Block vs Object Storage Pricing in the Cloud: A Practical Breakdown
A practical comparison of block and object storage pricing in the cloud, covering cost structure, performance, and which fits common GPU workloads.
Storage choices quietly shape both the performance and the cost of GPU workloads. The two options you will reach for most often are block storage and object storage, and they are priced and behave very differently. Choosing the wrong one can mean paying for performance you do not need, or starving a workload that needed fast disk. This guide breaks down how each is priced, the performance tradeoffs, and which fits common patterns like training datasets, checkpoints, and model serving.
What block and object storage are
Block storage behaves like a disk attached to your instance. It is divided into fixed-size blocks, mounted as a volume, and accessed with low latency. Your operating system sees it as a drive you can format and write to directly. It is the natural home for the boot disk, databases, and any data that needs fast, frequent, random access.
Object storage keeps data as objects in a flat namespace, accessed over an API rather than mounted as a disk. Each object carries its data and metadata and is retrieved by a key. It is built for scale and durability rather than low-latency random access, which makes it ideal for large datasets, backups, and anything you read in bulk or stream.
How the pricing differs
The cost structures reflect the designs. Block storage prices for provisioned capacity and performance, while object storage prices for what you store and how you access it.
| Aspect | Block storage | Object storage |
|---|---|---|
| Capacity billing | Per gigabyte provisioned | Per gigabyte stored |
| Performance billing | Often pay for provisioned throughput or IOPS | Generally not provisioned |
| Request charges | Usually none | Per-request fees common |
| Retrieval and egress | Egress applies when data leaves | Retrieval and egress fees can apply |
| Idle cost | Pays for provisioned size even if unused | Pays only for data actually stored |
The key difference: block storage often charges for capacity you provision whether or not you fill it, plus sometimes for a performance tier. Object storage charges for the bytes you actually store but layers on per-request and retrieval costs. For large, infrequently accessed data, object storage is usually far cheaper per gigabyte. For hot, latency-sensitive data, block storage earns its higher cost.
Performance tradeoffs that affect cost
Price and performance are linked here, so you have to consider them together.
- Latency: block storage offers low, consistent latency for random reads and writes; object storage has higher per-request latency suited to bulk and streaming access.
- Throughput: object storage can deliver very high aggregate throughput when you read many objects in parallel, which suits feeding large datasets to training jobs.
- Concurrency: object storage scales to enormous numbers of concurrent readers; block volumes are typically attached to one instance at a time.
- Access pattern: frequent small random access favors block; large sequential or bulk access favors object.
Matching storage to GPU workloads
Most GPU workflows use both kinds of storage, each for the part of the job it suits.
Training datasets
Large training datasets usually live in object storage, where capacity is cheap and many workers can stream data in parallel. You read the data in bulk, which plays to object storage's strengths, and you avoid paying for huge provisioned block volumes.
Active working data and checkpoints
Data being read and written constantly during a job, including intermediate files and frequent checkpoints, benefits from block storage's low latency. A fast volume keeps the GPU fed and avoids stalls. Once a checkpoint is final, you can move it to cheaper object storage for long-term keeping.
Model serving
For inference, model weights are often loaded from object storage at startup and then held in memory, while any low-latency local state sits on block storage. This combination keeps storage cost low while preserving fast access where it matters.
A tiering strategy that saves money
The cheapest approach is rarely all of one type. A simple tiering plan captures most of the savings without hurting performance.
- Hot data on block. Keep only the data a running job actively touches on fast block volumes, sized to what you need rather than oversized for headroom.
- Bulk and cold data on object. Store large datasets, finished checkpoints, and archives in object storage where per-gigabyte cost is low.
- Move data down as it cools. Promote final outputs from block to object once the job is done, freeing expensive provisioned capacity.
- Mind the request and retrieval fees. For object storage, batch reads and avoid chatty access patterns that rack up per-request charges.
Avoiding common storage cost mistakes
A few habits prevent the usual waste. Do not leave large block volumes provisioned after a job ends, since they bill whether or not they are used. Do not store massive datasets on block storage when object storage would be a fraction of the cost. And do not ignore object storage's request and retrieval fees for access-heavy workloads, where they can add up even though the per-gigabyte storage rate is low.
Storage classes within object storage
Object storage is not a single price either. Most providers offer several storage classes that trade retrieval speed and cost for a lower price on rarely accessed data. A standard class keeps data instantly available at a higher per-gigabyte rate, while colder classes drop the storage price in exchange for slower retrieval and sometimes a retrieval fee. For GPU workflows this maps neatly onto data temperature: datasets you train on this month belong in a standard class, while finished checkpoints and archives you may never touch again belong in a colder, cheaper class.
| Data temperature | Sensible class | Tradeoff |
|---|---|---|
| Actively used datasets | Standard object | Higher storage cost, instant access |
| Occasionally reused | Infrequent access | Lower storage, small retrieval fee |
| Archive and compliance | Cold or archive | Cheapest storage, slow retrieval |
Throughput to the GPU matters too
One storage cost that is easy to miss is the indirect cost of a slow data path. If your storage cannot feed the GPU fast enough, the expensive accelerator sits waiting, and you pay for GPU time that produces nothing. For training that streams a large dataset, this means object storage read in parallel by many workers, or a fast local cache, can keep the GPU busy and lower your effective cost per result. The cheapest storage on paper is not a bargain if it stalls the GPU; balance the storage rate against the throughput your workload actually needs.
Block and object storage are not competitors so much as partners. Block gives you fast, low-latency disk for active data at a higher cost, while object gives you cheap, durable, massively scalable capacity for bulk and cold data. Put hot data on block, bulk data on object, use cold classes for archives, move things down as they cool, and watch object storage's request fees. Match each piece of your workload to the storage that fits, and you will get the performance you need without paying for capacity or speed you do not.