Vector DB AI apps

Native vector storage and querying in S3 — serverless, billion-vector scale.

Purpose-built vector storage inside Amazon S3: store and query up to two billion vectors per index across thousands of indexes per vector bucket, with S3's durability and elasticity and no clusters to manage. Frequent queries return in around 100ms and infrequent ones in under a second, and it plugs directly into Amazon Bedrock Knowledge Bases for RAG. You pay only for storage and queries.

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.

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.

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.

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.

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.

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.

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.

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.

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.