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Vector Databases

Qdrant

Rust-built vector database with the best self-host story

8.7 / 10 20 Verified Reviewers Verified 2026-04-30 PythonRustGoTypeScript

Qdrant is open-source, Rust-built, and runs as comfortably on a single VM as on a Kubernetes cluster. Performance per dollar at large scale (>100M vectors) consistently beats managed offerings. Cloud product is solid but the OSS path is genuinely production-grade. Best for teams that want self-host control without paying the Weaviate ops complexity tax.

Pricing
Open-source free + Cloud from $0.50/M queries

Developer Consensus: Pros

  • Single-node performance beats most clustered competitors 18× mentioned
  • Rust memory profile means lower hosting costs 15× mentioned
  • gRPC API for low-latency production workloads 12× mentioned
  • Filter+vector queries optimized for production patterns 10× mentioned
  • Snapshot-based backups that actually work 8× mentioned

Common Friction Points

  • Smaller ecosystem of integrations than Pinecone 10× mentioned
  • Cloud product features lag OSS slightly 7× mentioned
  • Cluster mode requires more operator knowledge 6× mentioned
  • Less LangChain-default than Chroma 5× mentioned
  • Documentation for advanced features feels sparse 4× mentioned

Verified Peer Reviews

@rust_native
Backend Engineer · Rust · Mid
Verified
gRPC + Rust is the production combo.

We saturate a single Qdrant node at 8K QPS on a 32-core box with 200M vectors. Pinecone serverless was 4x more expensive at this scale. Memory profile is the unsung win.

1.13.x self-hosted, April 2026 4.7/5 · 22 helpful
@cost_engineer
Platform Engineer · Python · Enterprise
Verified
Performance per dollar is unmatched.

Migrated from Weaviate. 30% faster queries on the same hardware. Backup story is simpler. Worth the migration cost.

1.13.x self-hosted, March 2026 4.5/5 · 16 helpful
@embed_team
ML Engineer · Python · Startup
Verified
OSS quality you can actually run in prod.

Many OSS vector DBs are demo-grade. Qdrant is one of two we trust in prod (Weaviate being the other). Single-node deploy got us to 50M vectors before clustering.

1.13.x, April 2026 4.2/5 · 13 helpful

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Methodology

Every review on this page is verified through GitHub OAuth and weighted by reviewer credibility, use-case match, and conflict-of-interest disclosure. Aggregate scores combine with recency decay so rankings reflect current reality. Read full methodology →