Search every
vector.
Vector and full-text search built on S3, GCS, R2, and MinIO — fast, 10× cheaper than traditional vector databases, and infinitely scalable.
p50 latency
<10ms
Cost reduction
10×
Vectors / namespace
100B+
Recall@10
90%+
Your bucket — your data — no egress to us
S3, GCS, R2, MinIO — already yours.
Every search primitive. One API.
Vector Search
Approximate nearest-neighbour over dense embeddings using HNSW. Sub-10ms p50 latency with 90%+ recall — served directly from your object store.
Learn more →Full-text Search
BM25 tokenised full-text search across any stored attribute. The same namespace, the same API — no separate search cluster to operate.
Learn more →Hybrid Search
RRF fusion of vector and full-text scores, combined with structured metadata filters. Precision and recall in a single query.
Learn more →Built for the most demanding search workloads.
Speed
Sub-10ms p50, at any scale
Hot namespaces are served from memory; cold namespaces are fetched from object storage on demand. You pay for queries, not for idle capacity.
Cost
10× cheaper than in-memory DBs
Object storage costs fractions of a cent per GB. No pinned RAM for cold data. No replication at the database layer — your object store handles that.
Scale
Billions of vectors per namespace
No cluster resizing, no shard rebalancing. Storage scales with your object store — which was already built to handle exabytes.
<10ms
p50 latency
10×
cost reduction
100B+
vectors / namespace
90%+
recall@10
What teams build with ManyVector.
RAG Pipelines
Store embeddings and retrieve semantically relevant chunks to ground LLM responses in your private knowledge.
Learn more →Semantic Search
Find meaning, not just keywords. Search across millions of documents with sub-10ms latency.
Learn more →Recommendations
Nearest-neighbour retrieval across product, content, and user embedding spaces at production scale.
Learn more →AI Agents
Long-term vector memory and tool retrieval for autonomous agents — namespaced per session or user.
Learn more →Upsert, query, done.
No schema management. No index tuning. No cluster to provision. Drop in the client, point it at your bucket, and start searching.
<10ms
p50 query latency
10×
cost reduction vs in-memory
100B+
vectors per namespace
90%+
recall@10 default
Rethinking the vector database from the storage layer up.
Traditional vector databases pin all data in RAM. ManyVector inverts that — object storage is the source of truth, hot data is cached at the query layer.
Object store as the source of truth.
ManyVector stores all data in your own S3, GCS, R2, or MinIO bucket. No separate disk tier, no replication overhead — the object store IS the database. You own the bytes.
Fork a namespace in milliseconds.
Copy-on-write namespace branching lets you snapshot a collection, test new embedding models, run evals, and roll back — without duplicating a single vector on disk.
One API. Every search primitive.
Vector, full-text, hybrid, and metadata filter — all in a single consistent query interface. RRF fusion combines vector and BM25 scores. No schema management, no index tuning.
“We moved from Pinecone to ManyVector and cut our vector storage costs by 10×. The object store architecture means we pay for what we actually query — not for idle RAM.”
Platform Lead — AI-first startup
Free tier
100M vectors free
Start building with no credit card. Bring your own S3 bucket and pay only for queries above the free tier.
Sign up freePay as you go
Per query, not per GB
ManyVector charges per query — not for storage. Your data lives in your bucket at object-store prices. No idle RAM cost.
See pricingSelf-hosted
Deploy in your VPC
Run ManyVector entirely within your own infrastructure. Air-gapped operation supported. No internet required after setup.
Talk to usNo per-GB markup — you pay your storage provider directly using your own bucket credentials.