Qdrant vs Turbopuffer
Detailed side-by-side comparison to help you choose the right tool
Qdrant
🔴DeveloperVector Database
Open-source, Rust-built vector similarity search engine with payload filtering, hybrid search, quantization, and a fully managed Qdrant Cloud — popular for RAG, recommendation, and agent memory.
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Starting Price
FreeTurbopuffer
🔴DeveloperAI Knowledge Tools
Turbopuffer is a serverless vector and full-text search engine built on object storage that delivers 10x cheaper similarity search at scale with sub-10ms latency for warm queries.
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Starting Price
$64/month minimumFeature Comparison
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Qdrant - Pros & Cons
Pros
- ✓Apache 2.0 license with a credible, focused open-source core — easy to self-host
- ✓Excellent quantization options dramatically reduce RAM and infra cost at large scale
- ✓Payload filtering uses inverted indexes so metadata constraints don't hurt vector recall
- ✓Multiple community MCP servers make it usable as agent memory from day one
Cons
- ✗Smaller managed-service ecosystem than Pinecone — fewer hand-holding features for non-engineers
- ✗Sparse hybrid search is solid but less mature than dedicated full-text engines
- ✗Self-hosting still requires Kubernetes or Docker operational knowledge
- ✗Cloud pricing is per cluster size rather than per-document, so capacity planning matters
Turbopuffer - Pros & Cons
Pros
- ✓10x cheaper than traditional vector databases at scale due to object storage-first architecture instead of RAM-heavy designs
- ✓Sub-10ms p50 latency for warm queries rivals in-memory databases while maintaining dramatically lower costs
- ✓Native BM25 full-text search and hybrid search combine semantic and keyword retrieval without needing separate search infrastructure
- ✓Unlimited namespaces with automatic scaling makes it ideal for multi-tenant SaaS applications with thousands of customers
- ✓Proven at extreme scale: 2.5T+ documents, 10M+ writes/s in production — not just benchmarks
Cons
- ✗$64/month minimum commitment can be expensive for small projects or hobbyists compared to free tiers on Pinecone or Qdrant
- ✗Cold namespace queries have significantly higher latency (~343ms p50) which may not suit real-time applications accessing infrequently-used data
- ✗Not open source — no self-hosted option for teams that need full control over their infrastructure
- ✗Write latency is higher than in-memory databases (p50 >200ms), which can be a bottleneck for write-heavy workloads
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