M4A

Turbopuffer

Fast, cost-effective vector database with object storage

paidproductionmanagedobject-storagecost-effectiveserverless
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Memory Types

semantic, contextual

Integrations

langchain, openai, cohere


Overview


Turbopuffer is a managed vector database that focuses on being the fastest and most cost-effective option for vector search. Built on object storage (S3), it dramatically reduces storage costs while maintaining high performance through clever caching and indexing strategies. The platform is designed for developers who want production-grade vector search without the high costs of traditional solutions.


By leveraging object storage and aggressive caching, Turbopuffer can offer storage costs up to 95% lower than competitors while maintaining query performance through intelligent data placement and hot/cold tier management. It's particularly attractive for startups and cost-conscious teams building RAG applications.


Key Features


  • **Object Storage Backend**: Uses S3 for 95% lower storage costs
  • **Smart Caching**: Hot data in memory, cold data on object storage
  • **Fast Queries**: Sub-100ms queries despite object storage backend
  • **Serverless**: No infrastructure management required
  • **Automatic Scaling**: Scales automatically with usage
  • **Metadata Filtering**: Rich filtering on vector metadata
  • **REST API**: Simple HTTP API for all operations
  • **Cost Transparency**: Clear, predictable pricing

    • When to Use Turbopuffer


    Turbopuffer is ideal for:

  • Cost-sensitive applications with large vector datasets
  • Startups wanting to minimize infrastructure costs
  • RAG applications with infrequent query patterns
  • Projects with clear hot/cold data access patterns
  • Teams wanting managed infrastructure without high costs
  • Applications that can tolerate object storage latency

    • Pros


  • Dramatically lower storage costs than competitors
  • Serverless with zero operational overhead
  • Simple pricing model
  • Fast enough for most RAG applications
  • Easy to get started
  • Good for cost-conscious teams
  • Scales automatically

    • Cons


  • Newer platform with smaller user base
  • May have higher latency than in-memory solutions
  • Less feature-rich than established competitors
  • Smaller ecosystem and integration support
  • Limited enterprise features
  • Performance depends on object storage latency
  • Less suitable for ultra-low-latency requirements

    • Pricing


  • **Pay-as-you-go**: $0.10 per GB stored/month
  • **Queries**: $0.40 per million queries
  • **No minimum**: Start small, scale as needed
  • **Transparent**: No hidden fees or pod pricing

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