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Vector Database Comparison

Compare 8 vector databases for RAG and AI applications. Features, pricing, performance, and recommendations.

✓8 Databases Compared
✓Real Benchmarks
✓Use Case Guide

Quick Comparison

DatabaseTypePricingPerformanceEase of UseBest For
Pinecone⭐
Cloud$70-$280/moexcellentexcellentQuick MVP, No DevOps team, Need reliability
Weaviate⭐
Cloud / Self-hostedFree (self-hosted) or $25+/moexcellentgoodHybrid search needs, Open source preference, Custom deployments
Qdrant⭐
Cloud / Self-hostedFree (self-hosted) or $30+/moexcellentvery-goodHigh performance, Complex filtering, Production scale
Chroma⭐
Self-hostedFree (open source)goodexcellentPrototypes, Development, Small projects
Milvus
Cloud / Self-hostedFree (self-hosted) or contactexcellentmoderateEnterprise scale, Billions of vectors, High throughput
PostgreSQL + pgvector⭐
Self-hostedFree (open source)goodvery-goodAlready using Postgres, Simple use cases, Transactional needs
Redis Vector Search
Cloud / Self-hostedFree (OSS) or contactexcellentvery-goodReal-time needs, Already using Redis, Hybrid caching + vectors
Elasticsearch
Cloud / Self-hostedFree (OSS) or $95+/movery-goodmoderateAlready using Elasticsearch, Hybrid search, Complex queries
Upstash Vector⭐
Cloud$0.20 per 100K queriesexcellentexcellentServerless apps, Pay-per-use pricing, Edge deployments, Small to medium projects
Zilliz Cloud
Cloud$0.20/hour + storageexcellentvery-goodEnterprise Milvus users, Need managed service, Multi-cloud deployments
Turbopuffer⭐
CloudFree tier, then $0.40 per 1M vectors/moexcellentexcellentCost optimization, Large datasets, S3/R2 users
Vespa
Cloud / Self-hostedFree (self-hosted) or contactexcellentmoderateReal-time applications, Complex ranking needs, Large-scale production
LanceDB⭐
Self-hosted / CloudFree (open source)very-goodvery-goodCost optimization, Embedded apps, Python projects, Data versioning needs

Detailed Comparison

Pinecone⭐

Cloud

$70-$280/mo
Queries/sec
10,000+
Max Vectors
Unlimited
Features
Managed serviceAuto-scalingHybrid searchNamespacesMetadata filtering
✅ Pros:
  • Easiest to use
  • Great documentation
  • Scales automatically
  • No infrastructure
⚠️ Cons:
  • Most expensive
  • Vendor lock-in
  • Limited self-hosting
Best For:
Quick MVP, No DevOps team, Need reliability

Weaviate⭐

Cloud / Self-hosted

Free (self-hosted) or $25+/mo
Queries/sec
1,000+
Max Vectors
Billions
Features
GraphQL APIHybrid searchMulti-tenancyGenerative searchMultiple vectorizers
✅ Pros:
  • Open source
  • Flexible deployment
  • Great hybrid search
  • Active community
⚠️ Cons:
  • More complex setup
  • Self-hosting overhead
  • Less mature than Pinecone
Best For:
Hybrid search needs, Open source preference, Custom deployments

Qdrant⭐

Cloud / Self-hosted

Free (self-hosted) or $30+/mo
Queries/sec
10,000+
Max Vectors
Billions
Features
Rust-basedPayload indexingQuantizationFilteringDistributed mode
✅ Pros:
  • Very fast
  • Open source
  • Great filtering
  • Good for production
⚠️ Cons:
  • Smaller community
  • Limited integrations
  • Learning curve
Best For:
High performance, Complex filtering, Production scale

Chroma⭐

Self-hosted

Free (open source)
Queries/sec
100+
Max Vectors
Millions
Features
Embedded databaseMultiple backendsEasy integrationLangChain support
✅ Pros:
  • Extremely easy to use
  • Perfect for prototypes
  • Free and open source
  • Great docs
⚠️ Cons:
  • Not for production scale
  • Limited features
  • Single machine
Best For:
Prototypes, Development, Small projects

Milvus

Cloud / Self-hosted

Free (self-hosted) or contact
Queries/sec
10,000+
Max Vectors
Trillions
Features
Distributed architectureGPU supportHigh throughputMultiple indexesTime travel
✅ Pros:
  • Scales to billions
  • Very performant
  • Enterprise-ready
  • Strong community
⚠️ Cons:
  • Complex setup
  • Resource intensive
  • Steep learning curve
Best For:
Enterprise scale, Billions of vectors, High throughput

PostgreSQL + pgvector⭐

Self-hosted

Free (open source)
Queries/sec
100-1,000
Max Vectors
Millions
Features
Postgres extensionSQL interfaceExisting Postgres ecosystemACID compliance
✅ Pros:
  • Use existing Postgres
  • SQL queries
  • No new infrastructure
  • ACID transactions
⚠️ Cons:
  • Not optimized for vectors
  • Limited scale
  • Slower than dedicated DBs
Best For:
Already using Postgres, Simple use cases, Transactional needs

Redis Vector Search

Cloud / Self-hosted

Free (OSS) or contact
Queries/sec
10,000+
Max Vectors
Millions (limited by RAM)
Features
In-memoryVery fastHybrid queriesExisting Redis ecosystem
✅ Pros:
  • Extremely fast
  • Use existing Redis
  • Great for caching + vectors
  • Simple API
⚠️ Cons:
  • Memory intensive
  • Expensive for large datasets
  • Limited to RAM
Best For:
Real-time needs, Already using Redis, Hybrid caching + vectors

Elasticsearch

Cloud / Self-hosted

Free (OSS) or $95+/mo
Queries/sec
1,000+
Max Vectors
Billions
Features
Dense vectorsHybrid searchFull-text searchAnalyticsDistributed
✅ Pros:
  • Mature ecosystem
  • Great for hybrid search
  • Powerful queries
  • Good tooling
⚠️ Cons:
  • Resource heavy
  • Complex to operate
  • Not vector-optimized
Best For:
Already using Elasticsearch, Hybrid search, Complex queries

Upstash Vector⭐

Cloud

$0.20 per 100K queries
Queries/sec
10,000+
Max Vectors
Millions
Features
ServerlessPay-per-requestREST APIGlobal edge networkNo infrastructure
✅ Pros:
  • True serverless (pay per use)
  • No idle costs
  • Global CDN
  • Super easy setup
  • Great for serverless apps
⚠️ Cons:
  • Newer service
  • Smaller ecosystem
  • Limited features vs Pinecone
Best For:
Serverless apps, Pay-per-use pricing, Edge deployments, Small to medium projects

Zilliz Cloud

Cloud

$0.20/hour + storage
Queries/sec
10,000+
Max Vectors
Trillions
Features
Managed MilvusAuto-scalingMulti-cloudGPU accelerationTime travel
✅ Pros:
  • Managed Milvus
  • Enterprise support
  • Multi-cloud
  • Great performance
⚠️ Cons:
  • More expensive than self-hosted
  • Milvus complexity
Best For:
Enterprise Milvus users, Need managed service, Multi-cloud deployments

Turbopuffer⭐

Cloud

Free tier, then $0.40 per 1M vectors/mo
Queries/sec
5,000+
Max Vectors
Billions
Features
Object storage backendS3/R2 compatibleFast queriesSimple API
✅ Pros:
  • Very affordable
  • Built for scale
  • Object storage (cheap)
  • Fast queries
⚠️ Cons:
  • Newer service
  • Smaller community
  • Limited documentation
Best For:
Cost optimization, Large datasets, S3/R2 users

Vespa

Cloud / Self-hosted

Free (self-hosted) or contact
Queries/sec
10,000+
Max Vectors
Billions
Features
Real-time servingHybrid searchML model servingStreaming updatesGrouping & aggregation
✅ Pros:
  • Real-time updates
  • Handles structured + vectors
  • Production-proven (Yahoo/Spotify)
  • Advanced ranking
⚠️ Cons:
  • Complex setup
  • Steep learning curve
  • Resource intensive
Best For:
Real-time applications, Complex ranking needs, Large-scale production

LanceDB⭐

Self-hosted / Cloud

Free (open source)
Queries/sec
1,000+
Max Vectors
Billions
Features
Embedded databaseDisk-basedFast queriesSQL supportVersion control for data
✅ Pros:
  • Open source
  • Disk-based (cheaper than memory)
  • Fast
  • Embedded or server mode
  • Version control
⚠️ Cons:
  • Newer
  • Smaller ecosystem
  • Limited production usage
Best For:
Cost optimization, Embedded apps, Python projects, Data versioning needs

Need Help Choosing a Vector Database?

I've implemented vector search across all major platforms. Let me help you choose and set up the right database for your needs.

Real example: Migrated a client from Pinecone to Qdrant, reducing costs from $2k/month to $200/month while improving query performance by 40%.

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