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Object, File, and Block Storage: What's the Difference

April Wong

June 23, 20266 min read

Object, File, and Block Storage: What's the Difference

TL;DR

Three storage types, one rule: match the type to how your data is accessed.

File (shared drive, NFS/SMB): many machines read the same files at once, like a GPU cluster on a training set. Intuitive, but deep folders slow it down at scale.
Block (raw chunks, one machine): lowest latency, highest IOPS. Where databases and live data run. Fast, but pricey and hard to share, so not for bulk.
Object (S3 bucket, over an API): cheapest, near-infinite scale. Backups, data lakes, model weights. High latency and immutable, so never a live database.

At SuperAI this year, storage was everywhere. Object storage vendors, high-performance file storage players, even diamond sponsors staking out the space. For an AI conference, that says something: storage isn't an afterthought anymore. Teams are gearing up, and the demand is running right through the stack.

What stood out in the conversations was the confusion. We spoke with companies running block storage who called it "S3." The labels have blurred to the point where "storage" just means "the place data goes," and the distinctions that decide latency and cost get lost.

So let's clear it up: three types of storage, how each is built, how people use it, and which one to reach for when you're running agents.

File storage

How it's structured. File storage organizes data into a hierarchy of directories and files, the familiar filing-cabinet model, accessed over a network protocol like NFS or SMB. The filesystem handles structure, permissions, and locking, and many machines can mount the same share at once. It's intuitive for humans and native to Windows and Linux, which is why so many applications expect it.

How people use it. Folders, paths, shared drives. Teams reach for it when many machines need concurrent access to the same files: shared application data, content repositories, home directories, and lift-and-shift apps that expect a POSIX filesystem.

In the wild. AI training is the headline example. A GPU cluster needs every node to read the same multi-terabyte dataset at once, so teams put it on a high-throughput parallel file system and mount it across the fleet. Same shape outside AI: a render farm pulling shared assets, a genomics pipeline over shared reference data, an enterprise team on a NetApp share.

The bottleneck. The hierarchy that makes file storage intuitive becomes the drag. At extreme scale, deep directory paths add overhead and traversing them slows down, while the network protocol plus permission and locking logic sits between every client and the data. Under heavy concurrent load the filesystem itself, not the disk, becomes the ceiling.

Block storage

How it's structured. Block storage splits data into fixed-size blocks, each tagged with a unique ID, and presents them to the operating system as a raw volume. There's no concept of files and no metadata layer. The OS, a database, or a filesystem on top decides how to assemble the blocks. Because there are no folders to traverse and the access path is so short, block storage delivers the lowest latency and the highest IOPS of the three models.

How people use it. You attach a block volume to a single machine and treat it like a local disk: the boot disk under your VM, the data volume under your database. When a team runs Postgres, ClickHouse, Elasticsearch, or MongoDB, the hot data sits here, because the workload is thousands of small random reads and writes where latency and sustained throughput decide whether the system keeps up.

In the wild. An AWS EBS volume under an RDS database. A Solana or Ethereum archive node, where chain state is hundreds of gigabytes of constantly-updated data and the node falls behind if storage can't keep pace. A trading firm's order-matching database, where a millisecond of storage latency is a millisecond of slippage.

The bottleneck. Block is fast but it doesn't fan out. A volume binds to one instance, so sharing means adding a network layer that erases the latency advantage. And the premium price per gigabyte means using it as a cheap bulk store gets expensive fast.

Object storage

How it's structured. Object storage keeps data as discrete objects in a flat namespace, no directory tree, each bundled with rich metadata and a unique ID, accessed strictly over the network via HTTP and RESTful APIs (the S3 API is the de facto standard). That flat design is what lets it scale to petabytes and exabytes cheaply, and the API access is what makes it reachable globally and easy to wire into cloud-native apps and distributed AI pipelines.

How people use it. This is the S3 bucket. Teams use it to store a lot of data they read whole and infrequently: backups and archives, media and large static assets, data lakes, logs, model weights, and training datasets. You don't run a transactional database on it, but it's the system of record for AI datasets and unstructured media, where the cold and the bulk live, durably and cheaply.

In the wild. Netflix streaming its catalog from S3. A Hugging Face dataset or model checkpoints parked in a bucket until training pulls them. Your nightly database backups. The raw event logs feeding a data lake queried in batch, not in real time.

The bottleneck. Latency and immutability. Every request is a network round trip over HTTP, so baseline latency is high, especially on sequential reads, and objects can't be edited in place, so a one-line change rewrites the whole object. That rules it out for anything transactional or latency-sensitive, no matter how cheap the per-gigabyte price looks.

How to choose

The decision comes down to access pattern, not preference. Get it wrong and the failure is immediate: put a high-speed relational database on object storage and the latency alone will sink the app on day one.

Many small random reads and writes that care about latency? You want block (databases, real-time analytics, indexing). Many machines sharing the same files? You want file. Large volumes you read whole and infrequently, where cost-per-gigabyte rules? You want object.

In practice the answer is rarely one type. The strongest architectures are tiered: object for the data lake, backups, and the AI dataset of record; file for shared application data; block for the databases and latency-sensitive systems doing the real work. You move data across tiers as it cools, paying block prices only for what's hot. The mistakes to avoid are the two ends of that trade: running a high-IOPS database on object storage to save money, or paying block prices to archive cold data.

Where Nirvana fits

Nirvana is built around high-performance block storage, because the workloads we serve, blockchain, AI, and databases, are dominated by exactly the access pattern block storage wins at: thousands of small random reads and writes at high queue depth, where sustained IOPS and sub-millisecond latency decide whether the system keeps up.

Accelerated Block Storage (ABS) delivers sustained 20,000 baseline IOPS (up to 600,000 burst), sub-millisecond latency, and io2-class performance at gp3-class pricing, with no burst-credit falloff and flat per-TB billing. That's the layer under a vector database, a ClickHouse cluster, an archive node, or a fleet of concurrent agents writing memory and context to disk.