Amazon S3 Vectors vs Turbopuffer
A side-by-side comparison of Amazon S3 Vectors and Turbopuffer, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Amazon S3 Vectors
Vector DBNative vector storage and querying in S3 — serverless, billion-vector scale.
View Amazon S3 VectorsAt a glance
| Attribute | Amazon S3 Vectors | Turbopuffer |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing | PAID | PAID |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Amazon Web Services | Turbopuffer |
| Capabilities |
|
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The honest brief
Amazon S3 Vectors
Pay only for storage and queries — AWS claims up to 90% lower cost than dedicated vector DBs for large, infrequently queried indexes.
- Two billion vectors per index
- S3 durability and elasticity
- No idle compute costs
- Native Bedrock Knowledge Bases integration
- Locked to the AWS ecosystem
- Cold queries are sub-second, not low-latency
- Up to 100 results per query
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
We found no distinguishing difference between Amazon S3 Vectors and Turbopuffer on the signals we track — pricing, license, platforms, and model support, or capabilities. Pick on team fit.