Best Glean Alternatives for Enterprise Search (2026)

Last updated: 3 October 2026

Quick Answer: Glean alternatives fall into four groups: suite copilots like Atlassian Rovo and Microsoft Copilot, AI knowledge base tools, managed retrieval services, and self-hosted search. Permission-aware retrieval decides between them, because the tool has to mirror every source system's access rules at query time. Connector coverage and deployment mode separate the rest.

Glean set the pattern for enterprise AI search, and most teams shopping for an alternative are reacting to its packaging, deployment model or permissions handling.

The search for Glean alternatives usually starts with a budget or a security review. Either way the shortlist is wider than it looks, because four product categories now answer the same question: let staff ask in plain language and get an answer from the company's own documents, tickets and wikis. They price, index and fail differently.

What are the best Glean alternatives in 2026?

The strongest alternatives sit in four groups: suite copilots that ship with software you already pay for, AI knowledge base tools that own the content as well as the search, managed retrieval services you assemble in your own cloud account, and self-hosted search.

Which group fits is decided before features. A company standardised on Atlassian or Microsoft 365 chooses between the bundled copilot and a neutral layer spanning both. One whose security team will not send document text to a vendor's cloud chooses between a managed service in its own account and open source it can run air-gapped.

Glean's connectors page documents "275+ apps across ecosystems out of the box" and states that "all data permissions are inherited and strictly enforced, so users only see what they're allowed to". Searches for the Glean AI platform mean that combination, not the chat window on top.

Capabilities as documented by each vendor on 2 October 2026 (Origins row, 3 October 2026); links in the text. Origins AI, which publishes this page, is included as one of the compared providers.

How do Glean, Confluence AI (Rovo) and Microsoft Copilot compare?

Glean is neutral across your tool estate, Rovo is strongest inside Atlassian, and Microsoft Copilot is strongest inside Microsoft 365, which is why the answer follows whichever suite already holds most of your documents.

What the Glean AI tool does today

The Glean AI tool is an index plus an assistant plus an agent builder on one permissions model. Buyers evaluating enterprise AI search care about the index far more than the chat interface, because a polished assistant over a partial index produces confident answers about half your company.

Atlassian's documentation says Rovo search combines Jira and Confluence results "with connected third-party apps (like Google Drive and Slack)", and that "Rovo respects your user permissions". Rovo credits are included in all paid Jira, Confluence, Service Collection and Teamwork Collection cloud subscriptions, so it is the easiest entry for an Atlassian shop to trial. We cover that pairing in our Confluence AI knowledge base comparison.

Microsoft documents Copilot as grounded in organizational data through Microsoft Graph with "access scoped by user permissions", and Copilot Search as a universal search across Microsoft 365 applications and connected non-Microsoft data sources. The catch is licensing: automatic grounding in work data belongs to the Premium add-on, and lower tiers answer from the public web unless you attach the file. Searches for glean vs copilot, or glean vs claude, compare one product against a licence tier.

Which Glean alternatives can be deployed in your own cloud?

Three options run inside infrastructure you control: Amazon Bedrock Knowledge Bases in your own AWS account, Onyx as self-hosted open source, and the Origins AI (originshq.com) Velocity AI Suite deployed into your environment by an implementation team. The rest of the shortlist is vendor-hosted.

Onyx's repository describes an "air-gappable, self-hosted deployment where the document index, database, and processing all happen within a self-contained set of services", released under the MIT licence (Community Edition), indexing 50+ applications. Upgrades, embedding costs and index tuning are yours, an engineering commitment rather than a configuration task.

Bedrock's managed form keeps ingestion and the index inside your AWS account while AWS operates the pipeline; the customer-managed form hands you the vector store, naming Amazon OpenSearch Serverless, Aurora and Neptune. A Glean AI assistant built this way is a component, not a product: the chat surface, evaluation harness and admin tooling are yours to build.

Which alternatives keep permissions and decision context intact?

Glean, Atlassian and Microsoft all state that their answers respect existing source or tenant permissions, and Guru lists permission-aware AI as a security feature.

Managed retrieval is where the gap appears. AWS documents "document-level permission filtering using Access Control Lists (except for Web Crawler) at retrieval time" for Managed Knowledge Bases, and says third-party connectors and document-level permissions "are only available for Managed Knowledge Bases". Build the customer-managed version instead, and access control becomes your code.

Decision context is the other half. Much of what a company knows sits in the thread where a decision was made and the ticket where it was reversed, not in a document, so tools that index wikis and files but not conversations miss the questions that matter. Where answers depend on how facts connect, retrieval design matters more than connector count, the argument in our note on GraphRAG versus plain vector retrieval.

Which AI knowledge base tools (Guru, Slite, Document360) can stand in for Glean?

They can stand in when the content problem is bigger than the search problem. These tools own the knowledge as well as the index, so they fix the stale documentation that is usually the real reason search answers are wrong.

Guru positions itself as "The Governed Knowledge Layer for Enterprise AI", structuring and verifying knowledge so other AI tools can consume it. Slite describes an AI knowledge base that stays accurate, synced with your tools and verified by your team, with an agent that detects knowledge drift. Document360 advertises 60+ connectors, an Eddy AI search that cites every source, and an MCP server connecting its knowledge base to external assistants. Intercom Fin is the outlier: its own site reports a 76% average resolution rate across 12,000+ customers, on support conversations rather than internal search.

Glean AI agents and agentic knowledge tools

Glean AI agents are why many evaluations restart halfway through: buyers who began by comparing search indexes end up comparing agent builders, governance consoles and model routing instead. Separate searches for glean agentic ai and glean ai search reflect that split. If you are scanning lists of Glean AI competitors, sort them by whether the agent can act in another system: retrieval-only and action-taking tools are priced and governed differently.

Choose a knowledge base tool when your documentation is the weak link, and a search layer when the documents are fine but scattered.

How do you run a 30-day pilot of a Glean alternative?

Run one department, one question set and one scorecard. A 30-day pilot on real content beats a six-vendor bake-off on sample data, because the failure modes only appear on real permissions and real mess.

1. Pick 40 questions your team knows the answers to, including five only a permissioned subset should see.
2. Connect one department's real sources. Do not clean the data first: stale and duplicate documents are the test.
3. Score every answer correct, incomplete or wrong, and log each citation. An answer without a traceable source is not correct.
4. Run the permission tests from two accounts with different access, and confirm neither sees the other's restricted material.
5. Measure the admin load, then price the shortlist at your real user and query volume.

Glean AI pricing is quoted per organisation rather than published, so a written quote at your actual headcount belongs inside the pilot. Each glean pricing model variant scales differently: suite copilots charge per licensed user, managed retrieval charges on ingestion and queries, and open source moves the cost into infrastructure and engineering time. Ask every vendor for its security documentation and data-retention terms.

When is Glean the right choice?

Glean is the right choice when you run many cloud applications, have no appetite to operate search yourself, and treat company-wide search as core infrastructure. Its connector breadth is the widest documented on this shortlist, and the permission inheritance is stated plainly rather than implied.

Its platform page describes "multi-cloud support, flexible model choice, and integrated security partners", but a customer-hosted or air-gapped deployment is not documented there as of 2 October 2026. If your security review requires document text and embeddings to stay inside your own network, the shortlist moves to the self-hosted group.

What mistakes should you avoid when replacing Glean?

The expensive mistake is buying a chat interface and assuming the index behind it is equivalent.

Testing on clean sample data hides every real failure. Treating a knowledge base tool and a search layer as interchangeable produces a tool that indexes nothing new. Skipping the permission test until after procurement causes incidents, because an oversharing index turns a quiet access-control problem into an answerable question. Counting connectors instead of checking which of your systems are covered leaves the sources your team asks about most outside the index. And nobody owns the index after launch unless you decide, before the pilot, who removes wrong content.

How Origins AI Velocity AI Suite handles enterprise knowledge

The Origins AI Velocity AI Suite is the self-hosted option that arrives with an implementation team rather than documentation. Its product page describes a modular enterprise AI suite for chat, voice, retrieval and embedded AI, built for mid-size companies, in three layers: Knowledge Foundation for ingestion, AI Core for retrieval and model orchestration, and Experience Delivery for the delivery surfaces.

The ingestion layer compares directly with the tools above. The page lists 1900+ data sources and 91+ document formats, compatibility with Pinecone, Chroma and Weaviate, connections to SQL, Postgres and MongoDB, and bring-your-own models across OpenAI, Anthropic and open-source options with no lock-in at the model layer. That one knowledge layer feeds a private assistant and embedded support experiences rather than a separate index per channel.

Deployment is what matters to a security reviewer. In on-premise and private-cloud deployment modes, retrieval and inference run inside your own network boundary; in hybrid mode the submitted context goes to the hosted model, so it is a per-use-case decision. The product page lists private storage options for every deployment model, plus rollout support from pilot to production. There is no published rate card.

Talk to an engineer

Share your main content sources and the questions your team keeps asking, and we will map a 30-day pilot plan against the shortlist above, including the permission tests most evaluations skip. Book a call.