Tools / Memory planning
Vector RAM Calculator
Estimate vector memory for Flat, IVF, HNSW, and PQ-style compression before the database-specific overhead starts hiding the first-principles budget.
Capacity planning tool
Estimate Vector Memory
Size the vector payload first, then add simplified index overhead and operating headroom.
This estimator is for first-principles planning. Production memory also depends on metadata, filters, allocator overhead, compaction, replication, sharding, and database implementation.
How the Estimate Works
The base payload is vectors x dimensions x bytesPerDimension. Float32
uses four bytes per dimension. Float16 uses two. Int8 uses one. Product
Quantization switches to a simplified 96 bytes/vector preset so the
calculator can model compression without pretending to know your codebook layout.
After payload sizing, the calculator applies an index multiplier: Flat is
x1.00, IVF is x1.05, and HNSW is x1.25. It
then adds 30% headroom for operating system pressure, allocator behavior, and
ordinary capacity margin.
Complexity Table
| Choice | Memory Shape | Latency Shape | Planning Risk |
|---|---|---|---|
| Flat Float32 | Highest payload memory | Predictable scan cost | RAM and bandwidth saturation |
| IVF | Payload plus small routing overhead | Lower scan count when tuned | Under-probing misses boundary candidates |
| HNSW | Payload plus graph overhead | Low latency with enough RAM | Graph edges and filters exceed the clean estimate |
| Product Quantization | Compact vector payload | Fast approximate distance pass | Codebooks, IDs, and reranking add back cost |
When to Use This
Use this calculator when you are deciding whether a dataset is in the range of one machine, a larger memory tier, or a sharded design. It is useful before vendor selection because it keeps the first budget visible: vector count, dimension count, precision, and index shape.
It is also useful when comparing embedding models. Moving from 768 to 1536 dimensions doubles the vector payload before HNSW graph links, filters, replicas, or compaction headroom enter the discussion.
When Not to Use This
Do not use this as a database-specific sizing guarantee. Managed vector databases, Milvus, Qdrant, Elasticsearch, PostgreSQL extensions, and custom HNSW libraries all have different storage layouts, metadata structures, deletion behavior, and replication models.
Do not use it as a latency estimator. RAM pressure influences latency, but query time also depends on filters, cache locality, shard fan-out, candidate count, reranking, hardware, and concurrency.
Production Failure Modes
The common failure is sizing only raw vectors and forgetting the rest of the system. IDs, metadata payloads, filter indexes, tombstones, graph edges, centroids, codebooks, replicas, shard coordinators, and rebuild buffers all consume memory.
Another failure is treating compression as free. PQ can lower payload memory, but it adds approximation error. Strict recall may require oversampling and reranking with original vectors, which can bring memory and latency pressure back through another path.
FAQ
Does the calculator include metadata and filter indexes?
No. It estimates vector payload, simplified index overhead, and 30% headroom. Metadata, filters, IDs, allocator overhead, replication, sharding, and implementation-specific storage can add more memory.
Why does HNSW have a larger multiplier than IVF?
HNSW stores graph connectivity and traversal structures in memory. This simplified model uses a larger multiplier to represent that additional RAM pressure.
Is the Product Quantization estimate exact?
No. PQ uses a simplified 96-byte/vector educational preset. Real systems also store codebooks, IDs, metadata, and index structures.
Can this estimate be used for cloud billing?
No. It is a first-principles planning estimate, not a billing guarantee or replacement for database-specific load testing.