Glean, the enterprise AI search company often called “the Google for work,” just told TechCrunch AI it has crossed $300 million in annual recurring revenue. That’s triple the $100 million it reported only 15 months ago. According to TechCrunch AI, the seven-year-old startup is speeding up at the exact moment the biggest names in tech are crowding into its market.
What stands out here is the timing. For most of its life, Glean had the category to itself. “The first four or five years of our existence, we had no competition,” CEO Arvind Jain told TechCrunch. Now the list of rivals reads like a who’s who of AI: Google, Microsoft, OpenAI, Anthropic, Salesforce, and Atlassian are all building Glean-like tools. And Glean is growing faster anyway.
What Glean actually does
Glean connects to a company’s internal software (think Slack, email, docs, CRM, ticketing systems) and learns how the business works. It then lets employees and AI tools search across all of it. Jain’s term for this layer of business knowledge is the “context graph,” a phrase that’s catching on across enterprise AI.
The pitch is simple: an AI assistant is only as good as what it knows about your company. Glean’s argument is that it knows more, because it’s wired into the systems where the work actually happens.
The real selling point: cutting the AI bill
Here’s the part worth paying attention to. Jain says the context graph doesn’t just make AI smarter, it makes AI cheaper to run.
“If you connect your AI to Glean, it gives you all the information that you need to do your work, and that results in AI consuming far fewer tokens compared to if you unleash AI onto your systems directly,” he told TechCrunch. The logic: when the model already has the right context handed to it, it performs fewer operations and burns fewer tokens to get an answer.
Why this matters: a lot of companies are blowing past their AI budgets right now. Token costs add up fast when you point a model at messy internal data and let it dig around. Jain says trimming that bill has become one of Glean’s biggest draws. “One of the things you know our customers really like about Glean is the fact that we can reduce your AI bill significantly,” he said.
That reframes Glean from “another search tool” into a cost-control layer for enterprise AI. In a market where every vendor promises more capability, selling lower spend is a sharp angle.
The fine print on that $300M
One honest caveat, flagged by TechCrunch AI itself. Glean offers a few pricing models:
- A consumption model, where customers pay per use
- A hybrid model, combining a fixed monthly fee per active user with separate usage fees for model consumption
Because part of the revenue comes from usage that rises and falls, the $300 million can’t be called pure ARR in the traditional sense. Recurring revenue implies predictable subscription renewals. Consumption revenue depends on how much customers actually use the product month to month. So a slice of that top line is more accurately an annualized run rate than locked-in recurring revenue. Glean is far from the only company that blurs this line, but it’s worth knowing when you read the headline number.
Who’s buying and what it signals
Glean’s customer list includes Databricks, Reddit, Pinterest, and Samsung. The company was last valued at $7.2 billion after a $150 million Series F last June.
The bigger signal for the industry: enterprise AI search has gone from a niche nobody contested to a battleground everyone wants. Jain believes being first matters, but says the real edge is having a better product, and right now he’s pointing to deep business context and lower token costs as the proof.
For practitioners, the takeaway is practical. As AI spend gets scrutinized, expect more vendors to compete on efficiency, not just capability. The question buyers will keep asking is whether a context layer like Glean’s saves enough on tokens to justify its own price tag.
Glean did not immediately respond to TechCrunch’s request for comment. More detail is available at the original TechCrunch AI report.