Amazon Bedrock Knowledge Bases vs Azure AI Search and Foundry IQ vs Google RAG Engine

Introduction

Enterprise AI is only as useful as the information it can reliably retrieve. A model that can’t find the right internal document, or finds it but can’t tell whether the requesting user is actually allowed to see it, isn’t production-ready no matter how good the underlying LLM is. That’s the real problem behind Amazon Bedrock Knowledge Bases vs Azure AI Search vs Foundry IQ vs Google RAG Engine, and it’s a messier comparison than it looks at first, because these four aren’t four peers of the same thing. This builds directly on the foundation-model layer covered in our Bedrock vs Foundry vs Gemini Enterprise vs OCI comparison, the retrieval layer sits on top of whichever model platform you’ve already chosen, not instead of it. Bedrock Knowledge Bases is AWS’s managed RAG capability. Azure AI Search is retrieval infrastructure. Foundry IQ is a managed knowledge layer built on top of that infrastructure, not a competing alternative to it. And Google’s RAG Engine is one component within the broader Gemini Enterprise Agent Platform, sitting alongside a separate Vector Search tool. Getting that architecture straight matters more than any feature checklist, this comparison covers what each layer actually does, how they relate, and which combination fits different enterprise RAG requirements.

Quick Answer

Short answer: AWS-heavy organizations should default to Amazon Bedrock Knowledge Bases. Microsoft/Azure-heavy organizations need both Azure AI Search (the retrieval infrastructure) and Foundry IQ (the managed knowledge layer on top of it), not a choice between them. Google Cloud-heavy organizations should use RAG Engine on the Gemini Enterprise Agent Platform. The right choice depends on your existing cloud ecosystem, data sources, permission requirements, agentic retrieval needs, and how much custom retrieval logic you’re willing to build versus use managed.

Comparison Table

What Is Amazon Bedrock Knowledge Bases?

Amazon Bedrock Knowledge Bases is AWS’s managed retrieval-augmented generation capability, handling the full pipeline from raw documents to grounded LLM responses within Bedrock. Data gets ingested from sources like S3, chunked into retrievable segments, converted into embeddings, and indexed in a vector store. At query time, the Retrieve API pulls relevant chunks, and RetrieveAndGenerate combines retrieval with generation in a single call, returning both an answer and source citations.

AWS supports both managed vector stores and customer-managed options (Amazon OpenSearch, Pinecone, Redis, and others), so teams with existing vector infrastructure aren’t forced to migrate. Reranking and agentic retrieval capabilities let more complex queries get decomposed and handled across multiple retrieval steps rather than a single flat search. Building and tuning this chunking, embedding, and retrieval pipeline reliably is genuine data engineering work, not just API configuration. This is also the direction AWS is actively pushing new customers toward, with Amazon Kendra closed to new signups as of July 30, 2026, Bedrock Knowledge Bases is now the primary AWS-native path for both enterprise search and RAG, not just RAG specifically.

What Is Azure AI Search?

Azure AI Search, formerly Azure Cognitive Search, is the search and retrieval infrastructure underneath most Microsoft-based AI solutions. It supports full-text keyword search, native vector search, and hybrid retrieval combining both with a semantic ranking layer, plus filtering, faceting, and geospatial query support that goes well beyond what a pure vector database offers.

It’s important not to think of Azure AI Search as “just a vector database”, it’s a full search platform that predates the current RAG wave and has been the indexing layer for countless copilots and chatbots already in production, sitting alongside Azure Machine Learning in the broader Azure AI ecosystem covered in our SageMaker vs Azure ML vs Vertex AI comparison. Its agentic retrieval capabilities, built around the concepts of knowledge sources (pointers to indexed or remote data) and knowledge bases (the retrievable unit built from those sources), are also the literal foundation Foundry IQ is built on, which is the relationship worth understanding clearly before evaluating either one.

What Is Microsoft Foundry IQ?

Foundry IQ is Microsoft’s managed knowledge layer for enterprise AI agents, announced at Ignite 2025 alongside the Azure AI Foundry-to-Microsoft-Foundry rebrand. The relationship to Azure AI Search is direct and worth stating precisely: Azure AI Search provides the underlying indexing and retrieval infrastructure. Foundry IQ is the managed layer built on top of it, not a competing product, and not something you choose instead of Azure AI Search. One technical deep-dive put it well: you’re not choosing between the two, Foundry IQ is simply the new front door to the same underlying search service.

In practice, Foundry IQ automates what used to be manual RAG-pipeline work, connecting to knowledge sources across M365 SharePoint, Fabric IQ, OneLake, Azure Blob Storage, Azure AI Search indexes, the public web, and MCP servers (in private preview), then automatically managing ingestion, chunking, vectorization, and hybrid retrieval across all of them through a single knowledge base endpoint. Query planning and agentic retrieval let a single query span multiple sources without the calling application needing to implement per-source retrieval logic itself. Governance is a genuine differentiator here, and it connects directly to the same security and compliance discipline that matters everywhere else in your infrastructure, Purview sensitivity label integration means classification signals stay attached to content through the retrieval pipeline, and elevated-access operations on Purview-enabled indexes are audited by default. For regulated industries specifically, that audit trail is often the difference between a RAG deployment that passes a compliance review and one that doesn’t.

Status is worth being precise about rather than rounding to a simple “GA” or “preview”: core capabilities are generally available through the Azure AI Search REST API version 2026-04-01, but the more advanced features, answer synthesis, higher reasoning effort settings, several source kinds, still require the 2026-05-01-preview API, and the Foundry and Azure portal experiences remain preview across the board. Real customer results are already public: Microsoft states AT&T’s use of Azure AI Search and RAG in a multi-agent framework reduced customer resolution times by 33%, cut average handle time by nearly 10%, and scaled 71 AI solutions to 100,000 employees. Ontario Power Generation used agentic retrieval across more than 40 years of nuclear operating experience for institutional knowledge access.

What Is Google RAG Engine?

Google’s RAG Engine now sits inside the Gemini Enterprise Agent Platform, the April 22, 2026 reorganization of what was Vertex AI, as one of several components under the platform’s “Build” section, alongside Model Garden and a separate Vector Search tool. Officially, it’s “RAG Engine on Gemini Enterprise Agent Platform”: a managed runtime for RAG orchestration and a data framework for building context-augmented LLM applications, ingesting files into a RAG corpus, generating embeddings, and handling retrieval and generation as connected but distinct steps.

Worth knowing operationally: RAG Engine currently requires allowlist access to use in several regions specifically, including us-central1, us-east1, and us-east4, existing projects aren’t affected, but new projects may need to work in other regions or request access. It’s also worth not treating “Google RAG Engine” and “Google enterprise search” as interchangeable, RAG Engine is the orchestration layer for retrieval-augmented generation specifically, while broader search capabilities (covered in our companion piece on Amazon Kendra vs Azure AI Search vs Google enterprise search) are a related but architecturally separate concern within the same platform.

RAG Architecture, Vector Search, and Hybrid Search

All four handle the same core pipeline, ingestion, indexing, retrieval, augmentation, generation, but differ in where the boundaries sit. Bedrock Knowledge Bases and Google’s RAG Engine both bundle most of this into a single managed flow. Azure AI Search provides the retrieval infrastructure as its own layer, with Foundry IQ adding the orchestration and multi-source query planning on top. All four support vector search with embeddings and similarity matching; Azure AI Search’s hybrid search (combining keyword, vector, and semantic ranking in one query) is the most explicitly configurable of the four, reflecting its longer history as a dedicated search platform rather than a RAG feature retrofitted onto one.

Agentic Retrieval and Enterprise Knowledge Sources

Agentic retrieval, decomposing a complex query into sub-queries, retrieving across multiple sources in parallel, and synthesizing results, is where Foundry IQ’s design is most explicit, given it was built specifically for multi-source, agent-facing retrieval from the start. Bedrock Agents provides comparable capability within the AWS ecosystem. Azure AI Search’s knowledge sources and knowledge bases concepts are the building blocks Foundry IQ orchestrates. Google’s Agent Platform provides its own broader agent-building tools that RAG Engine connects into. Knowledge source breadth varies meaningfully: Foundry IQ’s native reach into SharePoint, Fabric IQ, OneLake, and the web alongside Azure AI Search itself is currently the widest out-of-the-box source coverage among the four.

Security, Permissions, and Citations

All four support permission-aware retrieval in some form, but the depth differs. Foundry IQ’s Purview sensitivity label integration is a genuine standout, classification signals travel with content through retrieval and generation, and elevated access on protected indexes is audited automatically. Bedrock Knowledge Bases and Google’s RAG Engine both support access control appropriate to their respective cloud’s identity systems. For healthcare or other regulated content specifically, permission-aware retrieval is necessary but not sufficient, the underlying infrastructure still needs to meet the relevant compliance standard independently. None of the four should be treated as a complete substitute for reviewing your actual document permission architecture independently, retrieval-layer access control is a real safeguard, not a guarantee against a misconfiguration further upstream. All four support citations in generated responses, which helps with traceability but doesn’t eliminate hallucination risk entirely, a model can still misread or selectively ignore retrieved context even when citations are present, which is exactly the kind of failure mode our Bedrock Guardrails vs Azure AI Content Safety vs Google Model Armor comparison covers from the output-safety side.

Which Platform Is Best for Enterprise RAG?

Choose Amazon Bedrock Knowledge Bases when: AWS is your primary cloud, you’re already using Bedrock for model access, and you want a single managed RAG capability without assembling separate infrastructure and orchestration layers.

Choose Azure AI Search (with or without Foundry IQ) when: you need fine-grained control over search infrastructure specifically, custom relevance tuning, complex filtering, hybrid search configuration, and are comfortable working closer to the retrieval layer directly.

Choose Foundry IQ when: Microsoft Foundry is central to your agent architecture, you need agentic retrieval across multiple enterprise knowledge sources without building that orchestration yourself, and Purview-based governance matters for your compliance posture. Remember this requires Azure AI Search underneath it, not instead of it.

Choose Google RAG Engine when: Google Cloud and the Gemini Enterprise Agent Platform are your primary environment, Gemini model integration matters, and you’re comfortable with the current allowlist constraints in certain regions.

Cost and Operational Considerations

None of the four publish pricing simple enough to reduce to a single number worth repeating here, cost depends on indexed data volume, storage, retrieval query volume, embedding generation, LLM token consumption for generation, reranking operations where used, and in Google’s case, the underlying vector database instance (a RAG Engine-managed Spanner instance, for example, bills separately in GA locations). Engineering effort is a real, often underweighted cost too, Bedrock Knowledge Bases and RAG Engine both trade some of that for managed simplicity, while Azure AI Search plus Foundry IQ trades more setup complexity for retrieval control and multi-source orchestration. This is the same cost forecasting discipline that applies to any consumption-based cloud service, modeling actual expected volume against current rates, not budgeting off a rough estimate. Verify current official pricing before budgeting, since all four platforms adjust pricing and capabilities on an ongoing basis, particularly Foundry IQ, given its current mixed GA/preview status.

Pros and Cons

Amazon Bedrock Knowledge Bases

Pros: Single managed pipeline; supports both managed and customer-managed vector stores; now the clear AWS-recommended path following Kendra’s closure to new customers. Cons: Deepest value assumes AWS-native data and Bedrock model usage.

Azure AI Search

Pros: Most mature, configurable search infrastructure of the four; strong hybrid search; foundation for Foundry IQ. Cons: Requires more direct engineering investment than a fully managed RAG product; not itself a complete RAG orchestration layer.

Foundry IQ 

Pros: Strong agentic, multi-source retrieval; genuine governance depth via Purview; real published customer results. Cons: Mixed GA/preview status means some of the most valuable features aren’t fully stable yet; requires Azure AI Search as a prerequisite, not a standalone choice.

Google RAG Engine 

Pros: Tight Gemini integration; managed orchestration within a broader agent platform. Cons: Allowlist access required in several regions currently; naming and platform structure changed significantly and recently, so third-party documentation is inconsistent right now.

Real-World Enterprise Use Cases

Enterprise knowledge assistants and internal document search benefit most from strong permission-aware retrieval, Foundry IQ’s Purview integration and Bedrock Knowledge Bases’ access controls both matter directly here. Customer support copilots, like AT&T’s deployment, lean on agentic retrieval across multiple knowledge sources simultaneously. Legal and financial research assistants need strong citation and grounding support across all four platforms, paired with human review given none eliminate hallucination risk completely. Developer knowledge assistants and RAG-powered SaaS applications generally favor whichever platform’s API and SDK ecosystem the engineering team already knows, since implementation velocity often matters more than marginal retrieval-quality differences at this stage, though testing retrieval quality and defect rates systematically still matters before any of these ships to production. Once live, continuous monitoring of retrieval latency and relevance drift is the same operational discipline any production AI system needs, not something a managed RAG platform exempts you from.

How to Choose the Right RAG Platform

1. Which cloud is your primary infrastructure already on?
2. What data sources actually need to be searchable, documents, SharePoint, databases, the web?
3. Do you need agentic, multi-source retrieval, or is single-source search sufficient?
4. How complex are your document-level permission requirements?
5. Do you need vector search, hybrid search, or both?
6. What’s your realistic engineering capacity for building versus using managed retrieval?
7. Are your requirements sensitive to preview-status features, or do you need full GA stability?
8. What’s your total cost of ownership, including engineering time, not just platform fees?

Conclusion

The most important thing to understand about this comparison isn’t which platform “wins”, it’s that these four aren’t peers of each other in the way the question implies. Amazon Bedrock Knowledge Bases and Google’s RAG Engine are both complete, managed RAG pipelines. Azure AI Search is retrieval infrastructure, and Foundry IQ is the managed orchestration layer built specifically on top of it, not a fourth alternative, but the second half of a two-part Microsoft answer. AWS-first organizations should default to Bedrock Knowledge Bases, especially now that it’s the clear successor path after Kendra’s closure to new customers. Microsoft-first organizations need to budget for both Azure AI Search and Foundry IQ together, with a clear eye on Foundry IQ’s current mixed GA/preview status. Google Cloud-first organizations should build on RAG Engine within the Gemini Enterprise Agent Platform, accounting for the current regional allowlist constraints. Whichever direction fits, prioritize permissions, agentic retrieval depth, and observability over raw feature-list comparisons, those are the things that actually determine whether a RAG system holds up in production.

If you’re architecting enterprise RAG and want a second opinion grounded in real implementation experience across all three clouds, Triotech Systems can help you scope the right combination for your actual data and compliance requirements, reach us through our contact page.

Frequently Asked Questions

What is the difference between Amazon Bedrock Knowledge Bases and Azure AI Search? 

Bedrock Knowledge Bases is a complete managed RAG capability, ingestion through generation in one pipeline. Azure AI Search is retrieval infrastructure specifically; it needs to be paired with an orchestration layer (like Foundry IQ) or your own custom logic to become a complete RAG solution.

Is Foundry IQ the same as Azure AI Search? 

No. Foundry IQ is built on top of Azure AI Search, not an alternative to it. Azure AI Search provides the underlying indexing and retrieval infrastructure; Foundry IQ adds a managed knowledge layer with agentic, multi-source retrieval on top of that same infrastructure.

Is Amazon Bedrock good for RAG? 

Yes, Bedrock Knowledge Bases is AWS’s purpose-built managed RAG capability, supporting both managed and customer-managed vector stores, reranking, and citation-backed generation, and it’s now the primary AWS-recommended path following Amazon Kendra’s closure to new customers.

What is Google RAG Engine? 

RAG Engine on the Gemini Enterprise Agent Platform is Google Cloud’s managed runtime for retrieval-augmented generation, handling document ingestion, embedding, retrieval, and generation as part of the broader Gemini Enterprise Agent Platform (the 2026 successor to Vertex AI).

Which is better for enterprise RAG: AWS, Azure, or Google? 

None is universally better, it depends on your existing cloud ecosystem, data source requirements, and how much custom retrieval logic you want to build versus use managed. AWS favors Bedrock Knowledge Bases, Azure requires Azure AI Search plus Foundry IQ together, and Google Cloud favors RAG Engine.

What is the difference between RAG and enterprise search? 

Enterprise search returns relevant documents or passages for a human to read. RAG uses that same retrieval step as an input to an LLM, which then generates a synthesized, grounded response, retrieval is a component of RAG, not a separate competing category.

Does Azure AI Search support vector and hybrid search? 

Yes, natively, vector search, keyword search, and a semantic ranking layer can all combine in a single hybrid query, which is one of Azure AI Search’s most mature capabilities relative to the other platforms.

Does Foundry IQ use Azure AI Search? 

Yes, directly and necessarily, Foundry IQ’s knowledge bases are objects that live on your Azure AI Search service. You can’t deploy Foundry IQ without Azure AI Search underneath it.

Can RAG systems enforce document-level permissions? 

Yes, to varying degrees across all four platforms. Foundry IQ’s Purview sensitivity label integration is currently the most granular governance implementation among the four, with audited elevated-access operations built in by default.

Which RAG platform is best for AI agents? 

Foundry IQ was specifically designed for agent-facing, multi-source retrieval and currently has the broadest native knowledge-source coverage. Bedrock Agents and Google’s Agent Platform both provide comparable agentic capability within their respective ecosystems.