Vector search rethought from the storage layer up.
ManyVector is built by the team at ManyLayers — engineers who believe AI infrastructure should be sovereign, transparent, and priced honestly. We started with the conviction that object storage is already the right database primitive for vector workloads.
ManyVector is a product of ManyLayers.
ManyLayers builds sovereign AI infrastructure — starting with a unified LLM gateway and suite for enterprise teams. ManyVector is our purpose-built vector database layer, born from the same conviction: AI infrastructure should be transparent, self-hostable, and priced at cost.
The ManyLayers team brings deep experience in infrastructure reliability, distributed storage systems, and the operational realities of running AI at production scale.
Principles we won't compromise on.
Data sovereignty
Your vectors live in your bucket. ManyVector never stores your data on our disks — not during indexing, not during queries, not ever. You own the bytes.
Engineering depth
We implement the hard parts ourselves — HNSW indexing, BM25 ranking, RRF fusion, copy-on-write namespaces — and expose them through a simple, consistent API.
Object storage first
We built ManyVector on the premise that object storage is already the right database primitive. Infinite scale, 11 nines of durability, and fractions of a cent per GB.
Developer experience
No schema management. No cluster provisioning. No index tuning. Drop in the client, point it at your bucket, and start searching in minutes.
Engineering values that show up in the product.
Storage as the source of truth
Traditional vector databases pin data in RAM and charge you for idle capacity. ManyVector inverts this — object storage holds everything, hot data is cached at the query layer. You pay for queries, not for storage.
Simple over clever
A single consistent query API for vector, full-text, hybrid, and filtered search. One client, one namespace abstraction, one billing surface. No stitching together separate search clusters.
Forking as a first-class primitive
Copy-on-write namespace branching means you can snapshot a collection, test new embedding models, run evals, and roll back without copying a single vector. Version control for your vector database.
Infrastructure you can trust
We are not an AI company that happens to have a vector store. We are an infrastructure company. Reliability, predictable latency, and operational simplicity come before features.
Object storage is the source of truth. The query layer handles HNSW graph traversal, BM25 scoring, and RRF fusion. Hot namespaces are cached in memory; cold namespaces are fetched on demand. You pay for queries, not for idle RAM.
We'd love to hear from you.
Whether you're evaluating ManyVector for a production workload, exploring self-hosted deployment, or just want to talk vector search architecture — reach out.
Talk to the team about your use case, pricing, or VPC deployment options.