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Essential for RAG (Retrieval Augmented Generation), semantic search, and AI applications that store and query embeddings.

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Production-Ready Vector Databases

  • Pinecone
  • Qdrant
  • Weaviate
  • Chroma

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Pinecone

Fully managed vector databaseLightning-fast queries (<50ms), serverless scaling, enterprise-grade security.Best for: Production AI apps, enterprise scale, managed infrastructureKey Features:
  • Sub-50ms query latency
  • Automatic scaling (serverless)
  • SOC 2 Type II compliant
  • Multi-region deployment
  • Pinecone Assistant (all-in-one RAG)
  • Hybrid search support
Performance:
  • Handles billions of vectors
  • Consistent low latency
  • 99.9% uptime SLA
Pricing:
  • Free: 1 index, 5M vectors
  • Serverless: Pay per usage
  • Pod-based: Starting at $70/month
Use Cases:
  • Production RAG systems
  • Semantic search at scale
  • Recommendation engines
  • Enterprise AI applications
Documentation • Get Started

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PostgreSQL-Based Solutions

pgvector

PostgreSQL extension for vectorsStore embeddings directly in Postgres. Use with Supabase, Neon, or any Postgres.Best for: Existing Postgres users, unified database, cost savingsFeatures:
  • Native PostgreSQL extension
  • SQL queries for vectors
  • ACID guarantees
  • Works with existing tools
  • RLS support (Supabase)
Performance:
  • ~80ms query latency
  • Millions of vectors
  • L2, inner product, cosine distance
  • HNSW & IVFFlat indexes
Pricing: Free (part of PostgreSQL)Documentation

Supabase Vector

pgvector + Supabasepgvector with Supabase’s managed infrastructure, auth, and RLS.Best for: Supabase users, integrated solution, row-level securityFeatures:
  • pgvector extension
  • Row-Level Security
  • Supabase Auth integration
  • Edge Functions access
  • Auto-generated APIs
Pricing: Included with SupabaseDocumentation

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Enterprise & Specialized

Milvus

Open-source vector databaseBillion-scale vectors, high performance, cloud-native. Enterprise-grade.Best for: Billion-scale data, data engineering teams, enterprise AIFeatures:
  • Billion+ vector support
  • High throughput
  • GPU acceleration
  • Cloud-native architecture
  • Multiple index types
Performance:
  • ~60ms query latency
  • Handles billions of vectors
  • Horizontal scaling
  • GPU support
Pricing:
  • Free: Open-source
  • Zilliz Cloud: Managed service
Documentation

Vespa

Big data serving engineReal-time computation over large datasets. Vector search + full-text + structured data.Best for: Complex queries, real-time ML, large-scale systemsFeatures:
  • Hybrid search
  • Real-time updates
  • Machine learning inference
  • Combines vectors + text + structured
Pricing: Free & open-sourceDocumentation

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Performance Comparison

DatabaseLatencyScaleMemoryBest For
Pinecone<50msBillionsEfficientProduction, managed
Qdrant~60msBillionsVery efficientCost-sensitive, edge
Weaviate~70msBillionsEfficientHybrid search
Chroma~100msMillionsLightPrototypes, small apps
pgvector~80msMillionsPostgresExisting Postgres users
Milvus~60msBillions+HighEnterprise, billion-scale

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Feature Comparison

FeaturePineconeQdrantWeaviateChromapgvector
Managed Service✅ Yes✅ Yes✅ Yes❌ NoVia providers
Open Source❌ No✅ Yes✅ Yes✅ Yes✅ Yes
Self-Host❌ No✅ Yes✅ Yes✅ Yes✅ Yes
Hybrid Search✅ Yes✅ Yes✅ Yes❌ No⚠️ Limited
Free Tier✅ Yes✅ Yes✅ Yes✅ Yes✅ Yes
Filtering✅ Good✅ Excellent✅ Good⚠️ Basic✅ SQL
GraphQL❌ No❌ No✅ Yes❌ No❌ No

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Pricing Comparison

Free Tiers:
  • Pinecone: 1 index, 5M vectors
  • Qdrant: 1GB cluster free
  • Weaviate: Sandbox environment
  • Chroma: Unlimited (self-hosted)
  • pgvector: Free (with Postgres)
Paid Plans:
  • Pinecone: Serverless (~0.10/1Mqueries)orPod(0.10/1M queries) or Pod (70+/month)
  • Qdrant: $20+/month for cloud
  • Weaviate: $25+/month standard
  • pgvector: Postgres hosting costs only

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Integration Examples

  • Pinecone + OpenAI
  • Qdrant + LangChain
  • pgvector + Supabase
  • Chroma + LangChain
from pinecone import Pinecone
from openai import OpenAI

# Initialize
pc = Pinecone(api_key="YOUR_KEY")
openai = OpenAI()
index = pc.Index("your-index")

# Create embedding
embedding = openai.embeddings.create(
    model="text-embedding-3-small",
    input="Your text here"
).data[0].embedding

# Upsert to Pinecone
index.upsert([("id1", embedding, {"text": "Your text"})])

# Query
results = index.query(vector=embedding, top_k=5)

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Decision Guide

1

Assess Your Scale

Millions of vectors? → Chroma, pgvectorHundreds of millions? → Pinecone, Qdrant, WeaviateBillions of vectors? → Pinecone, Milvus, Qdrant
2

Consider Infrastructure

Want fully managed? → PineconeOpen-source + managed option? → Qdrant, WeaviateAlready using Postgres? → pgvectorPrototyping? → Chroma
3

Evaluate Budget

Minimal budget? → Chroma (free), pgvector (free)Cost-conscious? → Qdrant (most affordable managed)Production budget? → Pinecone, WeaviateEnterprise? → Pinecone, Milvus
4

Check Requirements

Need hybrid search? → Weaviate, QdrantRequire SOC 2 compliance? → Pinecone, WeaviateWant GraphQL? → WeaviateNeed edge deployment? → QdrantExisting SQL skills? → pgvector

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Use Case Recommendations

Small-scale (<1M docs): Chroma or pgvectorMedium-scale (1M-10M docs): Qdrant or WeaviateLarge-scale (10M+ docs): Pinecone or MilvusBudget-conscious: pgvector (Supabase) or Qdrant
User recommendations: Pinecone or QdrantProduct recommendations: Weaviate (hybrid)Content recommendations: Any (based on scale)
Simple chatbot: ChromaProduction chatbot: Pinecone or QdrantEnterprise assistant: Pinecone or WeaviateEdge chatbot: Qdrant

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Common Patterns

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RAG Pipeline

  1. Document Chunking → Split docs into chunks
  2. Generate Embeddings → OpenAI, Cohere, etc.
  3. Store in Vector DB → Pinecone, Qdrant, etc.
  4. Query → Semantic search for relevant chunks
  5. LLM Generation → Use chunks as context
  1. Vector Search → Semantic similarity
  2. Keyword Search → BM25 or full-text
  3. Combine Results → Reciprocal rank fusion
  4. Return Top K → Best of both worlds
  1. Image Embeddings → CLIP, etc.
  2. Text Embeddings → OpenAI, etc.
  3. Store Both → Same vector space
  4. Cross-Modal Search → Image → Text or vice versa

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Migration Guide

  • Chroma → Pinecone
  • pgvector → Qdrant
When: Scaling to productionSteps:
  1. Export embeddings from Chroma
  2. Create Pinecone index
  3. Batch upload to Pinecone
  4. Update query code
  5. Test thoroughly

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Performance Tips

Optimization Strategies

Indexing:
  • HNSW for speed (most DBs)
  • IVF for memory efficiency
  • Choose based on read/write ratio
Filtering:
  • Pre-filter when possible
  • Use metadata strategically
  • Consider hybrid indexes
Batching:
  • Batch upserts (100-1000 vectors)
  • Parallel queries when possible
  • Use connection pooling
Monitoring:
  • Track query latency
  • Monitor index size
  • Watch memory usage
  • Set up alerts