Why people search for glean alternatives after a search rollout
The usual sequence: a company buys enterprise search, people ask it questions, the answers are good, and then somebody asks it to do something. Update the stage on this opportunity. Reply to this ticket. Create the renewal task. That is the point where a team starts comparing products.
That moment is what most searches for glean alternatives are about. The reader wants AI to finish the job in the app, with the same access rules the person already has there. Search and tool calls are different products, and the honest first move is to say which one you are short of.
Glean's own pages are clear about what Glean does and more besides. Its enterprise search page calls Glean Search "AI-powered workplace search" and says "Results are real-time and permissions-aware, so everyone sees only what they should" (checked October 2026). Its Agents page says "Glean's agentic engine lets agents reason through tasks, plan next steps, and take action using enterprise context" (checked October 2026). Its Connectors page describes "human-in-the-loop controls that let you require approval before execution" for the actions agents take (checked October 2026). So this is not a post about whether Glean can act. It is a post about the five things to check in anything you compare it with.
| Where connectors come from | How a person signs in | How access is restricted | What the audit record holds | How it is hosted | |
|---|---|---|---|---|---|
| Glean | Native and MCP-based connectors, plus third-party MCP servers proxied through the Glean MCP Gateway | Each person uses their own Glean access; the Remote MCP Server is tenant-specific and permission-aware | Glean enforces permissions for every agent action; admins can disable MCP or hide the end-user Configurator | Not on the pages we checked | Not on the pages we checked |
| Elaichi | Connectors Elaichi authors and runs, plus native MCP connectors (vendors' own servers) | Each member connects their client once and approves their own OAuth grant at one address | Restriction rules name whole connectors or single tools, targeting a role or one member | Person, tool, connector, connection reached, classification, approval, surface, MCP client | Hosted by Elaichi at one endpoint, https://api.elaichi.ai/mcp |
The Glean cells come from Glean's own Connectors page, Remote MCP Server page, Agent governance page and "About Glean MCP server", all checked October 2026.
One note on counts before you put a number in a slide deck. Glean's connector counts differ by page: its enterprise search page says "With 100+ connectors" and its Connectors page says "275+ out-of-the-box connectors" (both checked October 2026). Quote a count only with the page it came from.
What to check in a Glean alternative, in order
Five checks, in the order that saves the most time. Each one eliminates products quickly.
1. Does it act in the apps, or only read them? Reading is the easy half. Ask for the list of write tools per app, by name, not a general claim about actions.
2. Does each call run as the person? This is the one that decides whether your access reviews still mean anything. A single service account that reaches everything is a different risk from each person's own sign-in.
3. Can you restrict one tool, not just one app? Most teams need the read tools of an app and a few of its writes, with the destructive ones closed. If the only switch is on or off per app, you will end up turning the app off.
4. Is there one audit log across apps, with the client named? Per-vendor logs exist. The question is whether anything joins them, and whether the record says which AI client made the call.
5. Does it work with the clients people already opened? If your team is in Claude, ChatGPT and Cursor today, a product that only works inside its own chat window is a migration, not an addition.
Where Elaichi fits on those five checks
Elaichi is a governed MCP control plane. MCP (the Model Context Protocol) is how AI clients discover and call tools; a governed control plane means every one of those calls passes through one place that checks access and writes it down.
On acting in apps: Elaichi's catalog carries connectors Elaichi authors and runs itself, plus native MCP connectors, which are vendors' own MCP servers that Elaichi publishes and governs. A connector's page in the console lists its tools and labels each one Read only or Makes changes, so the write surface is visible before you decide anything. On Stripe's page, searching "refund" shows Create a refund labelled Makes changes beside View refunds labelled Read only.
On per-person identity: every call made with an OAuth grant acts as the person who approved that grant, in the organization they picked. An OAuth grant is the standing permission a person gives one AI client when they connect it. Each member connects their own client once and approves their own grant. Elaichi looks up that person's permissions on each request rather than storing them in the token. For the app on the other end to see the person, the tools have to run on that person's own connections.
On per-tool restriction: a restriction rule names whole connectors, single tools, or both, and targets a role or one member. Allow rules and block rules merge per layer, and a block always beats an allow. An allow rule that names nothing blocks every connector and tool for its target. Its own summary reads "Allows nothing. Every connector and tool is blocked." If you are choosing between the two shapes, allowlists and blocklists compared walks through which fits which team.
On one audit log across all its connectors: an organization has exactly that. Each connected-tool call that reaches execution records the person, the tool's advertised and catalog names, the connector, the connection it reached, the tool's classification, how approval was given, the surface, and the MCP client's id when the surface is mcp. A live check on 2026-10-16 recorded reads Claude made as "Viewed charges in Stripe" and "Viewed customers in Stripe", with "via Claude" on those reads.
On the clients people use: Claude, ChatGPT and Cursor all sign in to the same address, https://api.elaichi.ai/mcp. There is no per-client or per-team URL. Which organization and which person a client reaches comes from the grant behind its token, not from the URL.
Each of those three vendors documents its own side of the connection. Anthropic's custom connectors article says custom connectors that use remote MCP work in Claude, Cowork and Claude Desktop (checked October 2026). OpenAI's developer mode page says full MCP support in ChatGPT is rolling out in beta to the Business, Enterprise and Edu plans (checked October 2026); that support includes modify and write actions. Cursor's MCP documentation says Cursor supports OAuth for MCP servers that require it (checked October 2026).
Where Glean is still the better choice
Some cases do not improve by swapping products. Be honest about them.
If the actual problem is that nobody can find the document, you want search. Glean's enterprise search page calls Glean Search "AI-powered workplace search" and says "Results are real-time and permissions-aware, so everyone sees only what they should" (checked October 2026). That is a different engineering problem from running a tool call, and a control plane for tool calls will not give you workplace search.
If you need one place that both searches and acts, Glean's own pages describe both. Its Remote MCP Server page says "The Remote MCP Server is tenant-specific and permission-aware. Each person uses their own Glean access" and that "Administrators can disable MCP or hide the end-user Configurator" (checked October 2026). Its admin page "About Glean MCP server" lists "External MCP servers: Third-party MCP servers you proxy securely through Glean" among what a Glean MCP server can expose, "brought in through the Glean MCP Gateway" (checked October 2026). Put the two shortlists side by side rather than assuming a gap.
And if you have one team, two apps, and no compliance obligation yet, neither product is the next thing to buy. Hold off on a gateway until the count of apps or people makes a single control point worth running.
What the comparison costs you to run
Buy nothing on the strength of a page. Run the five checks against a real app in a real client, because that is where each product's shape shows.
Pick one app your team argues about. Connect it. Open the connector's page and read the Read only and Makes changes labels on its tools. Write one restriction, give it to one role, and try a blocked tool from the AI client. Then open the audit log and see whether the row names the person, the tool, the account and the client.
Two things to expect while you do it. A role, team or restriction change takes about two minutes to apply everywhere. Revoking a grant, removing a member and suspending one are effective on the next call, and so are revoking a share and disconnecting an account. And restrictions apply to the person making the call, which means a tool you block for a role is blocked for them inside every toolbox entry too, frozen arguments included.
For the broader shape of this decision, the four gateway shapes sorts the category, and the MCP security review checklist gives you ten questions to put to any vendor on this list. If you want to see the connectors themselves before any of that, the connector catalog is public.
