What teams build with ManyVector.
From RAG pipelines to real-time recommendations — production vector search on your own object store, without the in-memory cluster overhead.
Four workloads, one vector store.
RAG Pipelines
Retrieval-augmented generation
Store document embeddings in your own bucket and retrieve the most relevant chunks at query time. Ground LLM responses in private knowledge at object-storage prices.
10x cheaper
Semantic Search
Meaning-first document search
Find documents by meaning, not just keywords. Search across billions of documents with sub-10ms latency using dense vector retrieval.
1B+ docs
Recommendations
Nearest-neighbour item matching
Power product, content, and user recommendations with nearest-neighbour vector retrieval. Filter by availability, category, or any attribute in real time.
<10ms serving
AI Agents
Long-term agent memory
Give autonomous agents persistent memory, tool retrieval, and per-user context. Namespaced per session, stored in your own bucket, queryable in under 10ms.
Per-user NS
Production-proven across workloads.
10x
cheaper than in-memory
<15ms
RAG retrieval p50
1B+
vectors per namespace
Any
LLM or embedding model