Vector Database Comparison

A practical guide to choosing the right vector database for AI agents, RAG systems, and semantic search. Current as of Q1 2026.

Vector Database Comparison Table

Database	Language	Stars	Hosted Option	Max Vectors	Filtering	Hybrid Search	Pricing
FAISS	C++ (Python bindings)	33k	No (library only)	Unlimited (RAM-bound)	Post-filtering only	No	Free / open-source
Milvus	C++ / Go	35k	Yes (Zilliz Cloud)	Billions (distributed)	Yes (scalar pre-filter)	Yes (dense + sparse)	Free OSS; Zilliz from $0 free tier
Qdrant	Rust	25k	Yes (Qdrant Cloud)	Billions (on-disk + distributed)	Yes (advanced payload)	Yes (dense + sparse + BM25)	Free OSS; Cloud from $25/mo
ChromaDB	Python	18k	Yes (Chroma Cloud beta)	Millions (RAM-limited)	Yes (metadata)	Partial	Free OSS; Cloud in beta
Weaviate	Go	20k	Yes (Weaviate Cloud)	Billions (cloud-native)	Yes (GraphQL filters)	Yes (BM25 + vector)	Free OSS; Cloud free tier available
Pinecone	Proprietary	N/A (closed)	Yes (fully managed)	Billions (serverless)	Yes (metadata)	Yes (sparse-dense)	Pay-as-you-go; free tier 1M vectors
pgvector	C (Postgres ext)	14k	Via Postgres hosts	Billions (with partitioning)	Yes (full SQL)	Partial (pair with tsvector)	Free OSS; hosted via Supabase/Neon etc

When to Use Which

graph TD A["Choosing a
Vector Database"] --> B{"What stage
are you at?"} B -->|"Prototyping /
Local dev"| C{"Need persistence?"} C -->|No| C1["FAISS"] C -->|Yes| C2["ChromaDB"] B -->|"Production /
Scale"| D{"Self-host or
managed?"} D -->|"Self-hosted"| E{"Primary
requirement?"} E -->|"Performance"| E1["Qdrant"] E -->|"Scale + ecosystem"| E2["Milvus"] E -->|"GraphQL API"| E3["Weaviate"] D -->|"Fully managed"| F{"Budget?"} F -->|"Minimize ops"| F1["Pinecone"] F -->|"Cost-conscious"| F2["Qdrant Cloud or
Weaviate Cloud"] B -->|"Already using
Postgres"| G["pgvector"] style A fill:#4a90d9,color:#fff style C1 fill:#2ecc71,color:#fff style C2 fill:#2ecc71,color:#fff style E1 fill:#e67e22,color:#fff style E2 fill:#e67e22,color:#fff style E3 fill:#e67e22,color:#fff style F1 fill:#9b59b6,color:#fff style F2 fill:#9b59b6,color:#fff style G fill:#e74c3c,color:#fff

Detailed Guidance

Prototyping & Local Development

FAISS – Best for experimentation. Facebook's library gives you raw speed with zero infrastructure. No server needed, just pip install faiss-cpu. Downside: no persistence, no filtering, no API.
ChromaDB – Best for quick RAG prototypes. Embedded mode with persistence. Simple Python API. Not built for production scale.

Production Self-Hosted

Qdrant – Written in Rust for performance. Excellent payload filtering. On-disk storage means you can handle datasets larger than RAM. Strong hybrid search with BM25.
Milvus – Battle-tested at billion-scale. Horizontal scaling via distributed architecture. Zilliz Cloud for managed option. Most mature open-source vector DB.
Weaviate – Go-based with a rich GraphQL API. Built-in vectorization modules (no need to pre-compute embeddings). Excellent hybrid search.

Fully Managed

Pinecone – Zero ops. Serverless scaling. Best if you want to focus purely on your application logic. Trade-off: vendor lock-in, no self-host option.

Already Using Postgres

pgvector – Add vector search to your existing Postgres. No new infrastructure. Full SQL filtering. Best when vectors are a feature, not the whole product. Combine with tsvector for hybrid search.

Performance Characteristics

Database	Indexing	Query Latency	Memory Efficiency	Disk-Based
FAISS	HNSW, IVF, PQ	Sub-ms (in-memory)	Moderate	No
Milvus	HNSW, IVF, DiskANN	Low ms	Good (segment-based)	Yes
Qdrant	HNSW	Low ms	Excellent (Rust)	Yes
ChromaDB	HNSW	Low ms	Moderate	Limited
Weaviate	HNSW	Low ms	Good	Yes
Pinecone	Proprietary	Low ms	N/A (managed)	N/A
pgvector	HNSW, IVFFlat	Moderate ms	Depends on Postgres	Yes

Last updated: March 2026

Table of Contents

Vector Database Comparison