Vector Database Comparison 2025 to 2026: Milvus vs Qdrant vs Weaviate vs pgvector
How the vector database landscape changed from 2025 to 2026, with real star growth.
💡 What You Will Learn
How the vector database landscape changed from 2025 to 2026, with real star growth.
📜 Table of Contents
Vector Database Comparison 2025 to 2026
Vector databases matured fast between 2025 and 2026. Here is how the four leaders compare now, with star counts from the 2026-07-31 snapshot.
The four leaders
| Database | Stars (2026-07) | Focus |
|---|---|---|
| Milvus | 45,439 | Enterprise, billions of vectors |
| Qdrant | 33,697 | Production Rust, rich filters |
| Weaviate | 16,677 | Hybrid graph + vector |
| pgvector | 22,417 | Postgres extension |
What changed from 2025
- Hybrid search became table stakes - keyword + vector in one query is now expected
- pgvector 0.8+ added HNSW improvements - Postgres users no longer need a separate DB for most apps
- Disk-based indexes matured - Qdrant and Milvus handle billions of vectors on NVMe without RAM blowups
- Managed cloud won - Qdrant Cloud, Zilliz (Milvus), Weaviate Cloud dominate new deployments
- Agent-era workloads - memory layers (mem0 62,202 stars) integrate with vector DBs
Decision for 2026
- Small app, Postgres already there: pgvector
- Serious product, want managed: Qdrant Cloud or Zilliz
- Need hybrid search with typo tolerance: Typesense (26,385 stars) or Weaviate
- Research/benchmarking: Milvus (its benchmark tool is industry standard)
FAQ
Did anything get worse? Self-managing a vector DB is harder in 2026 - indexes and scaling grew complex; managed services are the sane default. Is Chroma still relevant? Yes, for local dev and prototypes; not for production scale.
❓ FAQ
Did anything get worse?
Self-managing a vector DB is harder in 2026 - indexes and scaling grew complex; managed services are the sane default.
Is Chroma still relevant?
Yes, for local dev and prototypes; not for production scale.
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