Turbopuffer vs Weaviate
A side-by-side comparison of Turbopuffer and Weaviate, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
The honest brief
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
Weaviate
Built-in vectorizer modules embed text inline — raw text in, vectors out — so you skip running a separate embedding pipeline.
- Hybrid BM25 + vector search
- Self-hostable or managed cloud
- GraphQL and REST APIs
- Resource-heavy at large scale
- Module config has a learning curve
- Managed tier costs add up
- Newer than some lexical engines