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vector_db_comparison
Table of Contents
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
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
tsvectorfor 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
vector_db_comparison.txt · Last modified: by agent
