Best Enterprise RAG Platforms in 2026: How to Choose

Quick picks

“Enterprise RAG platform” covers three different purchases. An assistant product gives employees a chat and search box over company knowledge. A cloud building block gives your engineers a retrieval API for an application you build. A graph layer adds a knowledge graph for questions that plain retrieval answers badly. Decide which one you are buying before you compare vendors. Product facts come from each vendor’s official documentation as of November 2026, under current product names.

Three kinds of platform, and what you still own

All three run the pipeline described in our RAG architecture guide: connect sources, index them, retrieve passages, and have a model write a cited answer. They differ in how much of it the vendor operates.

Enterprise search and assistant products

These fit when the user is an employee asking about internal knowledge. You configure connectors and identity; the vendor runs the rest.

Glean: best when knowledge is spread across many SaaS tools

What it is: a work assistant and enterprise search product. Glean’s connector page lists 275+ native and MCP-based connectors.

Permissions: the same page states that data permissions are inherited from the source and enforced, so users only see what they are already allowed to see. For sources without a native connector, an Indexing SDK lets you push documents together with their permissions.

Deployment: Glean’s documentation describes two models. Glean Hosted is SaaS in Glean’s cloud. Customer Hosted (previously Cloud-Prem) runs a single-tenant instance inside your own Google Cloud or AWS account, still operated by Glean; you cannot patch or alter it yourself.

Who it suits: companies with knowledge in dozens of tools and no single dominant suite.

Microsoft 365 Copilot: best for a Microsoft 365 tenant

What it is and connectors: Microsoft’s assistant grounded in Microsoft Graph: mail, chats, meetings and documents. Microsoft’s documentation now calls the app Microsoft Copilot; grounding in work data comes with the Microsoft 365 Copilot (Premium) license. Outside systems come in through Microsoft 365 Copilot connectors, in two models according to Microsoft Learn. Synced connectors index external content into Microsoft Graph. Federated connectors fetch content in real time over the Model Context Protocol without indexing it into Microsoft 365, which Microsoft positions for live or sensitive sources that shouldn’t be indexed. Microsoft offers over 100 prebuilt connectors.

Permissions and data location: Microsoft’s documentation says Copilot only surfaces organizational data the individual user has at least view permission for, and that prompts, responses and Graph data are not used to train foundation models. Because Copilot follows existing permissions, overly broad SharePoint sharing carries straight into its answers. Microsoft describes Copilot as an EU Data Boundary service for EU customers, with one documented exception: Claude models from Anthropic, used as a subprocessor, are currently excluded from the EU Data Boundary.

Who it suits: organizations whose documents and identities are already in Microsoft 365 and Entra ID.

Gemini Enterprise: best for Google Workspace companies

What it is: Google Cloud’s intranet search, AI assistant and agentic platform. Per Google’s release notes of October 2025, the Google Agentspace experience is now referred to as Gemini Enterprise.

Connectors: Google sources (Drive, Gmail, Calendar) and third-party sources such as SharePoint, OneDrive, Outlook, Jira Cloud, Confluence Cloud and ServiceNow. Where a connector supports both, you choose between ingestion, which copies data into the Gemini Enterprise index, and federation, which queries the source directly. Google’s documentation notes that federation avoids storing a copy, but search quality may be lower and your query string is sent to the third-party backend.

Permissions and data location: Google uses your identity provider to decide whether the searching user may see each returned document. That is Google Identity or, through Workforce Identity Federation, a provider such as Entra ID or Okta. Two settings are hard to undo: access control on a data store must be chosen at creation, and switching identity provider types means recreating data stores. Ingested data is stored in the region you select, such as global, US or EU.

Who it suits: Google Workspace organizations and Google Cloud customers.

Amazon Quick: the AWS option now that Q Business is closed to new customers

What it is: many shortlists still name Amazon Q Business. According to the AWS documentation, Q Business is no longer open to new customers; existing customers get bug fixes and security updates, and AWS recommends Amazon Quick for new work. AWS describes Quick as an AI assistant for work that connects documents, applications and databases.

Permissions: the Quick user guide documents document-level access control lists for knowledge bases on Amazon S3, Google Drive, SharePoint, Confluence Cloud and OneDrive. Quick enforces the synced permissions on every retrieval and, for supported integrations, re-checks access against the source when it returns results. If it cannot evaluate permissions for a query, it returns no documents. Plan for two details: the ACL setting is permanent once a knowledge base is created, and permission changes sync on the refresh schedule, every 24 hours by default.

Who it suits: companies already on AWS. If you run Q Business today, AWS documents a migration path that reuses your existing index.

Elastic: best when you need to self-host or tune search yourself

What it is: a search platform more than a finished assistant. Elastic’s documentation covers full-text, vector and hybrid retrieval for RAG and recommends its Agent Builder for chat over your data.

Permissions: access is controlled through role-based access control and field- and document-level security. Elastic’s connector documentation lists document-level security as available by default for a subset of connectors, including Confluence, Jira, Google Drive, SharePoint Online and Server, OneDrive, Salesforce and ServiceNow.

Deployment: the widest range on this list: Elastic Cloud Serverless, Elastic Cloud Hosted with your cloud provider of choice, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, or fully self-managed on your own premises.

Who it suits: teams with search engineers, strict hosting requirements, or an existing Elasticsearch estate.

Cloud building blocks

These fit when RAG is part of an application you build. You get managed indexing and retrieval; the interface, prompts and evaluation are yours.

Azure AI Search

According to Microsoft Learn, Azure AI Search offers two engines: classic search (full-text, vector, hybrid and multimodal queries against one index) and agentic retrieval, where a knowledge base plans a query, splits it into subqueries, retrieves from several knowledge sources in parallel and reranks. Microsoft lists agentic retrieval as generally available, with some capabilities in preview. Azure AI Search also underpins Foundry IQ, the knowledge layer for agents in Microsoft Foundry.

Read the fine print on permissions. The generally available approach is security filters: your application stores user or group identities in a string field and filters every query by the caller’s identity. Native enforcement from the user’s Entra token, for ADLS Gen2 ACLs and RBAC scopes, SharePoint ACLs and Microsoft Purview sensitivity labels, is marked preview in the documentation. Microsoft does not recommend preview features for production workloads.

Amazon Bedrock Knowledge Bases

The AWS documentation describes two types. A Managed Knowledge Base handles ingestion, embedding, storage, reranking and agentic retrieval for you, with connectors including Amazon S3, SharePoint, Confluence, Google Drive, OneDrive and a web crawler, and document-level permission filtering by access control lists at retrieval time (not for the web crawler). A customer-managed knowledge base lets you provision your own vector store, such as Amazon OpenSearch Serverless, Aurora PostgreSQL or Neptune Analytics, and choose your parser and search strategy. Document-level ACL filtering is a feature of the managed type, and since September 30, 2026, new Confluence, SharePoint, Salesforce and web crawler connectors can no longer be created on customer-managed knowledge bases.

RAG Engine and Agent Search on Google Cloud

Google renamed this part of its cloud: the documentation now places RAG Engine under Gemini Enterprise Agent Platform, and the product long known as Vertex AI Search is documented as Agent Search. RAG Engine ingests files from sources such as Cloud Storage and Google Drive into a managed corpus and retrieves context for the model. Agent Search provides APIs for building search and RAG experiences over public websites and over structured and unstructured data stores.

Google’s RAG Engine overview says VPC Service Controls and customer-managed encryption keys are supported, while data residency controls are not, and that some regions require allowlist approval for new projects.

Graph RAG for enterprise AI: when a knowledge graph helps

Plain RAG finds passages that look like the question. That fails in two situations. The first is a global question, such as “what are the recurring causes of delay across last year’s project reports”, where no single passage holds the answer. The second is a multi-hop question, where the answer requires following a chain: this contract references that supplier, who makes this component, used in those products.

A knowledge graph stores entities and the relationships between them, so retrieval can walk those links. The cost comes at indexing: a model has to read the corpus to extract the graph, and the graph must be kept current. For single-fact lookups, that expense buys nothing.

Microsoft GraphRAG

An open-source project from Microsoft, published under the MIT license. Per its documentation, it extracts entities, relationships and claims from text, clusters the graph into a hierarchy of communities, and writes a summary of each community. Queries then run in one of several modes: global search uses the community summaries for corpus-level questions, local search starts from specific entities and their neighbors, DRIFT search combines the two, and basic search is ordinary vector retrieval.

Two statements in the project’s README set expectations. Indexing “can be an expensive operation”, with advice to start small. And the code is a demonstration, not an officially supported Microsoft offering.

Neo4j GraphRAG

Neo4j publishes neo4j-graphrag, which its documentation calls the official first-party GraphRAG package for Python, with a commitment to long-term maintenance. It includes a pipeline for building a knowledge graph from documents and a set of retrievers. The vector retriever searches a Neo4j vector index and the hybrid retriever adds a full-text index; their “Cypher” variants then run a graph query to pull context around each match, and Text2Cypher translates the question into a graph query. It requires Neo4j 5.18.1 or later, or Neo4j Aura 5.18.0 or later. It suits teams that already model their domain as a graph.

To test the idea without running a graph database, Amazon Bedrock Knowledge Bases has a managed GraphRAG option on Amazon Neptune Analytics. Per the AWS documentation, Amazon S3 is its only supported data source and the graph build cannot be customized.

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How to evaluate an enterprise RAG platform

Vendor demos run on clean data with one user. Your pilot should run on your messiest source with at least three users who have different access.

1. Test permissions before answer qualityPick documents that only some pilot users may see and ask about them from each account. Then revoke one person’s access and measure how long the platform keeps answering from that document.
2. Map connectors to your real sourcesList every system that holds knowledge people ask about. For each, check that a connector exists, whether it indexes or federates, whether it carries permissions, and how often it syncs.
3. Settle data residency in writingFind out where the index is stored, where model calls are processed, and which subprocessors are involved. Exceptions sit in the documentation, as the Microsoft example shows.
4. Measure quality with your own questionsAs a starting point, collect 50 to 100 real questions with known correct answers, including ones the system should decline. Score retrieval and answers separately. Our guide to RAG hallucinations covers what to look for.
5. Check admin and audit controlsYou need to see who asked what, exclude sources or sites, set retention for chat history, and restrict which models and agents are allowed.
6. Write the exit planThe index can usually be rebuilt from your sources. What does not move is connector configuration, tuned prompts, agents and user habits. Note which settings are permanent at creation, and keep your evaluation set outside the platform so you can test a replacement.

When to build custom instead

A platform is the right call when the users are employees, the sources are mainstream and a standard chat interface is enough: the vendor carries the connector maintenance. Build custom when one of these is true:

• RAG is a feature of the product you sell. The assistants above are designed for employees asking about internal knowledge. Your customers expect answers inside your interface with your tenant model.
• Answers need your business logic. If a correct answer combines documents with live data from your systems, pricing rules or account state, a general assistant will not get there by configuration.
• Your quality bar needs control. Platforms hide chunking, retrieval and ranking. When evaluation shows a retrieval problem, you can only fix it if you own those stages.
• No platform runs where your data must stay. Air-gapped or on-premises environments rule out most of this list.

The honest trade-off: a custom system gives you control and takes on everything in the white boxes of the diagram above, including permission sync, which is the hardest part to get right. A custom build can still sit on a building block from this list, so you rarely start from zero. If you go this way, see our RAG development services; the list of RAG development companies for enterprises compares vendors that do it.

FAQ

Where Gilzor fits

Gilzor does not sell or resell any platform on this list. We are a software development company with a team in Poland. We build custom RAG systems, add RAG to existing products, and evaluate RAG systems that are already running. If you are unsure whether a platform is enough for your case, our RAG consulting can help you decide before you commit to a license or a build, and a 2-week fixed-price Discovery Sprint is available to scope a custom system.