Vector Database Memory Footprint Estimator

Size your vector database before provisioning by pairing embedding count, dimension, numeric precision, replication strategy, and optional index overhead to forecast RAM needs per replica and across the cluster.

Total embeddings stored in the collection or index (count each vector after any preprocessing).
Width of each vector (e.g., 768, 1,024, 1,536 for popular foundation models).
Use 2 for float16, 4 for float32, or adjust for custom quantization and product quantization byte sizes.
Total replicas required for availability across the cluster (enter 1 for no replication).
Defaults to 10%. Represents graph or IVF metadata overhead added on top of raw vectors.

Memory planning should be validated against actual index structures, compression, and query workloads. Confirm with your vendor or benchmark environment before committing infrastructure spend.

Examples

  • Example 1 — 5,000,000 vectors, 1,536 dimensions, 2 bytes per value, replication 3, 10% overhead ⇒ Per-replica memory requirement: 15.74 GB • Cluster memory footprint: 47.21 GB • Raw bytes per replica: 16,896,000,000.00 bytes
  • Example 2 — 1,200,000 vectors, 768 dimensions, 4 bytes per value, replication 2, 5% overhead ⇒ Per-replica memory requirement: 3.60 GB • Cluster memory footprint: 7.21 GB • Raw bytes per replica: 3,870,720,000.00 bytes

FAQ

How do I model sharded deployments?

Divide the vector count across shards and run the calculator for each shard, then sum the outputs to estimate the total cluster footprint.

Can I account for disk-backed indexes?

Set the overhead percentage to include additional metadata or page cache you keep in RAM for disk-first architectures.

What bytes-per-value setting should I choose?

Use 4 for float32 embeddings, 2 for float16, or enter the byte size of your quantized representation such as 1 byte for int8 product quantization.

Does the estimate include operating system headroom?

No. Add your preferred safety margin on top of the per-replica memory requirement when selecting node sizes.

How can I translate GiB results into cloud instance sizing?

Compare the per-replica GiB output with the advertised RAM of your target instance family and add 20–30% buffer for OS and service overhead before final selection.

Additional Information

  • Raw bytes multiply vector count, dimension, and bytes per value to show the baseline payload.
  • Index overhead percentage inflates RAM needs to cover graph edges, centroids, or other metadata structures.
  • Per-replica memory highlights how much RAM each node must reserve before caching or query buffers.
  • Cluster footprint multiplies per-replica usage by the replication factor to capture redundancy costs.
  • Results use gibibyte (GiB) conversion so you can compare against memory modules and cloud instance specs.
  • Consider adding headroom for query caches, write buffers, and background compaction threads beyond the displayed totals.

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