8 AI infrastructure discussions tend to focus on GPUs, accelerators and models. But as enterprise AI moves into production, the storage architecture underneath those compute resources becomes increasingly important. Training datasets can contain billions of objects. Retrieval-augmented generation (RAG) pipelines continuously ingest and retrieve enterprise information. Inference creates additional data that may need to be retained, analyzed or fed back into future models. Meanwhile, organizations have to protect intellectual property, control where sensitive information resides and manage infrastructure costs as data volumes increase. These requirements have created a distinct market for AI storage companies: vendors developing storage and data infrastructure designed for large, performance-sensitive AI datasets. There is no single best AI storage company for every environment. The right choice depends on where AI runs, how much data is involved, required performance, data protection requirements and how the infrastructure needs to scale. Here are several of the companies infrastructure teams are likely to encounter when evaluating storage for enterprise AI. What is AI storage? AI storage is data infrastructure designed to store, protect and deliver the datasets used throughout AI and machine learning workflows. Those datasets are frequently dominated by unstructured data such as documents, images, video, audio, logs, scientific data and model artifacts. AI storage therefore has to accommodate both very large capacity and highly variable access patterns. A typical AI data lifecycle can include: Data ingestion → preparation → training → checkpointing → inference → retention → reuse Each stage can place different demands on storage. Training may require extremely high throughput to keep GPU clusters supplied with data. RAG applications may require access to enormous repositories of enterprise information. Checkpoints and model artifacts need reliable storage and protection. Older datasets may remain valuable for retraining or governance even when they no longer require high-performance media. This makes AI storage architecture a lifecycle problem as much as a performance problem. What should you look for in an AI storage company? Benchmark performance is useful, but it should not be the only criterion when comparing the best AI storage companies. Infrastructure teams should evaluate several characteristics. 1. Ability to scale with AI datasets AI datasets can grow quickly and unpredictably. Storage should therefore scale beyond the requirements of the initial AI project without forcing organizations into disruptive migrations or architectural redesigns. For large enterprise environments, this can mean scaling from petabytes toward tens or hundreds of petabytes, and potentially exabytes. The ability to scale capacity independently from compute or performance can also become important as the AI environment evolves. 2. High-throughput data access Expensive GPU infrastructure should spend its time processing data rather than waiting for storage. AI storage systems need sufficient aggregate throughput and concurrency to serve large GPU clusters while handling other activities such as ingestion, checkpointing and analytics. New approaches such as direct GPU-to-storage data paths and S3 over RDMA are also reducing overhead between object storage and accelerated compute. 3. Support for unstructured data A significant percentage of enterprise information relevant to AI is unstructured. Documents, images, video, medical imaging, sensor output and machine-generated data are common inputs for AI systems. Object storage has consequently become an important component of private AI infrastructure because it provides a scalable namespace and S3 access model for very large unstructured datasets. 4. Data lifecycle flexibility Not every byte in an AI environment requires the same storage media. Active training data may justify flash. Large repositories used for RAG or future training may fit capacity-oriented storage. Older datasets may eventually move to colder storage while remaining available for governance or reuse. Storage platforms that can accommodate different performance and capacity tiers can help organizations avoid storing every AI dataset on the most expensive media. 5. Cyber resilience AI datasets can represent years of accumulated intellectual property. Training data, proprietary models and enterprise knowledge repositories therefore need the same level of protection as other critical business information. Infrastructure teams should evaluate immutability, encryption, authentication, data integrity, failure-domain protection and recovery capabilities alongside performance. 6. Data sovereignty and control Organizations operating private AI infrastructure may need to know exactly where their data resides and who can access it. This is particularly relevant for regulated industries, government organizations and companies operating across jurisdictions. Software-defined storage deployed on infrastructure controlled by the organization can provide an alternative to placing sensitive AI datasets exclusively in public cloud environments. Best AI storage companies to evaluate in 2026 The following vendors represent different approaches to the AI storage problem. Some emphasize maximum flash performance, while others focus on large-scale object storage, unified data platforms or enterprise infrastructure. 1. Scality Best suited for: Enterprise AI environments requiring scalable object storage, flexible infrastructure and long-term management of large unstructured datasets. Scality develops software-defined data infrastructure for organizations operating at multi-petabyte and exabyte scale. Its portfolio includes Scality ADI, RING and ARTESCA, with object storage providing the foundation for large unstructured datasets and AI data pipelines. Scality ADI is designed around the changing storage requirements of enterprise AI. Its architecture can align different storage media and performance characteristics with stages of the data lifecycle rather than requiring organizations to maintain every dataset on a single storage tier. For performance-sensitive AI workloads, Scality supports GPU-direct data access using S3 over RDMA. At the same time, the architecture can span flash, HDD and tape, allowing organizations to retain large datasets without treating all AI data as permanently hot. Scality’s software-defined architecture also separates storage software from proprietary hardware, giving infrastructure teams greater flexibility over server and media choices. Key capabilities include: Multi-petabyte to exabyte scalability S3 object storage for large unstructured datasets GPU-direct performance with S3 over RDMA Independent scaling of infrastructure resources Support for flash, HDD and tape across the data lifecycle Built-in cyber-resilience capabilities Autonomous infrastructure operations Support for private and sovereign AI environments Scality is particularly relevant where AI is one part of a broader enterprise data strategy and organizations need to retain, protect and reuse very large datasets over long periods. 2. VAST Data Best suited for: Large AI and HPC environments prioritizing high-performance flash infrastructure and a unified data platform. VAST Data has become closely associated with large-scale AI infrastructure through its flash-based, disaggregated architecture. The platform combines storage and data services within a unified architecture and is designed to provide high throughput and low latency for GPU-intensive workloads. Its disaggregated design allows capacity and processing resources to scale separately, which can be useful in large AI clusters where infrastructure requirements change over time. VAST is a strong candidate for organizations building high-performance AI environments where flash performance, GPU utilization and a unified data architecture are primary requirements. 3. WEKA Best suited for: GPU-intensive AI training and HPC workloads requiring very high storage performance. WEKA focuses heavily on performance-sensitive AI and high-performance computing environments. Its architecture is designed to deliver low-latency, high-throughput access to data across large GPU clusters. WEKA supports file and object access and is commonly positioned alongside high-density accelerated computing infrastructure. Organizations training large models or operating GPU-heavy AI infrastructure may evaluate WEKA when storage performance is one of the dominant architectural requirements. The tradeoff to consider is whether the broader AI data lifecycle requires the same performance characteristics as the active training tier. Large enterprises may still need additional capacity-oriented infrastructure for the much larger body of data surrounding active training datasets. 4. Everpure Best suited for: Enterprises looking for all-flash storage with strong performance and simplified infrastructure management. Everpure, formerly Pure Storage, provides flash-based storage and data management infrastructure for enterprise AI workloads. Its FlashBlade platform supports highly parallel file and object workloads across AI, analytics and HPC environments. The architecture is designed to deliver high throughput and concurrency for data-intensive applications, including GPU-based AI infrastructure. Everpure can be a strong option for organizations that prioritize flash performance, operational simplicity and non-disruptive infrastructure lifecycle management. For very large AI data estates, infrastructure teams should also consider the economics of retaining data on flash throughout its lifecycle, particularly when significant portions of the dataset do not require maximum performance. 5. Cloudian Best suited for: Organizations looking for S3-compatible object storage for large AI datasets. Cloudian provides scale-out object storage based around its HyperStore platform and has expanded its positioning toward AI infrastructure. Its architecture emphasizes S3 compatibility, large-scale unstructured data and on-premises deployment. Cloudian also supports direct storage access for GPU environments using RDMA-based technology. For organizations considering object storage as the persistent data layer behind AI pipelines, Cloudian represents another option in the market. Other AI storage companies to consider The AI storage market extends beyond these five vendors. Dell Technologies, HPE, IBM and NetApp all provide enterprise storage platforms that can participate in AI infrastructure. Hyperscale cloud providers including AWS, Microsoft Azure and Google Cloud also offer extensive object, file and data services for cloud-based AI workloads. The appropriate vendor shortlist depends heavily on the architecture being built. A cloud-native AI application may favor hyperscale storage services. A large GPU training cluster may prioritize high-performance parallel storage. A private enterprise AI platform may place greater emphasis on scalable object storage, sovereignty, cyber resilience and long-term data economics. Comparing AI storage companies CompanyArchitecture focusStrong fitScalitySoftware-defined object/data infrastructureEnterprise AI, private AI, large unstructured datasets, long-term AI data lifecycleVAST DataUnified flash-based data platformLarge GPU clusters, AI and HPCWEKAHigh-performance parallel data platformGPU-intensive training and HPCPure StorageAll-flash file and object storageEnterprise AI requiring high flash performanceCloudianScale-out S3 object storageLarge on-premises AI datasets This comparison also illustrates why evaluating AI storage solely by peak throughput can be misleading. The highest-performance storage tier is only one component of an AI data architecture. Object storage and the AI data lifecycle One of the most important changes in AI infrastructure is the growing role of object storage. Historically, high-performance AI and HPC environments relied heavily on parallel file systems. Those systems remain valuable for workloads requiring extremely low latency and intensive small-file access. But the amount of data surrounding AI workloads is expanding far beyond the active training dataset. Enterprise AI may draw from years of documents, images, video, telemetry, research data and business records. RAG architectures further increase the value of maintaining large repositories of enterprise information that applications can retrieve when needed. Object storage provides several useful characteristics for this environment: Massive namespace scalability Efficient storage of unstructured data S3 API compatibility Metadata associated with individual objects Independent capacity scaling Strong durability Integration with cloud-native applications Flexible economics at large scale This does not mean every AI workload should run entirely from object storage. Instead, infrastructure architects increasingly need to determine where different storage technologies fit within the overall AI data pipeline. AI storage performance versus AI data economics One of the central design decisions in AI infrastructure is determining how much data actually requires the fastest possible storage. Consider an organization with 50 PB of AI-relevant data. Only a subset may be actively involved in model training at a given moment. Another portion may support RAG or inference. Large amounts could consist of historical training data, previous model versions, checkpoints or datasets retained for future use. Putting the entire 50 PB on premium flash may deliver excellent performance, but it can create unnecessary infrastructure cost and power consumption. The alternative is a data architecture that matches storage resources to workload requirements. Hot training data can reside on high-performance media. Larger persistent datasets can use capacity-oriented storage. Older information can transition to colder media while remaining part of the organization’s governed AI data estate. This approach becomes increasingly important as AI deployments move from individual projects toward shared enterprise infrastructure. Questions to ask AI storage vendors Before selecting an AI storage company, infrastructure teams should test the architecture against the actual data lifecycle rather than a single benchmark. Useful questions include: How does the platform scale when the dataset grows by 10x? Can capacity and performance scale independently? How does the system behave with billions of objects and highly concurrent clients? Which protocols are supported, including S3, NFS and POSIX? Can GPUs access data efficiently without unnecessary CPU or network overhead? How are inactive datasets handled? Can different storage media be used within the architecture? What protections exist against ransomware and accidental deletion? How does the system maintain data integrity during hardware failures? Can the infrastructure operate entirely on premises when sovereignty requires it? Is the organization tied to proprietary storage hardware? What happens to storage cost and power consumption as the environment reaches tens of petabytes? These questions provide a more realistic picture of long-term suitability than peak throughput alone. Which AI storage company is best? The best AI storage company depends on the workload. Organizations building extremely performance-intensive GPU clusters may prioritize vendors such as VAST Data, WEKA or Pure Storage. Organizations building large S3-based AI repositories may evaluate Scality and Cloudian alongside cloud object storage services. For enterprises operating private AI at multi-petabyte or exabyte scale, the decision becomes broader. Performance still matters, but so do cyber resilience, sovereignty, hardware flexibility, operational complexity and the economics of retaining rapidly growing datasets. The most effective AI storage architecture is therefore likely to be the one that can support the entire lifecycle of enterprise AI data while providing the appropriate performance where it is actually required.
