Vector Database Comparison

The dominant fully-managed option. Serverless tier (index scales to zero when idle) and pod-based tier (dedicated resources, predictable latency). No infrastructure to manage.

Best for: startups and teams that want to ship fast without ops overhead. Cost scales with namespace; can get expensive at high query volume.

Written in Rust for high performance. Supports payload filters alongside vector search (pre-filtering), sparse vectors for hybrid search, and named vectors per document for multi-vector indexing. Self-host or use Qdrant Cloud.

Best for: teams with DevOps capacity who want control over performance and cost. Strong filtering makes it good for metadata-heavy corpora.

Python-native, embeds directly in your application process (no separate server for local use). Designed for developer experience - minimal boilerplate, in-memory or persistent modes, tight LangChain/LlamaIndex integration.

Best for: local development, prototyping, and small datasets (<100K documents). Not production-grade at scale without Chroma Cloud.

GraphQL API, native BM25 + vector hybrid search, auto-vectorisation (integrates directly with OpenAI/Cohere/Hugging Face embedding APIs so you never store raw text and embeddings separately). Multi-tenancy built in.

Best for: applications that need hybrid search out-of-the-box without a separate BM25 index, and multi-tenant SaaS.

Enterprise-grade, designed for billion-scale vector collections. Distributed architecture with separate compute and storage. Multiple index types (HNSW, IVF, DiskANN) with configurable speed/accuracy tradeoffs.

Best for: large-scale enterprise deployments with hundreds of millions of vectors and experienced MLOps teams.

Adds vector similarity search to PostgreSQL. If you already run Postgres, pgvector lets you store embeddings alongside relational data and query both in a single SQL statement. Performance is limited vs. dedicated vector DBs but sufficient for <1M vectors.

Best for: small-to-medium apps already on Postgres that want to avoid a second database. Zero new infrastructure.