Turbopuffer vs Weaviate
Overview
Turbopuffer is a serverless vector database designed for speed and cost efficiency.
Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.
Key features
Core capabilities each platform advertises.
- Open-source vector database
- Hybrid search (vector + keyword)
- Multi-modal support
- GraphQL and REST APIs
- Kubernetes-native deployment
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Up to 100x cost reduction compared to traditional vector databases
- Sub-10ms p50 latency with serverless auto-scaling
- No namespace limits unlike competitors
- Enterprise compliance (HIPAA, SOC 2, CMEK) on standard plan
- Trusted by major companies: Notion, Cursor, Linear, PlayerZero
Cons
- $64/month minimum spend makes it less suitable for small experiments
- Relatively new platform with smaller community
- Limited documentation compared to more established solutions
- Object storage architecture may have latency trade-offs for some use cases
Turbopuffer or Weaviate — which should you choose?
Choose Turbopuffer if you wantChoose if you want
Choose Weaviate if you wantChoose if you want
- Multimodal search across text, images, and more
- RAG with hybrid retrieval
- E-commerce product search
- Content recommendation engines
- Self-hosted vector search
Compare Turbopuffer and Weaviate on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.