datastores.ai

Compare databases

Side-by-side comparison of every vector database in our directory. Features, licensing, deployment models, and more.

Choosing the right vector database can make or break your AI application. Compare deployment models, pricing structures, performance characteristics, and ecosystem integrations to find the perfect match for your requirements.

How to compare vector databases

Technical Considerations

  • •Indexing algorithms: HNSW for speed, IVF for scale, DiskANN for cost efficiency
  • •Distance metrics: Support for cosine, euclidean, dot product, and custom metrics
  • •Filtering capabilities: Pre-filtering, post-filtering, or hybrid approaches for metadata
  • •Multi-tenancy: Namespace isolation, resource quotas, and security boundaries

Operational Factors

  • •Scaling model: Vertical scaling, horizontal sharding, or serverless auto-scaling
  • •Deployment flexibility: Cloud-only, self-hosted, hybrid, or edge deployment options
  • •Monitoring & observability: Built-in metrics, OpenTelemetry support, and logging
  • •Backup & recovery: Snapshot frequency, point-in-time recovery, geo-replication

Head-to-head comparisons

Detailed side-by-side breakdowns of the most popular matchups.

Pinecone vs WeaviatePinecone vs QdrantWeaviate vs QdrantMilvus vs Qdrantpgvector vs PineconeChromaDB vs WeaviateElasticsearch vs PineconeSupabase Vector vs PineconeSupabase vs FirebaseRedis vs MemcachedRedis vs MongoDBRedis vs KafkaRedis vs DynamoDBElasticsearch vs OpenSearch

All databases

Pinecone

Pinecone

cloud

Serverless vector database for AI at scale

  • Serverless architecture
  • Hybrid sparse-dense search
  • Metadata filtering
  • Namespaces & multi-tenancy
  • Real-time index updates
  • SOC 2 Type II compliant
Semantic searchRecommendation enginesRAG pipelinesAnomaly detection
Zilliz Cloud

Zilliz Cloud

cloud

Managed Milvus with enterprise scalability

  • Fully managed Milvus
  • Auto-scaling
  • Built-in backups & DR
  • Enterprise SLAs
  • Role-based access control
  • Usage-based pricing
Enterprise searchProduction RAG pipelinesMulti-tenant appsReal-time recommendations
Upstash Vector

Upstash Vector

cloud

Serverless vector database with per-request pricing

  • True serverless (scale to zero)
  • Per-request pricing
  • REST API (edge-compatible)
  • Metadata filtering
  • Multiple distance metrics
  • Global replication
Edge AI appsServerless RAGChatbot memoryLow-traffic projects
Turbopuffer

Turbopuffer

cloud

Serverless vector search on object storage

  • Built on object storage (S3)
  • Separated compute & storage
  • Low-cost at scale
  • Serverless architecture
  • REST API
  • Automatic indexing
Cost-efficient vector searchLarge archival datasetsBatch processingLog similarity search
Weaviate

Weaviate

open-source

AI-native vector database with built-in vectorizers

  • Built-in vectorization modules
  • Hybrid BM25 + vector search
  • GraphQL & REST APIs
  • Multi-modal support
  • Horizontal scaling
  • RBAC & multi-tenancy
Semantic searchGenerative search (RAG)ClassificationImage search
Qdrant

Qdrant

open-source

High-performance vector search engine in Rust

  • Written in Rust for speed
  • Rich payload filtering
  • Multiple distance metrics
  • Quantization support
  • Distributed deployment
  • gRPC & REST APIs
Similarity searchNeural searchMatching enginesRAG applications
ChromaDB

ChromaDB

open-source

The AI-native open-source embedding database

  • Embedded & client/server modes
  • Automatic embedding generation
  • Metadata filtering
  • Python & JavaScript SDKs
  • LangChain integration
  • Simple, intuitive API
Prototyping RAG appsLocal AI developmentChatbot memoryDocument Q&A
pgvector

pgvector

open-source

Vector search for PostgreSQL

  • PostgreSQL extension
  • IVFFlat & HNSW indexes
  • Exact & approximate search
  • SQL-native queries
  • ACID transactions
  • Works with any Postgres host
Adding vectors to existing appsHybrid relational + vectorPrototypingSmall-to-medium datasets
Vald

Vald

open-source

Highly scalable distributed vector search engine

  • Kubernetes-native deployment
  • Custom NGT algorithm
  • Horizontal auto-scaling
  • gRPC API
  • Automatic rebalancing
  • Multi-index support
Large-scale similarity searchImage search at scaleReal-time recommendationsInternal ML platforms
Milvus

Milvus

hybrid

Distributed vector database built for scale

  • Billion-scale vector search
  • Separated compute & storage
  • Multiple index types
  • Strong consistency
  • GPU acceleration
  • Multi-language SDKs
Large-scale similarity searchRecommendation systemsDrug discoveryImage deduplication
Vespa

Vespa

hybrid

The open big data serving engine

  • Combined text + vector search
  • Real-time indexing
  • ML model serving
  • Horizontal auto-scaling
  • Multi-phase ranking
  • Billions of documents
Enterprise searchE-commerce rankingContent personalizationConversational AI
Deep Lake

Deep Lake

hybrid

Multi-modal AI data lake with vector search

  • Multi-modal data storage
  • Dataset versioning (Git-like)
  • Vector similarity search
  • Streaming data loader for ML
  • LangChain & LlamaIndex integration
  • S3/GCS/Azure storage backends
ML dataset managementMulti-modal RAGComputer vision pipelinesData versioning
MongoDB Atlas Vector Search

MongoDB Atlas Vector Search

traditional

Vector search built into the #1 document database

  • Native MongoDB integration
  • Aggregation pipeline queries
  • Pre-filtering with MQL
  • Atlas Search (full-text + vector)
  • Global multi-cloud deployment
  • SOC 2 & HIPAA compliant
Semantic search in existing appsE-commerce recommendationsContent discoveryFraud detection
Elasticsearch

Elasticsearch

traditional

Distributed search engine with vector capabilities

  • kNN vector search
  • Hybrid BM25 + vector queries
  • Distributed & horizontally scalable
  • Kibana visualizations
  • Machine learning features
  • Massive integration ecosystem
Enterprise searchLog analytics + similarityE-commerce searchSecurity analytics
OpenSearch

OpenSearch

traditional

Community-driven fork of Elasticsearch with vector search

  • k-NN vector search (FAISS, NMSLIB)
  • Hybrid text + vector search
  • OpenSearch Dashboards
  • SQL query support
  • Anomaly detection
  • AWS managed service available
Log & event analyticsEnterprise searchSecurity monitoringObservability + AI
Redis Vector

Redis Vector

traditional

Sub-millisecond vector search in memory

  • Sub-millisecond latency
  • In-memory vector index
  • HNSW & FLAT algorithms
  • Hybrid vector + tag filtering
  • Redis Stack modules
  • Redis Enterprise Cloud
Real-time recommendationsChatbot session memoryLow-latency semantic searchCaching + similarity
Supabase Vector

Supabase Vector

traditional

pgvector on Supabase — vectors in your Postgres

  • pgvector integration
  • SQL-native vector queries
  • Supabase Auth & RLS
  • Edge Functions support
  • Realtime subscriptions
  • Dashboard & client libraries
Full-stack AI appsStartup MVPs with vectorsAuthenticated RAGRapid prototyping
Azure AI Search

Azure AI Search

cloud

Microsoft's enterprise vector + full-text search service

  • Vector + full-text hybrid search
  • Semantic ranking
  • AI enrichment pipelines
  • Azure OpenAI integration
  • Enterprise security (RBAC, VNET)
  • Managed scaling & replication
Enterprise knowledge basesInternal document searchAzure OpenAI RAGCompliance-sensitive AI
Google Vertex AI Vector Search

Google Vertex AI Vector Search

cloud

Google-scale vector search on GCP

  • Built on Google ScaNN
  • Sub-10ms latency at scale
  • Streaming & batch updates
  • Vertex AI integration
  • Filtering & boosting
  • Global deployment
Google Cloud RAGProduct recommendationsMedia similarity searchEnterprise AI on GCP
SingleStore

SingleStore

traditional

Unified transactional-analytical DB with vector search

  • Unified HTAP + vector engine
  • Standard SQL interface
  • Real-time analytics
  • Distributed architecture
  • JSON & full-text support
  • Cloud & on-premise deployment
Real-time analytics + AIUnified operational + vector queriesFinancial servicesTelco & IoT
Kinetica

Kinetica

traditional

GPU-accelerated database with vector search

  • GPU-accelerated queries
  • Vector similarity search
  • Native geospatial support
  • Real-time streaming ingest
  • SQL interface
  • On-premise & cloud
GIS + vector searchHigh-frequency financial dataIoT real-time analyticsTelco network optimization

Vector Database Comparison FAQ

Pinecone vs Weaviate: which is better for production?

Both are production-ready managed services. Pinecone offers a simpler serverless experience with automatic scaling and zero infrastructure management, making it ideal for teams that want to move fast. Weaviate provides more flexibility with hybrid cloud deployment options, built-in vectorization modules, and strong open-source community support. Choose Pinecone for simplicity and scale, Weaviate for customization and hybrid deployment.

Should I use pgvector or a dedicated vector database?

Use pgvector if you're already running PostgreSQL and want to add semantic search without introducing new infrastructure. It's perfect for applications that need to combine structured relational queries with vector similarity search. Choose a dedicated vector database like Qdrant or Milvus if you need specialized indexing algorithms, higher query throughput, or advanced features like multi-tenancy and filtering at scale.

What's the difference between cloud and self-hosted vector databases?

Cloud-managed services (Pinecone, Weaviate Cloud) handle infrastructure, scaling, backups, and updates automatically. You pay for convenience and speed to production but have less control over deployment and data locality. Self-hosted solutions (Qdrant, Milvus) give you complete control over infrastructure, data sovereignty, and cost optimization at scale, but require operational expertise and DevOps resources.

How do I compare vector database performance?

Key metrics include: query latency (p50, p95, p99), throughput (queries per second), indexing speed (vectors per second), memory efficiency (RAM per million vectors), and recall accuracy at different speed-accuracy tradeoffs. Run benchmarks with your actual embedding model, dataset size, and query patterns, as published benchmarks may not reflect your use case.

Are open-source vector databases production-ready?

Yes, many open-source vector databases like Qdrant, Milvus, and Weaviate are production-ready and used by large enterprises. They offer strong performance, active development, and comprehensive documentation. However, you'll need to manage infrastructure, monitoring, scaling, and updates yourself, or use their managed cloud offerings for operational simplicity.