Infrastructure & Cloud. Vector DB
Managed Vector Store Infrastructure for AI Applications
Store and query over a billion vectors with sub-50ms latency, automatic index optimisation, and multi-tenancy built in, fully managed, no ops required.
Overview
What is a managed vector database?
Vector databases are the memory backbone of RAG systems, semantic search, and recommendation engines. They store high-dimensional embeddings and retrieve the most semantically similar ones in milliseconds. Managed vector infrastructure removes the complexity of provisioning, indexing, sharding, and scaling vector stores, giving AI engineers a simple API to store and query billions of embeddings at production latency.
What's included
HNSW indexing
Hierarchical Navigable Small World indexes deliver sub-50ms approximate nearest-neighbour queries across billion-scale vector collections.
Metadata filtering
Combine vector similarity search with structured metadata filters to scope queries to specific tenants, date ranges, or document types.
Multi-tenancy
Namespace-level isolation allows multiple teams or customers to share the same cluster with full data separation and independent quotas.
Automatic reindexing
As you upsert vectors, the index is updated incrementally in the background. No manual reindex jobs or query downtime.
Embedding model agnostic
Store vectors from any embedding model. OpenAI, Cohere, custom models, and switch embedding models without migrating data.
Hybrid search
Combine dense vector search with BM25 keyword search in a single query for workloads that benefit from both semantic and lexical matching.
How it works
From setup to production
Create
Create a vector collection by specifying dimensions and distance metric. The managed cluster provisions in under 30 seconds.
Upsert
Push vectors and associated metadata via the REST API or SDK. Batch upserts support millions of vectors per minute.
Query
Query with an embedding vector and optional metadata filter. Results return in under 50ms with similarity scores.
Scale
The cluster scales automatically as data volume and query rate grow. No resharding, no downtime, no manual intervention.
FAQ
Common questions
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