Glean Review - The Enterprise AI Platform Built On Your Own Data

Welcome to this Glean review ✨

Every company I've worked with has the same quiet problem. The answer exists. Someone wrote it down eighteen months ago, in a Confluence page, or a Slack thread, or a Google Doc nobody linked to. It's there. But finding it costs more than rewriting it, so people rewrite it. Then there are two answers, and the next person finds the wrong one.

General-purpose AI didn't fix this. If you paste your question into ChatGPT, you get a confident answer built on the public internet and nothing about how your company works. It doesn't know your product names, your on-call rotation, or that the pricing doc got superseded in March. Glean's bet is that the missing ingredient was never the model. It was context.

Glean is an enterprise AI platform that connects to your internal apps, indexes everything behind your existing permissions, and then runs three things on top of that index: search, an AI assistant, and agents.

One thing to say plainly up front, because it shapes this whole review. Glean has no free tier, no trial, and no self-serve signup. Every button on the site is "Get a demo." Sign-in goes to a corporate SSO screen. Even the interactive product tour is locked behind a lead-capture form asking for your work email and phone number. So I could not run Glean against a real corpus and tell you what it retrieved. Nobody outside a paying customer can.

What I could do is dig into the parts that are open, and Glean's are unusually open: a public connector directory, a public agent library, and technical documentation that publishes things most vendors bury, including a full consumption rate card and per-connector crawl schedules. For a product you buy on a sales call, that documentation tells you more than a scripted demo would.

The index is the actual product

It's tempting to look at Glean and see a chat box. The chat box is the least interesting part.

What you're really buying is the layer underneath: a permissions-aware index of your company, plus two graphs built on top of it. Glean calls them the Enterprise Graph and the Personal Graph, and the split is genuinely useful. The Enterprise Graph maps entities (people, projects, teams, products, customers, processes) and the signals hanging off each one. Their own example is a product entity carrying its documentation, its support tickets, and its feature specs. The Personal Graph does the same for you individually: which tasks you're on, who you owe things to, how fast you usually turn work around.

The point of building both is disambiguation. Glean's documentation uses the example of telling "Gemini 2.0 Flash" apart from "Gemini 2.0 Pro", the kind of distinction that pure semantic similarity fumbles and that wrecks an answer when it does. It's a good example precisely because it's unglamorous.

Connectors are what feed this, and there are a lot of them: 275+, spread across thirteen categories from Calendar to Single sign-on. Browsing the directory, each connector carries one of three badges (Native, MCP, or Push API) telling you how it's wired in. Slack, Jira, Confluence, SharePoint, Salesforce, GitHub, Workday, Notion, Databricks, ServiceNow: the enterprise long tail is well covered, and if yours isn't there you can build one with the Indexing SDK, add actions via an OpenAPI spec, or point it at an MCP server.

A small thing I noticed while clicking around: Glean's own site can't agree on the number. The body copy says 275+, the navigation menu says "more than 250," and the enterprise search page says "100+ connectors" and "100+ tools." Four figures, live at the same time. It's cosmetic, but it's the kind of thing you notice when you're reading closely.

Permissions are the part that matters most and that I want to be careful about. Glean inherits and enforces whatever your source systems already say, so nobody sees anything they couldn't already open. That's the right design. It also means Glean will faithfully surface your existing oversharing. If a sensitive channel is open to all staff, Glean will find it and hand it to someone who would never have thought to go looking. That isn't Glean doing something wrong. It's Glean showing you a permissions problem you already had, and it's the reason an access audit belongs in the rollout plan rather than after it.

Glean Assistant, and what "agentic" actually means here

The Assistant is the surface most employees will touch. Glean's framing is "your enterprise-ready AI coworker," which is the sort of phrase I usually skip past. But the documented mechanics behind it are more specific than the tagline.

A request runs through five named stages: Find, Plan, Research, Iterate, Deliver. Find sweeps your tools and the web. Plan breaks the request into a strategy and spins up sub-agents to work in parallel. Research sends out what Glean calls scouts to fill gaps the original question didn't anticipate. Iterate re-evaluates and asks clarifying questions. Deliver assembles the result.

The scouts idea is the one I'd want to test hardest if I had access, because it's where this either earns its keep or wastes your money. Chasing context the user didn't ask for is exactly how you get a genuinely better answer, and also exactly how a query balloons in cost. As you'll see in the pricing section, Glean meters that.

Around the Assistant sit the pieces you'd expect from a 2026 product: Deep Research for long-form reports, Canvas for editing and refining documents, slide and image generation, a code writer, meeting notes with near-real-time transcription, and a real-time voice mode. It runs in a browser app, a sidebar and companion, a desktop app, a mobile app, and embedded inside other tools.

Worth knowing: this is where I hit the wall. Clicking "Take a tour" opens a scripted walkthrough of a demo tenant, and before it plays you have to hand over your name, work email, company, company size, phone number and country, and accept marketing email. For a product this expensive that gate is defensible. It's still a gate on a marketing page, and it's the only route to seeing the interface without a sales call.

Agents: two kinds, and a library to start from

Agents are where Glean is pushing hardest, and where the design decision I liked most sits.

Glean splits agents into two types rather than pretending one shape fits everything. Workflow agents are built in a visual drag-and-drop builder and follow a fixed path. You use these when the process must run the same way every time. Autonomous agents take plain-language instructions and work out their own approach. Most tools in this space pick one philosophy and make you contort your use case around it; offering both, and being clear about which is which, is the more honest engineering choice.

The build flow is four steps: Describe, Connect, Refine, Deploy. You describe the agent in plain language and Glean drafts it; you connect data sources and APIs with permissions intact; you test outputs and adjust instructions and "creativity settings"; then you ship it. Every change is versioned automatically, with rollback, which sounds boring until an agent that's live for 4,000 people starts behaving differently and you need to undo it.

The Agent Library is the part you can actually browse today, and it's the most concrete thing on the whole site. Around thirty prebuilt agents, tagged by department. A few that show the range: SEO article evaluation audits a blog post against a 21-point helpful-content checklist and returns a scorecard. Resolve Jira ticket turns an issue into a review-ready pull request plus a summary of what changed. Delegation tracker surfaces every ask you've made that never got a reply. Deal loss insights works out why you're losing deals.

I like this library more than I expected to. Not because the agents are clever, since several are things you could prompt your way to in an afternoon, but because a named, department-tagged catalogue is how you get non-technical staff to adopt any of this. "Here are thirty things that already work, pick one" beats a blank text box every time. Glean's own claim is value in "days, not months," and starting from templates is the only version of that claim I find plausible.

The flip side, which their customer numbers hint at without meaning to: Zillow has 3,400+ agents, Ericsson 2,700+. That's impressive adoption and also a governance problem in waiting. Glean clearly knows it. There's a whole observability layer tracking adoption, error rates, upvotes, downvotes and ROI per agent, and a certification flow for blessing the good ones. You will need it.

The rest of the platform

A quick roundup of what else is in the box:

• Model Hub: around 34 models across Amazon Bedrock, Azure OpenAI, Google Vertex AI and OpenAI. Claude, Gemini, GPT, Llama, DeepSeek, Nova. A "universal model key" gets you across clouds without managing four vendor relationships, and models are tiered Basic / Standard / Premium, which matters for your bill.
• APIs: Search, Chat and Agents APIs, plus a web SDK and headless MCP support. Index once, use it anywhere, including inside products you build yourself.
• Glean Protect: the security layer, covering sensitive-content detection, oversharing triage, acceptable-use topic blocking, and "alignment models" that pre-scan every write action before it executes.
• Deployment: single-tenant only. Either Glean-hosted, or in your own cloud with full data residency. Certifications include SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, GDPR and TX-RAMP Level 2. No FedRAMP, if that matters to you.
• Zero-retention agreements with the model providers, so your data isn't used for training.

Pricing: no public price, but a very public rate card

Glean does not publish prices. glean.com/pricing redirects to the homepage. Everything routes through sales.

But here's the most interesting thing I found: Glean does publish the structure and the full consumption rate card in its documentation, which almost nobody in enterprise software does. It's worth understanding before you take the call.

The model is called Enterprise Flex, and it's a hybrid. You buy Enterprise Flex Seats per user per month, meant to be deployed to everyone. Seats include unlimited Fast Mode queries, unlimited collections and go-links, and unlimited agent creation, testing and sharing. On top sits a pooled, org-level allowance of FlexCredits that covers the expensive stuff.

The dividing line is reasoning. Thinking Mode with standard models is included up to 100 queries per user per week; past that, and for any premium model, you're spending credits. Here is Glean's own published consumption table, in FlexCredits per operation:

Two rows deserve a second look. Slide generation is one of the most expensive things on the list, well above Deep Research at the median. And a Glean Agent run swings from ~7 to ~114, a 16x spread that tells you agent costs are extremely hard to forecast before you have usage data.

Glean doesn't publish what a FlexCredit costs in dollars, so the table gives you relative cost, not absolute. Two more line items to ask about: Glean Protect+ and Premium Support are separately licensed annual fees on top.

For actual money, the best public data comes from Vendr, which brokers SaaS contracts and publishes anonymised transaction figures. Its Glean page, last updated in February 2026, puts the median contract at $98,890 a year across 174 purchases, with a range from $29,880 to $208,897 and average savings of 20%. Vendr also reports minimum commitments typically starting around 100 to 250 users, and annual price escalations of 3 to 7%. Treat the widely-repeated "$50 per user per month" figure with suspicion. It traces back to competitor blog posts, not to Glean or to any transaction data.

One genuine lever: Glean discounts both seats and credits if you bring your own LLM keys or self-host in your private cloud.

Who this is for

Glean makes sense if you're:

• A company of several hundred people or more, where knowledge is genuinely scattered across a dozen-plus systems
• Already paying for Slack, Confluence, Jira, Salesforce, Workday and the rest. The more connected systems, the better the graph
• In a regulated industry that needs single-tenant deployment, data residency and audit trails
• Serious about agents as an operating model, not just a chat box, and willing to staff the governance

If you're a ten-person team with everything in three tools, this isn't your purchase yet, and Glean isn't pretending otherwise: the minimum commitments start where the problem starts. That focus is a feature. Glean has built for the company where nobody can name every system that holds an answer, and it doesn't dilute the product trying to also serve the company where everyone can.

There's also a real question for mid-size companies, and it's worth asking out loud. Microsoft can bundle Copilot into M365 seats you already pay for. Glean has to be enough better to justify a separate line item, and its CEO says so himself. Arvind Jain told TechCrunch in May 2026 that after four or five years with no competition, "every single company in the world wants to be in this space." TechCrunch names Google, Microsoft, OpenAI, Anthropic, Salesforce and Atlassian among the entrants. Glean's answer is depth: better connectors, a real graph, single-tenancy, model choice. On the evidence that's a strong answer, and the same article reports the company tripled revenue to $300M while those giants were arriving.

Conclusion

Glean is a serious piece of infrastructure, and it's serious in the unglamorous places: permission inheritance, entity disambiguation, per-connector crawl schedules, versioned agents with rollback, a published consumption rate card. Those are the things that decide whether an enterprise AI platform survives contact with a real company, and Glean has clearly spent its years on them rather than on the demo.

It's expensive, and it's opaque about price until you talk to sales. It's also completely dependent on the quality of what you feed it, and that's the part buyers underestimate. On G2, where Glean averages 4.7 out of 5 across 168 reviews, the recurring criticisms aren't that the AI is weak. They're that results come back too broad, that filtering is thin, and that obsolete content has to be flagged by hand: one reviewer notes that tagging documents "remains a manual task." Glean is a very good lens. It cannot invent focus that your documentation never had.

If you're at the scale where this makes sense, Glean is worth the demo call.

What I liked:

• The two-graph design, separating what the company knows from how you personally work, is a genuinely smart split, and the disambiguation example shows they've thought past naive semantic search
• Splitting agents into deterministic workflow agents and reasoning autonomous agents instead of forcing one shape on every use case
• The Agent Library: thirty named, department-tagged agents is the right answer to "how do we get non-engineers to adopt this"
• Automatic versioning and rollback on agents, which sounds dull and is the difference between a pilot and production
• Real model choice across four hosting providers, with one key and no per-vendor contracts
• Publishing the full FlexCredit rate card and per-connector crawl intervals. That transparency is rare and I'd like more vendors to copy it

Things to keep in mind:

• No trial, no self-serve, no public pricing. Even the product tour needs your phone number and a marketing opt-in
• Real cost is high: Vendr puts the median contract near $99k a year, with minimums typically starting around 100 to 250 seats
• Agent run costs are wildly variable: ~7 credits typical against ~114 at the 90th percentile makes budgeting genuinely hard before you have usage data
• "Real-time" needs an asterisk. Incremental syncs run every 1 to 6 hours, but full re-crawls run on 6-hour to 28-day cycles depending on the connector, and initial indexing takes several days
• A large share of the flagship Glean Protect capabilities (oversharing triage, topic blocking, alignment models) are still labelled beta
• It inherits your permissions faithfully, which means it will also surface your existing oversharing. Expect to clean house first
• Output quality is capped by your content hygiene. On G2 the recurring complaints are broad results, thin filtering, and limited transparency over which sources an answer used

If your company's knowledge is real but unfindable, Glean is built for exactly that problem.