Milvus vs Amazon S3 Vectors
A side-by-side comparison of Milvus and Amazon S3 Vectors, 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 | Milvus | Amazon S3 Vectors |
|---|---|---|
| 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) | Zilliz | Amazon Web Services |
The honest brief
Milvus
Storage/compute split plus DiskANN make it the most robust open-source choice at billion-vector scale.
- Scales to billion-vector deployments
- Storage/compute separation
- Many index types (HNSW, IVF, DiskANN) + GPU
- Mature project with a large community
- Operationally heavy to self-host
- Overkill for small workloads
- Performance hinges on data quality
- Higher latency than Qdrant at p50
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