Glean is an AI-powered enterprise search platform. It indexes the SaaS applications a company already runs, inherits each tool’s existing access permissions, and returns ranked results and generated answers from a single search box. Founded in 2019 and now organized around an assistant and a library of AI agents, Glean sells one core premise: the context that makes workplace AI useful is already scattered across Slack, Google Drive, Jira, Confluence, SharePoint, and Salesforce, and the job is to connect it rather than re-enter it. Here is what the product does, what it costs, and where it sits against Algolia, Coveo, Elastic, and Microsoft.
A company built by search people
Glean Technologies was founded in 2019 by Arvind Jain, who spent more than a decade at Google as a distinguished engineer working on Search, Maps, and YouTube, and who later co-founded the data-security company Rubrik. Glean’s own about page describes the founding group as "a seasoned team of former Google search engineers and industry veterans." That is close, with one correction worth making: co-founder Tony Gentilcore is genuinely ex-Google, where he led the Chrome Speed Team, while co-founder and CTO T.R. Vishwanath came from Facebook and Microsoft. The search pedigree is real. It is not uniformly Google.
The company is headquartered in Palo Alto with a San Francisco office, and its funding history is steep. CNBC reported a $260 million Series E in September 2024 at a $4.6 billion valuation, co-led by Altimeter and DST Global. TechCrunch reported a $150 million Series F in June 2025, led by Wellington Management at a $7.2 billion post-money valuation, taking the total raised since early 2024 to roughly $610 million. No later round has been reported through mid-2026, so $7.2 billion remains the last public mark.
Revenue moved with the valuation. Glean announced crossing $200 million in annual recurring revenue in December 2025 and $300 million in May 2026. One caveat belongs next to that number: TechCrunch noted that because part of Glean’s business is consumption-priced, the $300 million figure is closer to an annualized run rate than to strictly recurring revenue, and Glean did not comment on the characterization.
What Glean actually does
Strip away the "Work AI" branding and the core is federated, permissions-aware indexing. Glean connects to source systems through connectors, pulls content and metadata into its own index, and keeps that index current in near real time. Three layers sit on top:
- Hybrid retrieval: keyword matching combined with vector search, so a natural-language question can surface a document that never uses the query’s exact words.
- A knowledge graph: Glean builds what it calls a personal graph and an enterprise graph, modelling people, content, and interactions. Results rank differently for a support engineer than for a recruiter, and when no document exists the system can point at a likely subject-matter expert instead.
- Grounded generation: summaries and direct answers assembled from retrieved material, which is retrieval-augmented generation pointed at internal content rather than the open web.
If the shape sounds familiar, it should. This is the federated search pattern with a language model attached to the retrieval layer, and most of the engineering difficulty lives in the connectors and the permission model rather than in the generation step.
The Glean product surface in 2026
The platform breaks into five pieces. Glean Search is the original workplace search experience, available in a web app, a browser extension, and inside tools like Slack. Glean Assistant is the chat layer for questions, summaries, research, and drafting. Glean Agents is the automation layer, with an agent builder, an orchestration engine, a shared agent library, and governance controls. Model Hub handles model selection. Glean Protect is the security and data-governance bundle. An API surface lets teams build their own retrieval-backed applications.
On connectors, read the vendor’s numbers carefully. Glean’s connectors page claims more than 275 out-of-the-box integrations, mixing native connectors with MCP-based ones. The global navigation on that same page says "more than 250," and older boilerplate in Glean’s press kit still says "over 100." The breadth is genuinely large, but treat 275 as a marketing ceiling rather than an audited count.
Model Hub is the more interesting piece. Glean does not train a frontier model of its own. It routes queries to models hosted on Amazon Bedrock, Azure OpenAI, Google Vertex AI, and OpenAI, listing options across the Claude, Gemini, GPT, Llama, and Nova families, with administrators deciding which models their organization can use. Glean’s marketing puts the total at "35 or more." Multi-provider routing is an architectural position rather than a slogan: it is the main structural difference between Glean and a single-vendor assistant, and it lets a customer swap models without swapping the retrieval layer underneath.
Permissions are the hard part
Every enterprise search vendor promises permission fidelity, and it is where deployments succeed or fail. Glean’s stated model is that permissions are inherited from source systems and enforced at query time, with changes reflected in near real time, so a document a user cannot open in SharePoint never appears in their results. Deployment is single-tenant, either hosted by Glean or run inside the customer’s own AWS, Azure, or GCP account, with data residency across the Americas, EMEA, and APAC. Glean announced an on-premises option with Dell Technologies in May 2025. Displayed compliance badges include SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, GDPR, and TX-RAMP Level 2.
These are vendor claims, and Glean’s public security page is thinner on mechanism than the marketing implies. The due-diligence question is not whether permissions are enforced. It is how fast a revoked permission propagates, what happens to content that was already over-shared inside the source system, and whether a generated summary can leak what the result list correctly hides.
Who Glean is for, and who it is not for
Glean fits organizations with a wide SaaS footprint and enough employees that finding things has become a measurable cost: several thousand seats, a dozen or more content systems, and no single vendor owning the whole stack. Customers named in press reporting include Databricks, Reddit, Pinterest, and Samsung; Glean’s own customer stories add Booking.com, Zillow, Ericsson, and Intuit.
It is a poor fit for small organizations running five tools, where search inside the two systems that matter will do the job for a fraction of the cost. It is a harder sell in a shop living almost entirely inside Microsoft 365, where the incumbent has a structural advantage. And it is the wrong product for customer-facing site or app search, which is a different buyer and a different problem.
What Glean costs
Glean publishes no pricing. The pricing URL redirects to the homepage and every call to action is a demo request. What is on the record comes from Jain, who told TechCrunch in May 2026 that Glean offers a consumption-based model where customers pay per use, alongside a hybrid model combining a fixed monthly fee for active users with separate usage fees for model consumption. He told Fortune that contracts run one to three years.
Per-seat dollar figures circulate widely on comparison blogs. None trace back to Glean or to a reputable publication, and several appear on sites run by direct competitors, so treat them as rumor rather than a planning number. Budget for an enterprise negotiation with a meaningful floor, model seat cost and consumption cost separately, and ask what happens to the bill when agent usage scales. Glean also promotes a Forrester Total Economic Impact study reporting 141% ROI over three years; that study was commissioned by Glean and built on a composite organization derived from a small number of customer interviews, which makes it a directional marketing artifact rather than independent evidence.
Where Glean sits against the alternatives
The competitive set splits along two axes: who the search is for, and how much of the stack the vendor owns.
- Algolia: search-as-a-service for your customers, embedded in a website or app. Different buyer, different budget, not a substitute.
- SearchBlox and SearchStax: mid-market enterprise and site search with on-premises deployment and comparatively transparent pricing, attractive to regulated buyers who want fixed costs more than a knowledge graph.
- Elastic: an engine your team builds on and operates. Maximum flexibility, maximum engineering effort, no packaged assistant.
- Coveo: the mature incumbent, strongest where relevance ties into Salesforce, service, and commerce workflows.
- Microsoft 365 Copilot: the real fight. Copilot is bundled, ecosystem-native, and already licensed in most enterprises.
Glean’s answer to Microsoft is ecosystem neutrality: broader third-party connector coverage, permission enforcement at the object level rather than the data-source level, and model choice. That is Glean’s own framing and should be read as such. The most useful outside read comes from analyst Nick Patience of the Futurum Group, who assessed in April 2026 that Glean’s vendor neutrality is a credible structural differentiator but that the gap with Copilot is narrowing rather than closed, and that if Microsoft materially improves retrieval quality, Glean’s moat gets harder to defend in Microsoft-heavy accounts. TechCrunch has separately noted that Google, Microsoft, OpenAI, Anthropic, Salesforce, and Atlassian are all building in this direction.
Three things to check before you buy
Run a permissions test against your messiest content repository rather than your cleanest one, and see what surfaces. Price the consumption component at realistic agent volume rather than pilot volume, because the pricing model has shifted toward consumption and the bill scales with usage. And decide honestly whether your problem is retrieval or governance. If nobody can find the document, Glean and its peers help. If the document is wrong, duplicated, or should never have been shared, better search helps everyone find the wrong answer faster, and the AI agents built on top will act on it.
Frequently Asked Questions
What is Glean used for?
Glean is used for enterprise search and workplace AI. Employees query one search box or chat interface, and Glean returns results, summaries, and generated answers drawn from connected applications such as Slack, Google Drive, Confluence, Jira, SharePoint, and Salesforce. On top of search, the platform supports an AI assistant and custom agents that automate multi-step work.
Who founded Glean and when?
Glean Technologies was founded in 2019. Arvind Jain, a former Google distinguished engineer who worked on Search, Maps, and YouTube and later co-founded Rubrik, is founder and CEO. Co-founders include Tony Gentilcore, also ex-Google, and CTO T.R. Vishwanath, who came from Facebook and Microsoft.
How much does Glean cost?
Glean does not publish pricing, and all purchases go through a sales conversation. Jain has said publicly that Glean offers a consumption-based model and a hybrid model combining a fixed monthly fee for active users with separate usage fees for model consumption, with contracts running one to three years. Per-seat figures on third-party comparison sites are not confirmed by Glean or by any reputable publication.
How does Glean handle permissions and security?
Glean states that its connectors inherit permissions from each source system and enforce them at query time, with changes reflected in near real time. It offers single-tenant deployment either hosted by Glean or inside a customer’s own AWS, Azure, or GCP account, with regional data residency. Displayed certifications include SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, GDPR, and TX-RAMP Level 2. These are vendor claims and warrant testing during evaluation.
Which AI models does Glean use?
Glean does not train its own frontier model. Its Model Hub routes to models hosted on Amazon Bedrock, Azure OpenAI, Google Vertex AI, and OpenAI, spanning the Claude, Gemini, GPT, Llama, and Nova families, and administrators choose which models their organization can access. Glean’s marketing cites more than 35 available models.
How many applications does Glean connect to?
Glean’s connectors page claims more than 275 out-of-the-box connectors, combining native integrations with MCP-based ones. Other parts of the same site say “more than 250,” and older boilerplate still says “over 100,” so the figure depends on which page you read. Verify coverage against the specific systems you need rather than taking the headline number at face value.
Is Glean better than Microsoft 365 Copilot?
It depends on your stack. Glean’s case is ecosystem neutrality: wider third-party connector coverage, object-level permission enforcement, and a choice of underlying models. Copilot’s case is that it is already licensed and already embedded in Teams, Outlook, and SharePoint. Futurum Group analyst Nick Patience assessed in April 2026 that Glean retains a credible retrieval advantage but that the gap is narrowing, which makes the answer stack-dependent.
