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Vector DB · pgvector community
Vector similarity search inside Postgres. The pragmatic default.
Postgres extension that adds a vector type plus exact and approximate nearest-neighbour search. Pairs naturally with Supabase, Neon, and any managed Postgres. The lowest-friction RAG backend if you already run Postgres.
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Related in Vector DB
Pinecone
Managed vector database. The industry-default serverless option.
Fully-managed vector DB built for production RAG and semantic search at scale. Serverless pricing, low-latency reads, integrations across every framework. Most Blokz-adjacent AI teams reach for it first.
AI insight: Fully managed with no self-host option — the trade-off for the serverless pricing it popularized in the vector-DB space.
Vespa.ai
Open-source serving engine for vector, lexical, and structured search at scale.
A big-data serving engine that combines approximate nearest-neighbor vector search, lexical search, structured filtering, and ML model inference in a single query, evaluated over data distributed across many nodes. Battle-tested at Yahoo scale, it is offered as a free Apache-2.0 engine you self-host, or as the managed Vespa Cloud — including an Enclave mode that runs inside your own AWS or GCP account.
AI insight: Powered Yahoo's search and ads for ~20 years before spinning out as an independent Apache-2.0 company in October 2023.
LanceDB
Embedded multimodal vector database on the Lance format.
An open-source retrieval engine for AI built on the Lance columnar format. It runs in-process alongside your app — no separate server — and stores, indexes, and searches vectors, metadata, and multimodal data (text, images, video) with vector, full-text, and SQL queries. A managed enterprise lakehouse tier scales the same engine to petabytes.
AI insight: Runs embedded in-process on its own Lance columnar format — there's no server to operate, unlike Pinecone or Weaviate.
Zilliz
Distributed open-source vector DB built for billion-scale.
Cloud-native, Apache-2.0 vector database for similarity search at scale, powering RAG, semantic and multimodal search, and recommendations. Its distributed architecture separates storage and compute and supports many index types (HNSW, IVF, FLAT, DiskANN, SCANN) with quantization and mmap. Created by Zilliz, which offers the managed Zilliz Cloud.
AI insight: A graduated LF AI & Data project that separates storage from compute and ships DiskANN, so a single cluster scales to billions of vectors.
Chroma
Embedded vector DB. Pip-install, prototype, scale later.
The low-friction starting point — Chroma runs embedded inside your Python process or as a hosted service. Great for prototypes and small-to-medium RAG apps; upgrade to a managed option when you outgrow it.
AI insight: Runs embedded inside your Python process — the lowest-friction way to prototype RAG before you need a server at all.
Qdrant
Open-source, Rust-based vector DB. Fast, predictable, self-hostable.
Vector database written in Rust with a strong focus on filtering, payloads, and predictable latency at scale. Self-host on a single binary or use the managed cloud.
AI insight: Written in Rust and ships as a single self-hostable binary — its payload filtering is why teams pick it for metadata-heavy search.
Turbopuffer
Object-storage-backed vector DB. Serverless economics at scale.
Bills like S3 — cold rest, warm reads, no per-namespace minimums. Designed for very-large, mostly-cold vector workloads where you can't justify keeping every index in RAM. Operated by Notion in production.
AI insight: Stores indexes on object storage instead of RAM, so cost tracks usage not corpus size — Notion runs it in production.
Weaviate
Open-source vector database with built-in vectorisers.
Cloud-native vector DB that can compute embeddings inline — pass raw text in, store vectors out. Strong hybrid (BM25 + vector) search; self-hostable or managed.
AI insight: Embeds text inline so you can skip a separate vectorizer step, and does hybrid BM25 + vector search out of the box.