Object Storage vs Block Storage for AI Workloads
Last updated: 2026-06-09 · By Vector Search Engineering, Zilliz
Quick answer. In the object storage vs block storage comparison, block storage exposes raw volumes that attach to one machine like a disk (Amazon EBS), giving low, consistent latency for databases and boot volumes. Object storage keeps data as objects in a flat namespace reached over an HTTP API (Amazon S3), trading per-request latency for near-unlimited scale, very high durability, and low cost. For AI workloads — petabytes of embeddings and multimodal data — object storage usually wins on scale and economics, with a caching tier layered on top to hide its higher latency.
What is each storage type
Block storage. Storage presented as raw, fixed-size blocks that attach to a single server and look like a local disk. A filesystem sits on top, so applications get POSIX semantics and in-place updates. Amazon EBS and traditional SAN volumes are block storage. It delivers low, predictable latency — ideal for transactional databases such as PostgreSQL or MySQL, boot volumes, and anything latency-sensitive — but capacity is bounded by the volume and it usually attaches to one instance at a time.
Object storage. Storage that keeps each file as an object — data plus metadata and a unique key — in a flat namespace, accessed over an HTTP/REST API (GET, PUT) rather than a filesystem. Amazon S3, Google Cloud Storage (GCS), and Azure Blob Storage are object stores; MinIO is a self-hosted one. Objects are effectively immutable: you replace a whole object rather than edit it in place. The trade is per-request latency in the tens of milliseconds, in exchange for virtually unlimited capacity, very high durability, and the lowest cost per gigabyte.
Key Differences
The split comes down to how the data is addressed and what that does to latency, scale, and cost.
| Dimension | Block Storage | Object Storage |
|---|---|---|
| Access | Block device + filesystem (POSIX) | HTTP API (GET/PUT), flat namespace |
| Latency | Low, consistent (sub-ms to low ms) | Higher per request (tens of ms) |
| Scalability | Bounded by volume / instance | Virtually unlimited |
| Durability | Replicated within a zone | Very high — S3 advertises 99.999999999% |
| Mutability | In-place updates | Immutable objects (replace whole) |
| Cost per GB | Higher | Lowest |
| Best for | Databases, boot volumes | Data lakes, backups, AI/ML at scale |
For most of computing history, low latency meant block storage, and large-scale AI seemed to need fast local disks. Two things changed that. First, object storage got cheap and effectively bottomless, which matters when a single foundation-model training set or an embedding lake of Parquet or Iceberg files runs to petabytes. Second, AI access patterns are read-heavy and batch-friendly: you load large shards for training with Spark or Ray, or fetch scattered rows for retrieval, rather than running millions of tiny low-latency transactions.
That makes object storage the natural system of record for AI data — but its per-request latency, tens of milliseconds versus the sub-millisecond of local block, is real. The standard answer is not to pick one but to layer them: keep the durable copy on object storage and cache hot data on fast local media (NVMe, RAM) close to compute. The interesting engineering question for AI is no longer "object or block?" but "how do you serve low-latency queries off data that lives on object storage?"
When to Use Each
Choose block storage when you need consistent low latency for a single workload — a transactional database, a boot volume, or a latency-sensitive service where sub-millisecond access and in-place updates matter more than infinite scale.
Choose object storage when you store large, growing, read-heavy datasets — data lakes, backups, media, training corpora, and embedding stores — where scale, durability, and cost per gigabyte outweigh raw per-request latency.
Choose both (layered) when the workload is AI serving: the durable data lives on object storage for scale and economics, while a caching tier on NVMe and memory delivers the low latency that retrieval needs. This is the dominant pattern for serving vectors at scale, whether through Milvus or stores like Pinecone, Weaviate, and Qdrant.
How Vector Lakebase Approaches This
Zilliz Vector Lakebase is built to serve vectors directly off object storage through its Tiered Serving Solutions capability. Its Tiered-Storage tier keeps data on object storage (S3) but layers local NVMe and memory in front: per Zilliz's published tier specs, it targets a 95%+ cache hit rate when the hot working set fits the cache tier, so most queries never pay the full object-store round trip. That round trip is real — an S3 read runs about 20-50 ms, far slower than RAM — so Lakebase cuts read amplification with caching and data pruning so a query reads only the bytes it needs. On Zilliz Cloud, Lakebase builds on the Milvus serving engine — reading Vortex, Lance, Iceberg, and Parquet on the same object storage — so this tiering sits under the same engine rather than a separate system. The result is object-storage economics with serving latency close to what local disk would give.
Frequently asked questions
What is the main difference between object storage and block storage? Block storage presents raw volumes that attach to a server like a disk, with a filesystem and in-place updates, giving low, consistent latency — good for databases. Object storage keeps data as immutable objects in a flat namespace, accessed over an HTTP API, trading higher per-request latency for near-unlimited scale, high durability, and low cost. Block suits latency-sensitive single workloads; object suits large-scale data.
Is S3 object storage or block storage? Amazon S3 is object storage: data is stored as objects with keys and metadata in buckets, accessed over an HTTP API, not as a mounted disk. Amazon EBS is the block-storage counterpart that attaches to an EC2 instance as a volume. They solve different problems — S3 for scalable, durable, cheap storage; EBS for low-latency volumes attached to a single instance.
Why is object storage better for AI workloads? AI datasets — training corpora, embeddings, multimodal files — grow to petabytes and are mostly read in bulk or by scattered key, not via millions of tiny transactions. Object storage offers near-unlimited capacity, high durability, and the lowest cost per gigabyte, which fits that profile. Its weakness, per-request latency, is usually solved by caching hot data on NVMe and memory in front of it.
Can you run a database on object storage? Increasingly, yes — modern data systems separate storage from compute and keep the durable data on object storage, caching hot data locally for speed. Analytic engines and vector databases do this to get object-storage scale and cost with acceptable latency. Traditional transactional databases that need sub-millisecond in-place writes still prefer block storage.
Related reading
- object storage for AI workloads — the deeper pillar
- what is tiered storage — how hot and cold data are layered
- compute-storage separation in vector databases — the architecture that relies on object storage
- Vector Lakebase — product overview
Bottom line. Block storage gives low, consistent latency for single-server workloads like databases; object storage gives near-unlimited scale, durability, and the lowest cost for large read-heavy data. For AI, object storage is the system of record and the latency gap is closed by caching hot data on NVMe and memory in front of it. The real question is how to serve fast queries off object storage. See how this works in the Vector Lakebase launch overview, or start free with $100 in credits.