4 The best storage architecture for retrieval-augmented generation (RAG) is usually a layered architecture that combines object storage for the authoritative dataset with databases and indexes optimized for retrieval. RAG is often discussed as if storage begins and ends with a vector database. It does not. A production RAG system may need to retain millions or billions of source objects, extracted text, embeddings, metadata, indexes, model artifacts and continuously updated versions of that data. Each part has different requirements. For enterprise RAG, the storage architecture therefore needs to support two distinct jobs: keeping the underlying knowledge corpus durable, scalable and governable, and making the relevant portions of that corpus fast to retrieve during inference. That distinction becomes increasingly important as RAG moves from small experiments to enterprise knowledge systems containing hundreds of terabytes or petabytes of unstructured data. The short answer: Use object storage as the data foundation A strong RAG storage architecture typically looks like this: Source data → object storage → data processing → embeddings/indexes → retrieval layer → LLM Object storage serves as the durable data foundation. It can hold documents, images, video, audio, logs, datasets, extracted content and other unstructured information used by the RAG pipeline. A vector database, search engine or other retrieval system then maintains the structures required to locate relevant information quickly. This separation matters because a vector database is designed primarily to find information, while the underlying storage system is responsible for retaining and managing the information itself. For large enterprise environments, asking one system to perform both roles can create unnecessary cost, scaling and governance problems. Why RAG storage is different from ordinary application storage Traditional applications often operate on relatively structured records with predictable access patterns. RAG systems work differently. A typical enterprise knowledge corpus can contain: PDFs and Office documents emails and knowledge-base articles images, audio and video application and machine logs research datasets source code product documentation archived business records sensor and IoT data previously generated AI content Most of this information is unstructured or semi-structured. The RAG pipeline must ingest the source material, extract or transform its contents, divide information into usable units, generate embeddings or other retrieval representations, associate metadata and make the resulting information searchable. Meanwhile, the original source material often needs to remain available. That creates multiple storage requirements instead of one. The four storage layers of a RAG architecture A useful way to think about RAG storage is as four logical layers. 1. Source data storage This is the authoritative corpus from which the RAG system derives its knowledge. The source layer can contain enormous amounts of unstructured information and generally benefits from storage that provides: high capacity strong durability horizontal scalability rich metadata parallel access S3 compatibility lifecycle management security and access controls Object storage is particularly well suited to this role because it was designed to manage large collections of unstructured data without requiring a traditional hierarchical file system. The source corpus also tends to grow continuously. A RAG application might begin with several terabytes of documents but eventually incorporate years of historical information, multimedia assets, application data and additional enterprise repositories. The architecture should therefore be designed around the expected long-term corpus rather than only the size of the initial RAG project. 2. Processing and intermediate storage Before information becomes searchable, it usually passes through an ingestion pipeline. Depending on the workload, that process can produce extracted text, normalized documents, chunks, metadata, temporary datasets and other intermediate objects. Keeping these artifacts separate from the original corpus can make pipelines easier to rerun and troubleshoot. It can also prevent teams from repeatedly performing expensive transformations on the same source data. Object storage can again be useful here because intermediate datasets can become surprisingly large at scale. 3. Embedding and retrieval storage This is where the vector database, search index or other retrieval technology enters the architecture. Embeddings represent information mathematically so that a retrieval system can identify content related to a query. Vector databases are designed to perform these searches efficiently. But embeddings are not necessarily the authoritative copy of the enterprise information. They are derived from it. That distinction affects architecture. If an embedding model changes, chunking strategies are modified or metadata needs to be rebuilt, organizations may need to regenerate large portions of the retrieval layer. Keeping the original and processed datasets independently available makes that possible without rebuilding the knowledge corpus from external systems. 4. Model and AI pipeline storage RAG infrastructure can also generate or consume model files, checkpoints, evaluation datasets, prompt libraries and other AI artifacts. Some of these datasets require very high performance. Others need inexpensive long-term retention. The result is often a broader AI data architecture in which different storage technologies serve different stages of the pipeline. The goal is not to force every workload onto the fastest storage available. It is to make the appropriate performance available where it produces a measurable benefit. Why object storage works well for RAG Object storage has several characteristics that align particularly well with enterprise RAG. It scales with the knowledge corpus RAG systems become more useful as they gain access to more relevant information. That can turn storage capacity into a long-term architectural issue. A departmental assistant might operate on a relatively small document repository. An enterprise RAG platform could eventually index information from dozens of applications and business units. Object storage is designed to scale horizontally across very large datasets, making it suitable for knowledge repositories that may grow from terabytes toward petabytes. It handles unstructured data naturally RAG source information rarely fits neatly into database tables. Object storage can retain files and other data objects alongside metadata without imposing a rigid schema on the entire repository. That allows different AI pipelines to process the same underlying information in different ways. A document could be used by one semantic search application today and become training or evaluation data for another AI system later. It separates durable data from temporary indexes Indexes change. Embedding models improve. Chunking strategies evolve. Retrieval algorithms change. New applications may require different representations of the same data. The underlying enterprise information should not have to move every time the retrieval technology changes. Using scalable object storage as the durable data layer gives organizations a relatively stable foundation underneath a faster-moving AI software stack. It supports parallel data access AI pipelines often need to process large numbers of objects simultaneously. Multiple workers may extract documents, generate embeddings or enrich metadata in parallel. Object storage architectures can provide substantial aggregate throughput when large numbers of clients access the repository concurrently. That characteristic becomes increasingly important as ingestion pipelines expand. Does RAG require all-flash storage? Not necessarily. RAG performance is frequently associated with GPU infrastructure, which can lead teams to assume that the entire RAG dataset should live on high-performance flash storage. The retrieval path does need to be fast enough to keep inference responsive. Vector indexes, frequently accessed data and latency-sensitive working sets may therefore benefit from flash or other high-performance storage. But the complete knowledge corpus can be much larger than the data actively involved in an individual retrieval operation. Consider a RAG environment containing hundreds of terabytes of historical documents. Only a tiny fraction of those documents may be relevant to any given prompt. Maintaining the entire corpus on premium flash can increase infrastructure costs without proportionally improving query latency. A better architecture can place high-performance storage around the retrieval working set while using scalable object storage for the much larger persistent corpus. Should you use a vector database or object storage for RAG? In many architectures, you use both. They solve different problems. RequirementObject storageVector databaseStore original documentsExcellentNot primary purposeStore massive unstructured datasetsExcellentLimited by design/economicsSemantic similarity searchNot primary purposeExcellentStore embeddingsPossibleExcellentLong-term data retentionExcellentNot primary purposeS3 application accessExcellentVariesRetrieval indexingLimitedExcellentRebuild indexes from source dataExcellent foundationDepends on source availability The vector database is part of the retrieval infrastructure. Object storage is part of the data infrastructure. Treating the two as competitors misunderstands the role each plays. What about file storage for RAG? File storage can still play an important role. Some AI frameworks and applications expect POSIX or file-based access. High-performance file systems can also be valuable when workloads involve large numbers of small files, tightly coupled compute or specific HPC and GPU access patterns. Enterprise RAG architectures do not have to choose exclusively between file and object protocols. A broader data architecture may expose data through different access methods depending on the application while maintaining a common scalable storage foundation where appropriate. The decision should be driven by application access patterns rather than the assumption that every AI workload requires the same protocol. A practical enterprise RAG storage architecture For a large enterprise deployment, the architecture might operate as follows. Step 1: Consolidate the knowledge corpus Documents and other source datasets are collected from enterprise systems into scalable storage. The original information remains available as the authoritative source for future processing. Step 2: Process and enrich the data An ingestion pipeline extracts content, cleans it, applies metadata and divides it into chunks suitable for retrieval. Processed outputs can also be retained so that later stages do not need to repeat every transformation. Step 3: Generate embeddings GPU or CPU infrastructure converts chunks into embeddings using the organization’s selected embedding model. These embeddings are loaded into the retrieval system. Step 4: Build the retrieval layer A vector database or search platform indexes embeddings and associated metadata. This layer is optimized for low-latency queries rather than bulk retention of the complete source corpus. Step 5: Retrieve relevant context When a user sends a prompt, the application searches the retrieval layer for relevant information. The system can then retrieve the necessary context and supply it to the LLM. Step 6: Preserve the underlying data Source and processed data remain independently available. If the organization changes embedding models, retrieval engines or AI applications, it can reprocess the corpus without reacquiring all of the original information. This is one of the most important architectural benefits of separating the data layer from the retrieval layer. What should enterprises look for in RAG storage? The best architecture depends on workload size and access patterns, but enterprise teams should evaluate several characteristics. Scalability Estimate the size of the entire AI data estate rather than only the first vector index. Include source documents, processed data, multiple versions, metadata, model artifacts and expected future growth. Throughput RAG ingestion can involve thousands of concurrent processing operations. Aggregate throughput can therefore matter as much as individual request latency. Metadata capabilities Metadata is central to retrieval. Permissions, timestamps, document types, business classifications and other attributes can help constrain searches and improve relevance. The storage architecture should preserve useful metadata rather than stripping context from the underlying information. Data protection A RAG knowledge base may contain some of an organization’s most valuable information. Storage should therefore provide appropriate mechanisms for encryption, access control, immutability, replication and recovery. Protecting the AI application without protecting its underlying knowledge corpus leaves a significant gap. Data sovereignty Enterprise RAG frequently incorporates sensitive internal information. Organizations may need control over where that information resides, who can access it and which infrastructure processes it. On-premises and private-cloud object storage can help organizations build RAG systems while maintaining control over the physical and administrative location of their datasets. S3 compatibility S3 has become a widely supported interface across data and AI ecosystems. S3-compatible storage can make it easier to connect data pipelines, analytics tools, AI frameworks and other applications to a common data repository without designing custom storage integrations for every workload. How Scality fits into a RAG storage architecture Scality RING provides scalable file and object storage for large unstructured datasets, making it relevant as the persistent data layer underneath enterprise AI and RAG environments. Within Scality’s broader Autonomous Data Infrastructure (ADI) strategy, the objective is to provide infrastructure capable of supporting large AI datasets while addressing requirements around scalability, cyber resilience and sovereign control. For RAG, that role is primarily about the data foundation. RING can retain the source documents, datasets and derived information feeding AI pipelines while retrieval technologies such as vector databases handle specialized semantic search. This allows enterprises to evolve embedding models, retrieval engines and AI applications without making those systems the sole repository for the underlying knowledge. For organizations building RAG across large existing data estates, that separation can become increasingly valuable as both the corpus and the number of AI applications grow. The best RAG storage architecture is layered There is no single storage technology that optimally performs every job in a RAG pipeline. The most practical architecture separates responsibilities: Object storage provides the scalable, durable foundation for the knowledge corpus. Processing infrastructure transforms and enriches the data. Vector databases and search engines provide fast retrieval. High-performance storage can accelerate latency-sensitive working datasets where required. This layered approach prevents the retrieval system from becoming the long-term data repository and prevents the entire enterprise corpus from having to reside on the most expensive storage tier. For small RAG applications, that distinction may not matter much. For enterprise deployments operating across petabytes of unstructured data, it can determine whether the architecture remains manageable as the system grows. FAQ What storage is best for RAG? Object storage is a strong choice for the persistent RAG data layer because it can scale to large unstructured datasets while providing durability, metadata and broad S3 compatibility. Vector databases or search indexes can then provide the specialized retrieval layer. Does RAG need a vector database? RAG requires a mechanism for retrieving relevant information, but that does not always have to be a dedicated vector database. Systems can use vector search, keyword search, hybrid search, knowledge graphs or combinations of these approaches depending on the application. Can RAG use S3-compatible object storage? Yes. S3-compatible object storage can hold source documents, processed datasets and other AI artifacts while integrating with data-processing and AI tools that support the S3 API. Is object storage fast enough for RAG? It depends on where it sits in the architecture. Object storage does not need to replace the low-latency vector index. It can provide high aggregate throughput for ingestion and durable storage for the much larger knowledge corpus while specialized retrieval infrastructure handles latency-sensitive searches. How much storage does a RAG system need? Storage requirements depend on the size of the source corpus, the number of processed copies, embedding strategy, metadata, indexes and retention requirements. Enterprises should plan for the complete AI data lifecycle rather than estimating capacity solely from the size of the vector database. Should RAG data be stored on-premises or in the cloud? Both are viable. The decision depends on data gravity, economics, security, sovereignty and where AI compute operates. Organizations with very large or sensitive enterprise datasets may prefer private or on-premises infrastructure, while other workloads can benefit from public cloud services.
