Qdrant vs Turbopuffer
A side-by-side comparison of Qdrant and Turbopuffer, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Qdrant | Turbopuffer |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing (differs) | FREEMIUM | PAID |
| License (differs) | Open core | Proprietary |
| Deployment (differs) | Hybrid | Cloud |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Qdrant | Turbopuffer |
| Capabilities (differs) |
|
|
The honest brief
Qdrant
Rust single-binary you can self-host, with payload filtering strong enough that teams pick it for metadata-heavy search.
- Open source, written in Rust
- Self-host or managed cloud
- Strong payload/metadata filtering
- Predictable latency at scale
- More ops than fully-managed rivals
- Smaller ecosystem than Pinecone
- Advanced features lean on managed cloud
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
When to pick which
Across the signals we compare, Qdrant and Turbopuffer differ on pricing, license, and deployment:
- Pricing
- FREEMIUM · PAID
- License
- Open core · Proprietary
- Deployment
- Hybrid · Cloud
Their capability lists differ in recorded depth — compare the full lists above before deciding.