Vector Database Selection Guide: Interactive Comparison
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{name:'Pinecone',type:'Managed Cloud',cat:['managed','enterprise'],desc:'Fully managed, serverless vector database optimized for production RAG. Zero infrastructure overhead with excellent query performance at billion-scale.',metrics:[{k:'Max Vectors',v:'Billions'},{k:'P95 Latency',v:'40-80ms'},{k:'Free Tier',v:'100K vecs'},{k:'Price',v:'$$$'}],pros:['Zero ops','Excellent filtering','High availability'],cons:['Expensive at scale','Vendor lock-in'],tags:['Managed','Production','Serverless']},
{name:'Weaviate',type:'Open Source + Cloud',cat:['opensource','managed','enterprise','filtered'],desc:'Open-source vector database with hybrid search (BM25 + vector), multi-modal support, and a managed cloud option. Excellent for complex retrieval pipelines.',metrics:[{k:'Max Vectors',v:'Billions'},{k:'P95 Latency',v:'30-70ms'},{k:'Free Tier',v:'Sandbox'},{k:'Price',v:'$$'}],pros:['Hybrid search','Multi-modal','GraphQL API'],cons:['Complex setup','Heavy resource usage'],tags:['Open Source','Hybrid Search','Multi-modal']},
{name:'Qdrant',type:'Open Source + Cloud',cat:['opensource','managed','enterprise','filtered'],desc:'Rust-based vector database with powerful filtering capabilities. Benchmarks consistently show the best过滤 + search performance in the open-source space.',metrics:[{k:'Max Vectors',v:'Billions'},{k:'P95 Latency',v:'20-50ms'},{k:'Free Tier',v:'1GB Cloud'},{k:'Price',v:'$$'}],pros:['Rust performance','Rich filtering','Payload indexes'],cons:['Smaller community','Newer ecosystem'],tags:['Open Source','Fast','Filtering']},
{name:'Milvus',type:'Open Source + Cloud',cat:['opensource','managed','enterprise'],desc:'The most scalable open-source vector database, designed for billion-scale with GPU-accelerated indexing. Used by thousands of enterprises worldwide.',metrics:[{k:'Max Vectors',v:'Billions+'},{k:'P95 Latency',v:'20-60ms'},{k:'Free Tier',v:'Self-hosted'},{k:'Price',v:'$-$$$'}],pros:['Massive scale','GPU indexing','Mature ecosystem'],cons:['Complex infrastructure','Resource heavy'],tags:['Open Source','Scalable','Enterprise']},
{name:'Chroma',type:'Open Source',cat:['opensource'],desc:'The developer-favorite lightweight vector DB. Perfect for prototyping, local development, and small-scale production. Simple Python API.',metrics:[{k:'Max Vectors',v:'Millions'},{k:'P95 Latency',v:'10-30ms'},{k:'Free Tier',v:'Unlimited*'},{k:'Price',v:'Free'}],pros:['Dead simple','Great for dev','Python-native'],cons:['Not production-scale','No managed option','Limited HA'],tags:['Open Source','Dev/Lightweight']},
{name:'pgvector',type:'PostgreSQL Extension',cat:['managed','opensource','enterprise','filtered'],desc:'Vector search as a PostgreSQL extension. If you already run PostgreSQL, this is the fastest path to production vector search with zero new infrastructure.',metrics:[{k:'Max Vectors',v:'Billions'},{k:'P95 Latency',v:'30-100ms'},{k:'Free Tier',v:'Self-hosted'},{k:'Price',v:'Free*'}],pros:['No new infra','ACID transactions','Existing tooling'],cons:['Not purpose-built','Index build slower'],Tags:['PostgreSQL','Transactional','Easy']},
{name:'Redis (Vector)',type:'In-Memory Store',cat:['managed','enterprise','serverless'],desc:'Add vector search to your existing Redis infrastructure. Unbeatable for real-time applications where vector search meets caching and session management.',metrics:[{k:'Max Vectors',v:'Billions'},{k:'P95 Latency',v:'5-20ms'},{k:'Free Tier',v:'30MB Redis'},{k:'Price',v:'$$'}],pros:['Sub-ms latency','Existing infra','Real-time'],cons:['Memory costs','RSAL license'],Tags:['Fast','Real-time','Redis']},
{name:'LanceDB',type:'Embedded',cat:['opensource','serverless'],desc:'Embedded vector database built on Lance columnar format. Runs in-process — no server needed. Excellent for edge, desktop apps, and multi-modal data.',metrics:[{k:'Max Vectors',v:'Millions'},{k:'P95 Latency',v:'5-15ms'},{k:'Free Tier',v:'Unlimited'},{k:'Price',v:'Free'}],pros:['Embedded','Zero server','Multi-modal'],cons:['Not distributed','Smaller community'],Tags:['Embedded','Edge','Zero-ops']}
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🗄️ Vector Database Selection Guide
Interactive comparison of 8 vector databases. Filter by your use case and find the best fit for your RAG pipeline.
Quick Comparison Table
Database
License
Managed
Scale
Best For
Pinecone
Proprietary
✅
Billions
Production RAG, zero-ops
Weaviate
BSD-3 (Open)
✅
Billions
Hybrid search, multi-modal
Qdrant
Apache 2.0 (Open)
✅
Billions
Fast filtering, Rust perf
Milvus
Apache 2.0 (Open)
✅
Billions
Massive scale, GPU index
Chroma
Apache 2.0 (Open)
❌
Millions
Dev/testing, lightweight
pgvector
PostgreSQL License
✅*
Billions
Existing PostgreSQL users
Redis Vector
RSALv2 / SSPL
✅
Billions
Real-time, existing Redis
LanceDB
Apache 2.0 (Open)
❌
Millions
Embedded, edge, multi-modal
* pgvector runs on any PostgreSQL-compatible managed service (RDS, Supabase, Neon)
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Pros: ‚+db.pros.join(‚, ‚)+‘
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Cons: ‚+db.cons.join(‚, ‚)+‘
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