Vector search at object-storage scale.
Approximate nearest-neighbour retrieval over dense embeddings using HNSW. Sub-10ms p50 latency with 90%+ recall@10, served directly from your S3, GCS, R2, or MinIO bucket.
HNSW on object storage — not RAM.
Algorithm
HNSW graph traversal
ManyVector builds and queries Hierarchical Navigable Small World graphs stored as objects. The graph structure lives in your bucket — no proprietary disk format.
Latency
Sub-10ms p50 on hot namespaces
Hot namespaces are memory-cached at the query layer. Cold namespaces are fetched from object storage on demand and warmed automatically on first access.
Recall
90%+ recall@10 by default
Default HNSW parameters are tuned for high recall. You can adjust efSearch and M parameters per namespace for workloads that need different recall/latency trade-offs.
Distance metrics
Cosine, dot product, Euclidean
Choose the distance metric that matches your embedding model. Cosine similarity for normalized embeddings, dot product for unnormalized, L2 for spatial workloads.
Dimensions
Any embedding dimension
From 384-dimensional MiniLM to 3072-dimensional text-embedding-3-large. Set the dimension once at namespace creation — ManyVector handles the rest.
Scale
Billions of vectors per namespace
No cluster resizing required. Object storage scales to exabytes — your namespace grows with your data without any operational intervention.
Performance built for production.
<10ms
p50 query latency
90%+
recall@10 default
100B+
vectors per namespace
3
distance metrics
Common questions
How does ManyVector achieve sub-10ms latency without keeping everything in RAM?
Hot namespaces are cached at the query layer. HNSW graph traversal runs against cached nodes, so most queries never touch object storage directly. Cold namespaces warm on first access.
What embedding dimensions are supported?
Any dimension from 64 to 4096+. Set it once at namespace creation — ManyVector handles storage and indexing automatically.
How does recall compare to fully in-memory databases?
With default parameters, ManyVector achieves 90%+ recall@10. Tune efSearch and M per namespace to trade recall for latency or vice versa.
Can I use different distance metrics across namespaces?
Yes. Distance metric is set per namespace. Run cosine namespaces alongside dot-product namespaces in the same account.