Azure AI Search vs Bedrock Knowledge Bases — choosing a RAG foundation
If you are building retrieval-augmented generation inside an enterprise, you are probably choosing between these two — and the decision is usually made by which cloud your data already lives in rather than by the services themselves.
AI Tools Hub Editorial · published · updated
The honest starting point
Both are competent managed retrieval services. Both handle chunking, embedding, vector storage and retrieval without you operating infrastructure. The quality difference between them is smaller than the difference made by your chunking strategy and the quality of your source documents.
Where they differ
| Azure AI Search | Bedrock Knowledge Bases | |
|---|---|---|
| Cloud | Azure | AWS |
| Identity | Entra ID | IAM |
| Hybrid search | Mature — vector plus keyword plus semantic ranking | Supported |
| Model choice | Azure OpenAI and others | Multiple model providers through Bedrock |
| Document ingestion | Rich connector set | S3-centric |
| Japan region | Available | Available |
| Best when | Microsoft 365 data and Entra ID | AWS-native data estate |
What actually decides it
- Where the source documents already are — moving them is the expensive part
- Which cloud your security team has already approved
- Which identity system governs access to the source material
- Whether you need hybrid keyword and vector search, where Azure is stronger
The part that is not the service
Most RAG quality problems are document problems. Inconsistent structure, stale content, no clear ownership, three versions of the same policy. A managed service retrieves accurately from a corpus that contradicts itself and the answers will still be wrong. Budget for document cleanup; it is where the quality comes from.
Should we build our own instead?
Only if you have a requirement neither satisfies and the engineering capacity to own it. The managed services remove a large amount of operational work that is not differentiating.
Can we keep everything in Japan?
Both offer Japan regions. Confirm that every component in your pipeline — storage, embeddings, inference — is in-region, since it is easy for one of them not to be.
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