Best Vector Databases
Compare the top vector databases tools. Find the right one for your project.
Notable Features
Serverless & pod-based options Hybrid vector & sparse search Automated replication & backups Integrated security
Strengths
- + Fully managed serverless service
- + High-performance vector search
- + Hybrid search & metadata filters
- + Enterprise-grade reliability
- + Scales automatically
Considerations
- - Higher cost compared to OSS
- - Limited customization
- - No on-prem deployment
- - Pricing per pod & usage
Notable Features
HNSW + product quantization Metadata & payload filtering HTTP & gRPC API Managed cloud option
Strengths
- + Open-source & free to self-host
- + High-performance Rust engine
- + Advanced metadata filtering
- + Distributed & horizontally scalable
- + ACID transactions
Considerations
- - Smaller community & tooling
- - Requires configuration for scaling
- - Fewer built-in analytics
- - Cloud cost after free tier
Notable Features
Hybrid graph + vector search GraphQL queries Multi-modal modules Schema-based indexing
Strengths
- + Open-source with managed cloud
- + GraphQL & REST API
- + Knowledge graph & vector search
- + Extensible modules (multi-modal & LLMs)
- + Flexible schema design
Considerations
- - Steeper learning curve
- - Resource-intensive at scale
- - More complex setup
- - Higher cloud cost than self-hosting
Notable Features
Cloud-native architecture Hybrid search Real-time ingestion GPU acceleration & dynamic scaling
Strengths
- + Highly scalable & distributed
- + Multiple index types
- + GPU & CPU acceleration
- + Active open-source community
- + Hybrid search support
Considerations
- - Complex cluster management
- - High infrastructure requirements
- - Steep learning curve
- - Operational overhead
Notable Features
HNSW indexes with disk-backed storage MIT-licensed open source Embedding function wrappers Segment and metadata filtering
Strengths
- + Simple developer-first API
- + Persistence by default
- + Great for local prototypes
- + Python and JavaScript clients
- + Active open-source community
Considerations
- - Primarily single-node today
- - Manual scaling for high availability
- - Fewer enterprise governance features
- - Limited observability tooling
Notable Features
IVFFlat and HNSW indexes JSONB metadata columns Compatible with Timescale/Neon/Supabase CPU-friendly operation
Strengths
- + Runs inside Postgres
- + ACID transactions and SQL joins
- + Easy to deploy on managed Postgres
- + Supports hybrid similarity and filters
- + Works with familiar tooling
Considerations
- - Needs tuning for very large collections
- - Slower than specialized vector databases
- - No built-in sharding
- - Requires Postgres 14 or newer
Popular Comparisons
pinecone vs qdrant
Pinecone offers a turnkey managed vector database with hybrid search and enterprise SLA; Qdrant provides open-source flexibility with strong filtering and lower costs.
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weaviate vs milvus
Weaviate combines graph and vector search with modules for multi-modal retrieval; Milvus focuses on scalable, cloud‑native vector storage with GPU acceleration.
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chromadb vs pgvector
ChromaDB is the fastest way to spin up a local or serverless vector store, while pgvector lets Postgres teams add similarity search without new infrastructure.
Read comparison
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