Amazon S3 Vectors

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.

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.

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.