Unblocked vs Glean (2026): Why Engineering Teams Choose the Context Engine

Updated September 13, 2026 for Glean's $300M ARR milestone and MCP server, Unblocked's CLI and current pricing, and new customer results from Cloudbeds, Webflow, RB Global, and Advidi.

Engineering teams choose Unblocked over Glean because Unblocked was built for the way engineering knowledge actually exists: spread across code, pull requests, Slack, Jira, Confluence, Notion, and production systems, often contradicting itself, and increasingly consumed by coding agents rather than people. Unblocked reads all of it at once and hands back one reconciled, permission-checked answer. Glean is enterprise search for the whole company, strong for HR, sales, legal, and support content, and it treats engineering as one more department to index. The two products get compared because both now plug into Claude Code, Cursor, and Codex over MCP, and both claim to save tokens. What arrives through that connection is not the same thing. Glean returns documents, tickets, people, and code matches for the agent to sort out. Unblocked returns the answer, with the conflicts already resolved and the sources attached. Cloudbeds measured the difference on a real incident investigation at about a third of the tokens (Cloudbeds customer story), and Advidi's CTO, after wiring native connectors to his agents and getting "extreme context bloat," consolidated on Unblocked instead (Advidi customer story).

Glean is a serious company. It crossed $300M in annual recurring revenue in May 2026 and serves Databricks, Reddit, Pinterest, and Samsung (TechCrunch, May 2026). It is also a CIO purchase, made for the whole company, and the engineers who inherit it keep asking the same question in Slack. That is the pattern behind every customer on this page: a technical team that had search available, or evaluated it, and picked the context engine instead. The question for an engineering leader is which tool your engineers and your agents reach for when the answer is spread across a PR thread, a Slack argument, a ticket, and the code. In 2026, for engineering teams, that tool is Unblocked.

For background on the category, see what a context engine is.

What problem does each platform solve?#

Glean solves enterprise-wide document discovery. It connects most of the SaaS stack a company runs, from Google Drive and Confluence to Salesforce and ServiceNow, builds a permission-aware index over all of it, and gives every employee a single search bar plus an AI assistant that answers from what it finds. Glean's CEO now pitches that index as a "context graph" that lets any AI consume "far fewer tokens" because the information is already gathered (TechCrunch, May 2026). For a salesperson asking where the latest pricing deck is, that is exactly right.

Unblocked solves engineering context fragmentation. Engineering knowledge does not live in a document. It lives in the PR that merged six months ago, the Slack thread where the team argued about the approach, the Jira ticket that tracked the work, the Sentry error that forced a revert, and the code itself. Unblocked ingests those sources into one living knowledge graph, traverses them in a single pass, resolves the places where they disagree, and returns one answer with links to every source it used (Unblocked context engine).

The difference is breadth versus depth, and it shows up in the question each tool is built to answer. Glean answers where is the document? Unblocked answers why is the code written this way, and what happens if I change it? Sonar's State of Code survey found developers spend nearly a quarter of the work week on toil such as debugging poorly documented legacy code, and it names finding information and understanding existing systems among the tasks that most hinder productivity (Sonar State of Code). Search shortens the first step of that work. A context engine removes the rest.

For an architectural comparison, see how context engines differ from enterprise search.

What changed in 2026: Glean can reach your agents now, but it still hands them homework#

The 2025 version of this comparison rested partly on delivery. Unblocked spoke MCP; Glean spoke to people. Glean has closed that gap on paper, so the comparison now comes down to what each tool puts in front of the agent.

Glean's MCP server now exposes Search, Chat, Read Document, Code Search, and People tools, lets administrators publish Glean agents as callable tools, and lists 22 supported hosts including Cursor, Claude Code, Codex, VS Code, Claude Desktop, and ChatGPT (Glean docs). There is an official Claude Code plugin that installs search, code exploration, and people skills (Glean developer docs). That is real work, and teams that already pay for Glean should turn it on. It will not change the outcome of an engineering evaluation.

What the plugin cannot change is what comes back. Glean's own documentation describes MCP results as "documents, tickets, people, code" spanning connected sources, and advises users to state the tool they want explicitly, as in "search Glean for" or "fetch the document" (Glean docs). Agents published as tools cannot include write steps or wait for a human, and hosts cut them off after roughly 30 to 60 seconds (Glean docs). In other words, the agent gets a search box. It still has to read the results, decide which Confluence page is current, notice that the PR contradicts the wiki, and assemble the answer itself, inside its own context window, on your token bill. That is the same ten-tab workflow engineers were trying to escape, handed to a model that is worse at it than they are.

Unblocked does that assembly server-side. Context is scoped by the asker's intent and identity, scored, compressed, and reconciled before it reaches the agent, and every answer ships with source links (Unblocked context engine). Engineering teams notice the difference immediately, because it shows up as agents that stop circling. Webflow's engineers who had written agents off because of hallucinations were the first to ask that Unblocked never be removed (Webflow customer story). The distinction the rest of this page is about is not whether an agent can reach the tool, but how much work is left when the response lands.

Where does Glean fit?#

Glean is the right tool when the buyer is the CIO and the users are everyone. Its connectors cover nearly every SaaS tool in a modern enterprise, with native integrations, push APIs, and partner-built connectors (Glean docs), and its connectors fetch each source's permission map so a result only appears to someone who can already see it in the source application. For an organization that needs one search bar across HR, sales, legal, and support, that breadth is the product, and Unblocked is not competing for it.

None of that is what an engineering team is buying. Engineers do not need a hundred connectors; they need the five that hold the decisions behind the code, read deeply enough to explain them. The teams on this page had access to company-wide search, or evaluated it, and still bought a context engine for engineering, because breadth across departments does not translate into depth inside one.

Where does Glean fall short for engineering teams?#

The shortfall is not a bug. It is a design choice. Glean was built to serve the whole company, and engineering depth was never the primary target. Four limits follow from that, none of them fixable with another connector, and each one is a reason technical teams end up asking for a separate tool.

The ten-tab problem#

Every engineer knows the workflow. Search for context on a service, open ten tabs, scan each one, discard the stale ones, cross-reference the rest, and piece together an answer. Search accelerates the first step. It does nothing for the next five.

That matters more now than it did two years ago, because the person doing the ten-tab workflow is increasingly an agent, and agents are bad at it. They cannot tell which Confluence page was written in 2023 and which PR superseded it last week. They take what retrieval gives them and treat it as ground truth. The output is the "almost right, but not quite" code that 66% of developers name as their top frustration with AI tools, while 46% actively distrust AI accuracy and only 33% trust it (Stack Overflow, 2025).

Conflict resolution#

When the Confluence page says the auth service uses a 15-minute token expiry and the code sets it to 60 minutes, search returns both. A Slack thread from last quarter explains the team changed the expiry during an incident and never updated the docs. Search finds all three. It resolves none of them.

This is the architectural ceiling of enterprise search for engineering. Search ranks by textual relevance. Engineering decisions require ranking by authority, freshness, and source type: code outranks stale docs, merged PRs outrank open drafts, last month's Slack decision outranks a year-old wiki page. That ranking logic does not exist in a system designed for document discovery, and Glean's documentation describes its assistant as retrieving indexed content and generating a response from it, not reconciling sources against each other (Glean docs). We covered the failure modes in how tools handle conflicting context.

Code as text, not as a system#

Glean's GitHub connector indexes repositories, source files, commits, READMEs, issues, and pull requests with reviews and diffs (Glean docs). That is real coverage. It is also code stored as text documents. A keyword-relevant chunk of a 4,000-line file is not an answer to "how does billing retry work," and no ranking model turns it into one. Tracing a dependency chain, reading the git history behind a pattern, or connecting a Sentry error to the PR that caused it requires a graph of the system, not an index of its files.

Freshness on a living codebase#

A sales deck from March is still true in August. The code from March is three refactors gone. Glean's GitHub connector is among its fresher ones, with incremental crawls and webhook updates, but full crawls across connectors range from 6 hours to 28 days, and content whose deletion event was missed waits for the next full crawl (Glean docs). GitHub content also does not appear for an engineer at all until that engineer completes an individual OAuth step (Glean docs). Teams discover that one ticket at a time. For a deeper walk through these limits, read Is Glean good enough for engineering questions?

What does Unblocked do differently for engineers?#

Unblocked starts where enterprise search stops. Anthropic's engineering team describes effective context assembly as a layered problem in which retrieval is the starting point, not the finish line (Anthropic, 2025). Glean stops at retrieval. Unblocked adds the reasoning, conflict resolution, and cross-source synthesis above it, and it does so with sources and surfaces chosen for engineers. Here is what that looks like in practice, and why technical teams describe the difference in terms of trust rather than recall.

Cross-source reasoning#

An engineer asks: "Why does the payment service retry with exponential backoff?" Unblocked reads the merged PR, the Jira ticket that requested the change, the Slack thread where fixed intervals were argued down, and the code. It returns one answer with a citation to each. No tab switching. No manual cross-referencing. At Cloudbeds, an engineering manager asked why a background sync component had been removed and got back five tickets, the Slack conversation, and a Confluence page one of his own engineers had written about the transition, then handed the whole rationale to a new hire instead of a day of digging (Cloudbeds customer story).

Conflict resolution and relevance#

Contradictions across sources are resolved automatically using recency, authority, and proximity signals, and results are personalized to each developer's contribution history and terminology (Unblocked context engine). This is the layer that turns ten tabs into one answer, and it is the reason customers describe the output in terms of trust rather than recall.

The thing I really like about Unblocked is that whenever it speaks up, it's always right. I can't think of a time it told me something that wasn't correct. You could build another AI to do that. It would take four times as long and cost ten times as much.

Charles Thompson — Engineering Manager, Reservation and Guest, Cloudbeds

Permission-aware delivery for people and agents#

Unblocked inherits the permissions already set in GitHub, Jira, Confluence, and Slack, per user, and applies them at query time to the engineer and to any agent acting on that engineer's behalf. If a junior developer cannot see the infrastructure team's private channel, neither can their agent. Advidi's CTO scoped his incident agent exactly like a team member's access for that reason, and singled out the permission layer as what made it safe to hand one engine to people and agents alike (Advidi customer story). SOC 2 Type II, SSO with SCIM, data isolation, and encryption in transit and at rest are standard (Unblocked security).

Engineering-native sources and surfaces#

Unblocked connects to GitHub, GitLab, Bitbucket, Jira, Linear, Confluence, Notion, Slack, Teams, and production systems such as Sentry and Datadog, at a depth designed for engineering. It reads PR review comments, commit messages, and git history, and it understands that a PR discussion is a different kind of source from a wiki page. It delivers through MCP to Claude Code, Cursor, Copilot, and Codex, through a CLI for terminal and scripted workflows, through Slack and Teams, in the browser, over an API, and as AI code review on pull requests (Unblocked).

Our team saves between 60-70 hours per week that otherwise would've been spent on looking for answers or answering questions from others.

Ekan Subramanian — VP of Engineering, Fingerprint

Why do engineering teams pick Unblocked for AI coding agents?#

This is where the comparison is decided in 2026, because the asker is increasingly an agent, and the teams running agents at scale have already voted. DORA's 2026 research on AI returns found the largest gains come from strong engineering foundations rather than the tools themselves, and that without them AI creates "localized pockets of productivity that are often lost in downstream chaos" (DORA, 2026). Context quality is one of those foundations, and more context is not the same as better context: on the NoLiMa benchmark, 11 of the 12 models tested fell below half of their short-context performance once the window reached 32,000 tokens (Modarressi et al., 2025). Handing an agent ten documents to reconcile is the failure mode that research describes.

What arrives through the MCP connection#

With Glean, the agent receives ranked documents, tickets, people, and code matches and does the reconciliation itself. With Unblocked, the agent receives the reconciled answer, scored and compressed server-side, with sources attached. The difference shows up on the token bill and in the time a human spends supervising, and it is why engineering teams that measure it standardize on Unblocked.

Cloudbeds tested it directly. An engineering manager triaged the same incident twice with his existing Claude skill, once as-is and once with a single added instruction to talk to Unblocked first.

Unblocked used about a third of the tokens. Five thousand versus fifteen thousand. And the thing I thought was amazing is that Unblocked had the answer already. The rest was just my AI dressing it up. After that I opened up about twenty PRs, and now the first thing all of our skills do is ask Unblocked.

Charles Thompson — Engineering Manager, Reservation and Guest, Cloudbeds

Advidi went the other direction first, wiring native MCP servers for GitHub, Confluence, and Jira to its agents. "You get this extreme context bloat," its CTO said. "You load all of your Confluence documentation or crawl all of Jira. It was a token-expensive job, and you'd still reference outdated docs and miss things." The team consolidated on one Unblocked MCP for 400-plus repositories, its engineers, and its incident agent (Advidi customer story).

Unblocked combines all of the tools we use into one product, one single MCP. It adds a permission layer on top. And the search is efficient: the context it gathers is relevant and recent. I haven't found another tool that does that as effectively as Unblocked does.

Jorden van Breemen — CTO, Advidi

Agents at scale#

Webflow runs remote agentic development on Claude, Cursor, and Codex through an internal platform that spins up a sandbox, clones the codebase, and gives the agent tools over MCP. Unblocked is the connection the team calls the brain layer. "We have all the other MCPs that can access Slack history or GitHub," said Russ Nealis, a staff technical product manager there, "but what Unblocked does really well is pull all the information sources together, tease out what really matters, and get us the signal we need to actually do the thing." Engineers who had been skeptical of agents because of hallucinations were among the first to ask that the tool never be removed, and in one case Unblocked stopped an agent from re-implementing a feature by pointing at the PR that already had (Webflow customer story).

In a controlled internal test, the same agent on the same codebase completed the same task with 48% fewer tokens and 83% faster with Unblocked feeding context upstream. That is a single-task benchmark, but it is consistent with what Cloudbeds and Webflow report after deployment (Unblocked).

Read the context engineering guide for the layered approach behind these results.

How do they compare on pricing?#

Glean does not publish list pricing. Buyer-reported data puts the median Glean contract at $98,890 a year, with recorded deals ranging from about $29,880 to $208,897 and minimum commitments that often start around 100 to 250 users (Vendr). TechCrunch reports Glean has moved toward consumption-based and hybrid pricing that combines a fixed monthly fee with usage charges (TechCrunch, May 2026). Seat floors are the number that matters for an engineering buyer: a 40-person engineering team pays for a company-wide rollout whether or not the rest of the company adopts it.

Unblocked publishes its pricing at $29 per user per month on annual plans, with a 21-day free trial and no stated seat minimum (Unblocked pricing). Rally's CTO, Alec Robins, found the enterprise options impractical for a company his size on both cost and management overhead when he shopped the category. "For what we needed, there really wasn't anything that solved this except for Unblocked," he said, and he now spends 90% less time answering product questions in internal channels (Rally customer story). For the wider field, see the best Glean alternatives for engineering teams.

How do the features compare side by side?#

Adoption is no longer the variable. 84% of developers use or plan to use AI tools (Stack Overflow, 2025). The infrastructure underneath those agents is what determines whether they produce trusted output or constant rework, so this table is organized around the three things engineering buyers ask about: integration breadth, permission controls, and impact on developer velocity. On the rows that matter to an engineering team, Unblocked wins.

Glean takes the breadth row, and if legal, HR, and sales all need search, it belongs on that shortlist. Every other row is an engineering requirement, and engineering leaders are asking a different question. Glean answers where is the document? Unblocked answers why is the code written this way, and what happens if I change it? For engineering teams, the second question is the one that costs money, and Unblocked is the tool built to answer it.

Frequently asked questions#

Is Glean good for engineering teams?#

For finding documents, yes. For the questions engineers actually ask, such as why a service is shaped the way it is or what will break if a retry policy changes, no, because the answer lives in a PR thread and a Slack argument rather than a page, and Glean ranks pages. Glean ranks documents against a query; it does not reconcile a current Confluence page against the commit that contradicted it last month. The longer answer, with the questions to test it on, is in Is Glean good enough for engineering questions?

Can Glean and Unblocked work together?#

Yes, and several customers run exactly that split. Glean stays the company search bar for HR, sales, legal, and support content. Unblocked handles code, PRs, Slack decisions, tickets, and engineering documentation for engineers and their agents. In practice the engineering team stops using the search bar for engineering questions within weeks, because the context engine answers them and the search bar returns links.

Does Glean work with Claude Code and Cursor?#

Yes. Glean's MCP server supports Claude Code, Cursor, Codex, VS Code, and Claude Desktop, and there is an official Claude Code plugin (Glean docs). The difference is what the agent gets back: search results across connected sources with Glean, or a reconciled, permission-filtered answer with Unblocked. Advidi's CTO tried the per-system connector route and got "extreme context bloat" before consolidating on Unblocked (Advidi customer story).

How does Glean compare to Sourcegraph for developers?#

They solve different halves of the problem. Sourcegraph is code search and navigation: it makes finding code across repositories fast and exposes that to agents over MCP. Glean is enterprise search across documents and conversations, with code indexed as one more source. Neither delivers the decisions behind the code as a reconciled answer. That is the gap Unblocked fills, and we compare it directly in Unblocked vs Sourcegraph Cody.

Is there an open-source alternative to Glean or Unblocked?#

Onyx is the credible open-source option for enterprise search: MIT-licensed, self-hostable, with a managed cloud tier (Onyx on GitHub). It is a search product, so it shares Glean's ceiling on engineering questions and adds the operational cost of running it yourself. Our roundup of Glean alternatives covers when it fits.

What do engineering teams gain by switching to Unblocked?#

Faster, more trustworthy answers for engineers and agents, at lower token cost. Cloudbeds runs every AI skill through Unblocked first at a third of the tokens. RB Global's new platform team took over a consultant-run stack in three months instead of six and now supports more than 200 developers with, in its director's words, "a support team without having a support team" (RB Global customer story). Fingerprint reports 60 to 70 hours a week recovered from answering questions. The pattern is the same: once Unblocked is the context source, agents stop producing almost-right output and engineers stop spending their day on archaeology.

Does Unblocked replace Glean?#

Unblocked replaces Glean for engineering context. That is the decision a VP of Engineering or Head of Platform makes for their team. If the broader organization uses Glean for pricing sheets and HR policies, those use cases stay with Glean. The engineering choice is independent, and it does not require removing anything.

Why engineering teams choose Unblocked#

Engineering teams at Cloudbeds, Webflow, RB Global, Advidi, and Rally chose Unblocked because search was not the problem. Resolution was. Each of those teams could have pointed a search tool at its repositories. They wanted the tool that reads the PR thread, the Slack argument, the ticket, and the code together and tells an engineer or an agent what is true now. Getting one reconciled, permission-checked answer across code, PRs, Slack, Jira, and docs, delivered to the agent over MCP and to the engineer in the IDE, terminal, or Slack, is what cuts agent tokens by two thirds at Cloudbeds and turned a six-month platform transition into a three-month one at RB Global.

Glean will keep serving sales, HR, and support at the companies that adopted it. If the question is what your engineers and their agents reach for to understand your codebase and the decisions around it, the answer in 2026 is Unblocked, and the technical teams above already made that call. Your agents already know the code. Start a free trial and give them the rest of the context.