5 The best storage for generative AI depends on what the AI system is doing. Large-scale model training may require extremely high throughput, retrieval-augmented generation (RAG) needs efficient access to large collections of enterprise data, and inference can introduce different requirements for model loading, caching and generated content. The main options include object storage, parallel file storage, all-flash storage, cloud object storage and local NVMe storage. Many enterprise generative AI environments use more than one because no single storage technology is optimized for every stage of the AI data lifecycle. The right choice depends on dataset size, performance requirements, data access patterns, infrastructure location, security requirements and cost. What storage options are available for generative AI? Generative AI storage generally falls into five categories. Each addresses a different part of the performance, capacity and cost spectrum. 1. Object storage Object storage is commonly used for large AI datasets because it can scale across petabytes of unstructured data while providing access through S3 APIs. It can store training datasets, documents used for RAG, images, video, model files, checkpoints and generated content. Object storage can also serve as a shared data layer for multiple applications rather than tying datasets to a specific server or compute cluster. Performance varies considerably between object storage platforms. Some are designed primarily for capacity and archival workloads, while others are built to support high-throughput AI and analytics environments. Best suited for: Large unstructured datasets, AI data lakes, RAG source data, model repositories and environments where capacity needs to scale significantly. 2. Parallel file storage Parallel file systems distribute data across multiple storage nodes so many clients can access it concurrently. They are widely used in high-performance computing and have become important in large AI training environments. Their primary advantage is the ability to deliver high throughput to large GPU clusters. File interfaces can also simplify integration with AI applications designed around traditional file-system semantics. The tradeoff can be infrastructure complexity and cost, particularly when very large quantities of less frequently accessed data need to be retained. Best suited for: Large GPU clusters, high-performance model training and workloads requiring parallel file access. 3. All-flash storage All-flash systems use solid-state media to provide high throughput and low latency. They can be useful for performance-sensitive AI workloads where storage delays could affect expensive GPU resources. Flash can support active training datasets, model serving, preprocessing and other parts of an AI pipeline that require rapid access to data. Capacity economics become an important consideration as datasets grow. An organization may have petabytes of potential AI data even if only a portion of it requires flash-level performance. Best suited for: Performance-sensitive training, inference and active AI datasets where latency and throughput justify the additional cost. 4. Cloud object storage Services such as Amazon S3, Azure Blob Storage and Google Cloud Storage are widely used in generative AI environments running in the public cloud. They provide elastic capacity and close integration with cloud AI services, data platforms and managed machine learning tools. This can make them particularly convenient for organizations already building AI applications around a hyperscale cloud. Organizations should also account for ongoing capacity charges, data transfer costs, data residency requirements and the implications of moving very large datasets between cloud and on-premises infrastructure. Best suited for: Cloud-native generative AI, experimentation, managed AI services and workloads already located in a public cloud. 5. Local NVMe storage NVMe storage provides extremely fast local access and can sit close to CPUs and GPUs. It is useful for temporary datasets, caches, scratch space and other parts of the AI pipeline where latency matters more than capacity or long-term durability. It can also complement shared storage by keeping frequently accessed data closer to compute. Because local NVMe is attached to individual systems, it is generally one component of a larger AI storage architecture rather than the repository for an organization’s complete AI dataset. Best suited for: Caching, scratch data, temporary working sets and latency-sensitive processing close to compute. Generative AI storage comparison The differences become clearer when the options are compared according to what they are designed to accomplish. Storage typeBest usePrimary strengthMain considerationObject storageLarge AI datasets and RAGScale and capacity economicsPerformance varies by platformParallel file storageLarge-scale trainingParallel throughputCost and operational complexityAll-flash storageActive high-performance workloadsLow latency and throughputEconomics at very large capacityCloud object storageCloud-native AIElasticity and cloud integrationOngoing cost and data movementLocal NVMeCache and scratchVery low latencyLimited shared capacity This is why asking whether object, file or flash storage is “best for AI” can be misleading. The answer changes depending on which part of the AI environment is being considered. What storage should I use for AI model training? AI training can place some of the highest performance demands on storage. GPU clusters may consume large datasets in parallel, and keeping expensive accelerators supplied with data can require substantial aggregate throughput. Large training environments therefore commonly use high-performance object storage, parallel file systems or all-flash infrastructure. Dataset characteristics matter as much as raw capacity. Millions of small files can create different storage behavior from large sequential objects, while preprocessing and network performance can also influence how quickly data reaches GPUs. Organizations building large training clusters should evaluate storage using representative datasets and actual AI pipelines rather than relying solely on peak throughput specifications. For smaller fine-tuning workloads, those extreme requirements may not apply. Fine-tuning an existing model against a comparatively small proprietary dataset can require considerably less infrastructure than training a foundation model from scratch. What storage should I use for RAG? RAG often shifts the storage requirement from maximum training performance toward efficient access to large volumes of enterprise information. A RAG system may draw from documents, support records, technical manuals, research data, images, application information and other proprietary datasets. Those source datasets can grow substantially as organizations connect more information to generative AI applications. Object storage is one option for maintaining these large unstructured data collections. Existing file storage or cloud storage can also be appropriate depending on where the source data already resides and how the RAG pipeline accesses it. Vector databases play a different role. They store and search vector embeddings used during retrieval, but they do not necessarily replace the underlying repository containing the original source information. The storage decision for RAG should therefore consider both the retrieval layer and where the authoritative enterprise data will reside. What storage should I use for AI inference? Inference requirements vary considerably depending on the application. A conversational AI service serving thousands of simultaneous users has different infrastructure requirements from an internal assistant answering occasional employee questions. Model size, concurrency, context length and response-time requirements can all influence storage and memory architecture. Local NVMe and flash can be useful for model loading, caching and other latency-sensitive functions. Shared file or object storage can provide access to models, RAG datasets and generated content across multiple inference systems. Inference also creates new data. Images, video, audio, documents and other AI-generated content can quickly become a capacity issue when generative AI applications operate at enterprise scale. Storage planning therefore needs to account for what AI systems produce as well as what they consume. What storage should I use for large unstructured AI datasets? Object storage becomes particularly relevant when the primary challenge is maintaining very large collections of unstructured information. Generative AI can make previously passive enterprise data more valuable. Historical documents, images, video, scientific data, logs and other information may become training data or context for future AI applications. These datasets can reach petabyte scale even when only a fraction is being actively processed. Object storage is designed for this type of capacity growth and can provide extensive metadata alongside individual objects. S3 compatibility also allows data to be accessed by a growing range of AI, analytics and data engineering tools. Enterprises evaluating object storage for AI should still examine performance carefully. An archival object store and a high-performance object storage platform may expose similar APIs while delivering very different behavior under demanding AI workloads. What storage should I use for private AI? Private AI environments keep AI infrastructure and data within infrastructure controlled by the organization rather than relying entirely on public AI services. The storage options are largely the same, object, file and flash, but the selection criteria change. Organizations may place greater emphasis on data sovereignty, access controls, cyber resilience and integration with existing data center infrastructure. They may also need to provide AI applications with access to data that is already stored on premises. On-premises object storage can provide S3 access without requiring the underlying data to be placed in a public cloud. Parallel file and flash systems can be deployed alongside it when particular AI workloads require their performance characteristics. Private AI therefore does not dictate a particular storage technology. It makes control over the data and infrastructure a more important part of the decision. What about hybrid AI environments? Many enterprises will run generative AI across a combination of locations. Data might remain in an enterprise data center while some AI processing occurs in the cloud. Different GPU environments may be used for training and inference. Organizations may also work with multiple AI platforms as models and infrastructure continue to evolve. In these environments, data mobility and interoperability become important storage considerations. An architecture that performs well but makes datasets difficult or expensive to move can create problems as AI infrastructure changes. Open interfaces such as S3 can help make data accessible across a wider range of applications and platforms. Organizations should consider where data needs to move, how frequently it moves and what those transfers will cost before committing large AI datasets to a particular environment. What should you look for in generative AI storage? Rather than selecting storage based on an “AI-ready” label, evaluate how the platform fits the workloads you actually plan to run. Performance Look at throughput, latency and concurrency in the context of the application. Training a large model across hundreds of GPUs has very different requirements from storing source documents for an internal RAG application. Scalability AI datasets can grow quickly as additional sources are incorporated and generated content accumulates. Consider whether the platform can expand from the current dataset into petabyte-scale environments without creating additional storage silos or requiring disruptive migrations. Data access Determine which interfaces your AI tools actually use. S3 is common across modern AI and data platforms, while some applications still depend on file protocols or POSIX semantics. Support for the right interfaces can be as important as the underlying media. Cost at scale Compare costs based on the amount of data that will actually be stored rather than the initial AI project. Flash economics may be reasonable for an active dataset but very different across multiple petabytes. Public cloud storage can offer convenient elasticity while introducing ongoing capacity and data movement charges. Cyber resilience AI datasets, models and training outputs can contain valuable intellectual property and may be difficult or expensive to recreate. Evaluate immutability, encryption, authentication, access controls and recovery capabilities alongside performance and capacity. Data sovereignty Organizations in regulated industries or jurisdictions with data residency requirements should understand where their AI data resides and who controls the infrastructure holding it. This is particularly relevant when generative AI incorporates sensitive enterprise information. Integration AI infrastructure is changing quickly. Storage that works with multiple compute platforms, AI frameworks and data tools can give organizations more flexibility as models and accelerator technologies evolve. Where does Scality ADI fit? Scality ADI is an enterprise data infrastructure platform designed for organizations managing large-scale unstructured data across AI and other data-intensive workloads. It is relevant to generative AI environments that require scalable object storage, particularly where organizations are building private or hybrid AI infrastructure and want to maintain control over large enterprise datasets. Scality ADI combines high-scale data storage with capabilities focused on enterprise AI, cyber resilience and sovereign control. It can support environments where AI datasets extend into petabytes or beyond and where data needs to remain accessible across changing applications and infrastructure. Scality also works with AI infrastructure technologies including WEKA and F5, enabling organizations to combine different performance and data management layers according to their workload requirements. For organizations evaluating generative AI storage, ADI is therefore one option to consider when large-scale unstructured data, S3 access, cyber resilience and data sovereignty are important selection criteria. Frequently asked questions about generative AI storage Is object storage good for generative AI? Yes. Object storage can be well suited to generative AI workloads involving large unstructured datasets, RAG source data, model repositories, checkpoints and generated content. Performance requirements should still be evaluated for individual workloads. Is file storage better than object storage for AI? Neither is universally better. Parallel file storage can be well suited to highly parallel training workloads, while object storage can provide greater scalability and capacity efficiency for large AI datasets. Some environments use both. Does generative AI require all-flash storage? No. All-flash storage can benefit performance-sensitive workloads, but not every generative AI dataset requires flash-level latency. Capacity, cost and access frequency should also influence the decision. Is cloud storage good for generative AI? Cloud storage can be a strong choice for AI applications already running in public cloud environments. Organizations should consider data transfer costs, residency requirements and long-term capacity economics when storing very large datasets. What storage is best for RAG? Object storage, file storage and cloud storage can all support RAG source datasets. The best choice depends on dataset scale, existing data location, access patterns and how the RAG pipeline integrates with the storage system. How much storage does generative AI need? Requirements range from terabytes for smaller applications to petabytes or exabytes for organizations working with large training datasets, multimedia data or extensive enterprise data repositories. Capacity planning should also include models, checkpoints and AI-generated data. Choosing the right storage for generative AI There is no single storage technology that is best for every generative AI workload. Object storage is a strong option for large unstructured datasets and RAG. Parallel file storage can serve demanding training environments. All-flash and NVMe address workloads where low latency is critical. Cloud object storage can fit applications built around public cloud AI services. Many enterprise environments will ultimately combine several of these approaches. The useful question is therefore not simply, “What is the fastest storage for AI?” It is which storage provides the right combination of performance, scale, access, protection and economics for the specific generative AI workload being built.
