Choosing the Right Storage for AI and ML Workloads

Building the right storage strategy for AI applications and ML workloads is especially difficult at the planning stage. Depending on the requirements, data storage solutions might need to exhibit high throughput, low latency, excellent durability, and the ability to scale to a massive size.

In this guide, we’ll trim the complexity and provide a straightforward rubric on how you should design a data storage strategy for your company. Not all AI systems are the same, so the right storage strategy depends heavily on what kind of AI system you're building.

Storage for training vs inference

There are primarily two types of AI problems: training and inference.

Training is when models are built: a human-designed model architecture learns from vast amounts of training data (e.g., articles, public records, data, document repositories, etc.). All models go through training: large language models, image models, video models, etc. Generally speaking, training requires immense amounts of data and massive amounts of compute. However, it is used to produce models in a periodic fashion.

Inference is different. Inference requires significantly less compute and data: a caller provides a trained model with an input and the model produces an output. However, inference happens every time a model is used, and with more systems relying on AI, the inference cost over a model's entire lifecycle might outweigh the training cost.

This breakdown, however, is from the standpoint of producing models from scratch. Most organizations use an off-the-shelf model (e.g., Claude, GPT-4o, Qwen, etc.). They may fine-tune it further (i.e. post-training), but their data needs center primarily on the inference stage.

Storage for AI Agents

Nowadays, storage design for AI systems transcends models. Most models don't run in a vacuum. Instead, they serve as the brain of an AI agent that autonomously queries and processes data to take actions. For example, an AI agent might look through medical records to determine whether a patient's case should be escalated.

For LLMs, storage is a bottleneck problem. For AI Agents, storage is a shape problem.

When thinking about storage for raw LLMs, it's typically a matter of bottlenecks. For massive training sets, the bottleneck is finding a system that scales to petabyte-level capacity without incurring excessive cost or latency. For inference, the bottleneck is finding a storage system with low enough latency to retrieve contextual data quickly.

However, storage problems for AI agents are less focused on throughput or scale, and more on the data’s shape that makes it easy for an AI agent to process. Previously, most organizations relied on APIs or MCP servers to dynamically surface information to AI agents upon request. However, many organizations are simplifying the process by exposing data to AI agents through a familiar folder structure, allowing an agent to lean on its background knowledge to reliably explore the filesystem for data.

There are a few storage options

Before discussing what storage solution makes sense for what system, let's review the storage systems that are available.

Object Storage

Object storage systems (such as Amazon S3, Google Cloud Storage, or Azure Blob Storage) store data as discrete objects within flat namespaces. Each object includes the data itself, metadata, and a unique identifier. Object storage is cheap, durable, and offers decent latency, making it the go-to choice for storing large, unstructured datasets like training corpora, image libraries, or log archives. Most cloud providers offer tiered pricing, so you can push infrequently accessed data into cold storage at a fraction of the cost. The tradeoff is that object storage isn't optimized for rapid, random read/write patterns—it's better suited for sequential access and bulk operations.

Block Storage

Block storage (such as Amazon EBS, Google Persistent Disk, or Azure Managed Disks) divides data into fixed-size blocks and stores them independently, each with its own address. This architecture delivers low latency and high IOPS, making it ideal for workloads that require fast, random read/write access: serving model weights during inference or running high-performance databases that back AI pipelines. The downside is cost: block storage is significantly more expensive per gigabyte than object storage, and it doesn't scale as elastically. It's best used for performance-critical components rather than bulk data storage.

File Storage

File storage systems (like Amazon EFS, Google Filestore, or Azure Files) organize data in a hierarchical directory structure—folders, subfolders, and files—accessible over POSIX-compliant protocols like NFS or SMB. The biggest advantage is familiarity: both humans and AI agents can navigate a file system intuitively. Performance sits between object and block storage, with reasonable latency and throughput for most workloads. File storage is particularly well-suited for AI agent architectures that expose structured data through a browsable directory, since agents can leverage their background understanding of filesystems to locate and process information without requiring custom API integrations.

Vector Storage

Vector databases (such as Pinecone, Weaviate, or Qdrant) function as an indexing and retrieval layer on top of another storage backend. They are not durable storage systems in the same category as object, block, or file storage. They are optimized for approximate nearest neighbor (ANN) search and low-latency retrieval, making them well-suited for inference workloads—but they are typically expensive.

The Key Insight: each storage type applies to a certain AI and ML workload

There's no single storage system that handles every AI workload well. The right choice depends on where you are in the pipeline—training, inference, or agent orchestration—and what constraints matter most: cost, speed, scale, or structure. Match each storage type to the workload it was built for.

Object Storage

Object storage is the natural fit for training workloads. Training datasets are massive, often spanning terabytes or petabytes of text, images, audio, or video. They're written once and read sequentially during training runs. Object storage handles this pattern efficiently: it scales horizontally without limits, costs a fraction of block or file storage per gigabyte, and offers durability guarantees (typically 11 nines, or 99.999999999% annual durability) that make it reliable for datasets you can't afford to lose. If you're storing raw training corpora, preprocessed datasets, model checkpoints, training artifacts, or feature store outputs, object storage should be your default.

Block Storage

Block storage is where you go when latency is the bottleneck. During inference, model weights must be loaded quickly, and any backing databases (e.g., for retrieval-augmented generation) must serve queries in milliseconds. Block storage delivers the IOPS and low-latency random access that model serving workloads demand. It is also the right choice for hosting high-performance vector databases or caching layers in the inference path. The cost-per-gigabyte is higher, but for components where speed directly affects user experience, the performance justifies the expense.

File Storage

File storage shines in AI agent architectures. When an agent needs to navigate, read, and process data across an organization, a hierarchical folder structure provides a natural interface. Agents can browse directories and read files without requiring custom API endpoints or tool definitions for every data source. This is especially powerful in enterprise environments where data is already organized in folders (finance reports in /finance/2025/Q1/, customer records in /customers/enterprise/, etc.). The agent can leverage its background understanding of how filesystems work to reliably find and process information. File storage also supports concurrent access from multiple agents or processes, making it practical for multi-agent or multi-tenant systems that need shared access to the same data.

Vector Storage

Vector databases are purpose-built for similarity search during inference. When a user asks a question and the system needs to find the most relevant documents or embeddings, a vector database returns results in milliseconds based on semantic similarity rather than exact keyword matches. This makes them essential for retrieval-augmented generation (RAG) pipelines, recommendation systems, and any workload where the query is conceptual rather than exact. However, they're an indexing and retrieval layer—not a primary data store. Source data still lives in object, block, or file storage; an ETL pipeline keeps the vector database index in sync for fast lookup. Plan accordingly: you'll need a pipeline that keeps the vector index in sync with your source data.

Best practices

No two organizations have the same data profile, so your storage architecture should fit your specific workloads rather than copy a reference architecture. Start by mapping out your AI pipeline end-to-end—from data ingestion and preprocessing through training, inference, and agent orchestration—and identify where each storage type provides the most value.

In most cases, the answer is a hybrid approach. A typical enterprise AI stack might use object storage for raw datasets and model artifacts, block storage for inference-critical databases, file storage for agent-accessible organizational data, and a vector database for semantic retrieval. These systems don't compete—they complement each other at different stages of the pipeline.

Caching is another lever that pays for itself quickly. At each stage of your pipeline, consider what data is accessed repeatedly and would benefit from a faster layer. Cache model weights on local SSDs during inference. Hold frequently retrieved embeddings in memory. Stage preprocessed training batches on faster storage before a training run begins. The pattern is consistent: use your cheapest storage as the system of record and cache aggressively on faster tiers where access patterns justify it.

Finally, cost optimization comes down to matching the right storage tier to the right workload. Training data accessed once a quarter doesn't belong on high-performance block storage; archive it to cold object storage. Conversely, don't try to serve real-time inference from a cheap object store just to save money. Audit your storage spend regularly and move data between hot, warm, and cold tiers as access patterns change.

Compliance

Compliance is another constraint that can affect your storage strategy. All storage types can meet compliance requirements, but some align more naturally with specific regulatory constraints.

Retention policies—where regulations require storing data for a fixed period without modification—map well to object storage. Features like S3 Object Lock or Azure Immutable Blob Storage let you enforce write-once-read-many (WORM) policies at the storage layer, simplifying compliance with regulations such as SEC Rule 17a-4, HIPAA, and GDPR data retention mandates, including right-to-erasure obligations.

Audit trails benefit from file and object storage systems that offer built-in versioning and access logging. When a regulator asks who accessed a specific dataset and when, you need storage that can answer that question without custom instrumentation. Most major cloud providers offer this natively. Enable it proactively, before an audit request arrives.

Data residency—the requirement that certain data must physically reside in specific geographic regions—is where your storage choice intersects with your cloud provider's regional availability. Object and file storage services are generally available in every major cloud region, giving you flexibility to keep EU patient data in Frankfurt or Canadian financial records in Montreal. Block storage is also regionally available, but its tighter coupling to compute instances can make multi-region architectures more complex to manage.

For example, a healthcare organization running an AI agent that processes patient records might store raw medical documents in regionally-locked object storage (satisfying data residency), expose them to agents through a file storage layer organized by facility and department (satisfying operational needs), and maintain immutable audit logs of every agent access event in a separate object store with retention locks (satisfying HIPAA audit trail requirements).

Closing Thought: Choose What's Best for You

It's boring advice, but that's the reality of storage: you need to choose the storage solution that makes the most sense for your company's needs. There's no universal architecture that works for every team.

When making the decision, run it through three filters:

1. First, workload type: are you primarily training models, serving inference, or running AI agents? Each workload has a storage type that fits it best. Forcing a single system to handle all three leads to overspending or underperformance.
2. Second, budget: understand the cost curve of each storage type at your expected scale. Object storage is cheap at the petabyte scale; block storage is not. Model your costs at both current and projected volumes. Third, regulatory requirements: if you're in a regulated industry, your compliance obligations will constrain which storage architectures are viable. Factor in data residency, retention mandates, and audit trail requirements from the start—retrofitting compliance into an existing architecture always costs more than designing for it upfront.

Start with the workload, match it to the storage type, validate against your budget and compliance needs, and build from there.