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TopK vs Turbopuffer

A side-by-side comparison of TopK and Turbopuffer, two Vector DB tools, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

TopK

Vector DB

Retrieval engine with hybrid search, multi-vector, and custom ranking in one query.

View TopK

Turbopuffer

Vector DB

Object-storage-backed vector DB. Serverless economics at scale.

View Turbopuffer

At a glance

Feature comparison of TopK and Turbopuffer
AttributeTopKTurbopuffer
CategoryVector DBVector DB
Pricing (differs)FREEMIUMPAID
LicenseProprietaryProprietary
Deployment (differs)HybridCloud
Platforms (differs)API, CLIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)TopKTurbopuffer

The honest brief

TopK

One query spans vector, keyword, and multi-vector search with custom ranking — no separate search + vector + reranker stack to stitch together.

  • Serverless, no infra to manage
  • Runs in your own VPC (BYOC)
  • Built-in embedding/OCR inference
  • Low latency at billion-doc scale
  • SDKs for Python, JS, Rust + MCP
  • Newer, smaller ecosystem than peers
  • No open-source self-host
  • Developer/API-first, no managed UI
  • Smaller community vs Pinecone/Qdrant

Turbopuffer

Indexes live on object storage, not RAM, so cost tracks usage not corpus size — built for huge, mostly-cold vector workloads.

  • S3-like billing: cold rest, warm reads
  • Scales to very large, cold corpora
  • No per-namespace minimums
  • Proven at Notion production scale
  • Cold reads have higher latency
  • Paid-only, no free self-host
  • API-only, no managed UI
  • Less mature ecosystem than peers
TopK detailsTurbopuffer detailsAll Vector DB apps