Vector Database Leaderboard
Compare vector databases for RAG applications. Evaluate deployment options, cost efficiency, and features to find the best fit for your infrastructure.
Last updated: December 4, 2025
| Deployment | Cost | Index Types | Compare | ||
|---|---|---|---|---|---|
| BYOC, Managed Cloud | Minimum commitment $64/month | SPFresh | Proprietary | ||
| Self-Hosted, Managed Cloud | Starts ~$0.014/hour for smallest node | HNSW, Sparse (dot similarity) | Apache 2.0 | ||
| Self-Hosted, Managed Cloud | Free (local), Chroma Cloud starts at $0 with $5 free credits | HNSW, SPANN | Apache 2.0 | ||
| Self-Hosted, Managed Cloud | Free (self-hosted), infra cost only | IVF, HNSW, DiskANN | Apache 2.0 | ||
| Self-Hosted, Managed Cloud | Free (extension), infra cost only | Flat, IVFFlat, HNSW | PostgreSQL | ||
| Self-Hosted, Managed Cloud | Free (self-host), cloud starts ~$25/mo | HNSW, Hybrid dense+sparse | BSD | ||
| Managed Cloud | Storage: $0.33/GB/mo; Write Units: $4/million; Read Units: $16/million; Minimum $50/mo | Dense (HNSW-like), Sparse | Proprietary |
What is a Vector Database?
Vector databases are specialized systems designed to store and query high-dimensional vector embeddings. Unlike traditional databases that handle structured data, vector databases enable semantic similarity search—finding items that are conceptually similar rather than exact matches. This makes them essential for RAG systems, recommendation engines, and AI-powered search applications.
Why Vector Databases Matter for RAG
In RAG pipelines, vector databases serve as the knowledge layer, storing document embeddings and enabling fast semantic retrieval. The right vector database can dramatically improve retrieval speed, accuracy, and scalability. Factors like index types (HNSW, IVF), deployment flexibility, and cost efficiency directly impact your RAG system's performance and operational overhead.
Choosing the Right Vector Database
Selection depends on your scale, budget, and infrastructure preferences. Cloud-only solutions like Pinecone offer zero-ops convenience but higher costs. Open-source options like Qdrant and Milvus provide flexibility and cost control. For existing PostgreSQL users, pgvector offers a unified data layer without additional infrastructure. Consider deployment options, index types, and regional requirements when choosing.
For Production RAG at Scale
Best for:
- • Applications with billions of vectors
- • Cost-sensitive large-scale deployments
- • Multi-region requirements
For Rapid Prototyping
Best for:
- • MVPs and proof-of-concepts
- • Local development and testing
- • Small to medium datasets
For Zero Infrastructure Management
Best for:
- • Teams without DevOps resources
- • Enterprise compliance needs
- • Predictable latency requirements
For Existing PostgreSQL Users
Best for:
- • Applications already using PostgreSQL
- • Need for transactional consistency
- • SQL + vector query combination
Methodology
How We Evaluate Vector Databases
Unlike rerankers and embeddings, vector databases don't have standardized performance benchmarks. We evaluate them using a composite scoring system based on deployment flexibility, cost efficiency, licensing, regional availability, and feature completeness.
Scoring Components
Our composite score combines five factors: deployment flexibility (30%), cost efficiency (25%), license openness (20%), regional flexibility (15%), and feature completeness (10%). This produces a normalized score similar to ELO ratings for consistency across our leaderboards.
Why No Performance Benchmarks?
Vector database performance depends heavily on your specific use case: dataset size, query patterns, index configuration, and infrastructure. Instead of synthetic benchmarks, we focus on the factors that affect real-world deployment decisions.
Evaluation Criteria
We analyze deployment options (cloud, local, hybrid), cost structures, licensing models, supported index types (HNSW, IVF, DiskANN), regional availability, and feature sets including hybrid search, filtering, and multi-tenancy support.
Common questions
Vector Database FAQ
- What is a vector database?
- A vector database is a specialized database designed to store and efficiently query high-dimensional vector embeddings. Unlike traditional databases optimized for exact matches, vector databases enable semantic similarity search, making them essential for AI applications like RAG, recommendation systems, and image search.
- Do I need a dedicated vector database or can I use pgvector?
- It depends on your scale and requirements. pgvector is excellent for applications already using PostgreSQL with small to medium vector datasets (millions of vectors). For billion+ vectors, high throughput, or specialized features, dedicated vector databases like Qdrant, Milvus, or Turbopuffer offer better performance and scalability.
- What's the difference between HNSW and IVF indexes?
- HNSW (Hierarchical Navigable Small World) offers better query speed and accuracy but requires more memory. IVF (Inverted File) is more memory-efficient and better for very large datasets but slightly slower. HNSW is ideal for production systems needing low latency, while IVF works well for cost-sensitive large-scale deployments.
- How do I choose between cloud and self-hosted?
- Choose cloud (Pinecone, Turbopuffer, Weaviate Cloud) for zero infrastructure management and predictable costs. Choose self-hosted (Qdrant, Milvus, pgvector) for maximum control, lower costs at scale, data sovereignty, and custom configurations. Hybrid options like Qdrant offer both deployment models.
- What's the cost of running a vector database at scale?
- Costs vary widely. Self-hosted options (Milvus, Chroma) have only infrastructure costs. Managed services range from Turbopuffer ($64/mo minimum) to Pinecone ($50/mo+ with usage-based pricing). For billions of vectors, expect $500-$5000/month depending on throughput, redundancy, and whether you choose managed or self-hosted.
- Can I migrate between vector databases later?
- Yes, but it requires planning. Most vector databases support standard embedding formats, so migration involves exporting vectors and metadata, then re-indexing in the new database. Open-source options reduce lock-in risk. Consider using abstraction layers like LangChain or LlamaIndex to simplify future migrations.