RAG platforms

Cloud RAG platform comparison: Bedrock Knowledge Bases vs Azure AI Search vs Agent Search

Compare managed cloud RAG options: Amazon Bedrock Knowledge Bases, Azure OpenAI with Azure AI Search, and Google Agent Search for enterprise search, permissions, citations, cost, and operations.

Updated 2026-06-119 min readIntermediate

Compare cloud AI platforms Read enterprise RAG security checklist

Best for

Enterprise teams choosing a managed RAG system
Cloud architects comparing AWS, Azure, and Google retrieval products
Developers deciding between managed RAG and custom vector stacks
Security teams reviewing permissions, citations, and data governance

Not for

A replacement for retrieval evaluation on your own documents
Assuming managed RAG automatically preserves source permissions
Ignoring ingestion quality, metadata, and source-system access control

Comparison

Choose by workflow, not brand

Option	Best for	Strengths	Tradeoffs	Use when
Bedrock Knowledge Bases	AWS-native RAG with Bedrock models, agents, guardrails, and AWS data controls	Managed retrieval and generation path with citations and integration into Bedrock workflows.	Best fit depends on AWS data sources, model access, quotas, and region support.	Your documents, security controls, and operations already live in AWS.
Azure AI Search plus Azure OpenAI	Microsoft enterprise search, Azure identity, document-level access, and Azure OpenAI apps	Strong fit for organizations with Azure data, Microsoft Entra, and AI Search governance.	Requires careful index design, security filters, and retrieval evaluation.	Your enterprise knowledge and identity stack is Microsoft-centered.
Google Agent Search	Google Cloud teams building search and answer generation over enterprise data stores	Managed data stores and search app patterns for generative AI experiences.	Validate connector, region, pricing, and data-governance fit for your exact sources.	Your data and app environment are already on Google Cloud.

Start with data ownership

RAG quality depends on documents, permissions, metadata, freshness, and source trust. The best cloud platform is often the one closest to the source systems and identity provider.

Map source systems, owners, sensitivity, and update cadence before choosing a product.
Confirm document-level permissions at retrieval time.
Check whether citations include stable source identifiers users can verify.

Managed does not mean finished

Managed RAG reduces infrastructure work, but teams still own ingestion policy, chunking, metadata, retrieval evaluation, user experience, and answer quality.

Create eval sets for exact-match, semantic, permission, and citation cases.
Test stale, duplicate, and conflicting documents.
Keep human review for high-impact answers.

Know when to go custom

A custom RAG stack can be worth it when retrieval logic, permissions, cost, model routing, observability, or multi-cloud requirements outgrow managed defaults.

Use managed RAG for speed and cloud-native governance.
Use custom RAG when retrieval rules or product UX are highly specialized.
Keep export paths for documents, embeddings, and evaluation data.

Decision Rules

A practical checklist

Use Bedrock Knowledge Bases for AWS-native RAG and Bedrock agent workflows.

Use Azure AI Search with Azure OpenAI for Microsoft identity and enterprise search integration.

Use Google Agent Search when Google Cloud data stores and search quality are central.

Move custom only when managed retrieval cannot meet permissions, quality, cost, or UX requirements.

Related Guides

Continue the decision path

Compare cloud AI platforms

Choose the larger hyperscaler AI platform first.

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Read enterprise RAG security checklist

Validate permissions, citations, and data controls.

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Enterprise RAG security checklist

Secure private knowledge retrieval before launch.

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Hybrid search RAG guide

Combine BM25, embeddings, and reranking for retrieval quality.

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Bedrock vs Azure OpenAI vs Vertex AI

Compare the broader cloud AI platform decision.

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Chinese Archive

Aligned deeper reading

Embedding and RAG archive

Chinese retrieval and embedding implementation notes.

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Dify and knowledge-base archive

Chinese knowledge-base and workflow materials.

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Topic Hubs

Explore the wider search cluster

Topic hub

RAG and models

Plan RAG systems, local LLM deployment, model APIs, cloud AI platforms, vector databases, evaluation, observability, rate limits, and cost optimization.

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Industry Pages

See this guide in a buyer workflow

Industry page

Data analytics AI

Compare AI tools for data analysis, business intelligence, data governance, customer data platforms, knowledge management, RAG, analytics workflows, and trusted decision support.

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FAQ

Common questions

Which cloud RAG platform is best?

The best platform is usually the one closest to your documents, identity provider, permissions, audit logs, and existing cloud operations.

Does managed RAG handle permissions automatically?

Not automatically in every architecture. Verify document-level access, source-system permissions, query-time filtering, and cache behavior before exposing private content.

When should I build custom RAG instead of using managed cloud RAG?

Build custom when managed options cannot meet retrieval quality, permission complexity, latency, observability, or product UX needs.

Source Links

Primary references used for this guide

Reference

Amazon Bedrock Knowledge Bases

AWS documentation for Bedrock Knowledge Bases.

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Reference

Azure OpenAI On Your Data

Microsoft documentation for Azure OpenAI On Your Data and document access.

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Reference

Azure AI Search overview

Microsoft documentation overview for Azure AI Search.

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Reference

Google Agent Search data stores

Google Cloud documentation for Agent Search apps and data stores.

Open

Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

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