously distinct categories are increasingly converging as vendors enhance their offerings to address the complete AI data pipeline.

When evaluating storage options, organizations must recognize that no single solution adequately serves all AI workflow stages. Different phases demand different storage attributes. Data ingestion requires scalability and cost-effectiveness, typically provided by object storage or scale-out NAS. Data preparation needs strong throughput and flexible access methods, often found in scale-out NAS, parallel file systems, or high-performance object storage. Training demands ultra-low latency combined with high throughput and IOPS, typically delivered by parallel file systems or specialized platforms with NVMe technology. Inference workloads require consistently low read latency and IOPS, achievable through solid-state storage, performant NAS/object systems, or specialized key-value and vector stores.

Several key technological developments are shaping purchasing decisions in this market. Flash storage, particularly NVMe SSDs and NVMe-oF, has become essential for meeting the low-latency and high-throughput requirements of AI workloads. GPU-direct storage capabilities that bypass CPU bottlenecks are increasingly critical for maximizing processing efficiency. Integration with MLOps platforms supports necessary lifecycle management, versioning, and reproducibility. Additionally, solutions that enable consistent data access and mobility across hybrid and multicloud environments are growing in importance as organizations distribute AI workloads across diverse infrastructures.

Organizations approaching this market should begin by designing architectures specifically matched to their AI workload phases, which often necessitates implementing multiple storage tiers. Evaluation criteria should extend beyond raw performance to include scalability potential, data management capabilities, MLOps integration, and support for emerging technologies like GPU-direct storage. A comprehensive TCO analysis should account for both capital expenditures and operational costs, including power, cooling, management overhead, licensing, and potential cloud egress fees. Performance metrics relevant to specific workloads, such as performance-per-dollar and IOPS-per-dollar, typically provide more valuable insights than simple cost-per-capacity measurements.

Before implementing new storage infrastructure, organizations should conduct rigorous proof-of-concept testing using representative workloads and clearly defined success criteria. This approach validates both technical performance and integration capabilities while significantly reducing investment risk. Simultaneously, developing a comprehensive data management strategy that addresses governance, security, compliance, quality, access controls, and full data lifecycle considerations will ensure the long-term success of AI initiatives.

Looking forward, the AI storage market is evolving toward more intelligent, automated data platforms focused on orchestrating data flows across complex memory and storage hierarchies. Emerging technologies like compute express link (CXL) will likely reshape these hierarchies by enabling new approaches to memory expansion, pooling, and tiering. Computational storage, which processes data directly on storage devices, will gain traction, particularly for edge deployments where reducing data movement improves efficiency. The emphasis increasingly shifts toward dynamic, intelligent orchestration of data across various tiers and locations, including specialized components like vector databases and feature stores.

Organizations can best prepare for these developments by maintaining rigorous alignment between workloads and storage architectures, adopting holistic data strategies that encompass governance and security, designing for architectural flexibility to accommodate new models and interconnect technologies, and investing in specialized data engineering and MLOps skills. Success ultimately depends on transitioning from viewing storage as static repositories to embracing intelligent platforms that dynamically manage data placement and movement to optimize AI workflows.

*Vendors marked with an asterisk did not participate in our research process for the Radar report, and their capsules and scoring were compiled via desk research.

For more information about our research process for Radar reports, please visit our Methodology.

My mission is to deliver innovative and scalable solutions that enable data-driven decision making and business transformation. I have extensive knowledge and skills in big data, data warehousing, Apache Airflow, and Google Cloud Platform, where I hold three professional certifications. I enjoy collaborating with clients and partners, sharing best practices, and mentoring the next generation of data and cloud professionals.

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