Glue.tools vs Competition: Complete 2025 Product Intelligence Comparison
Code search is table stakes now. Every engineer expects to grep across repos, find function references, trace dependencies. Sourcegraph does this well. So does GitHub's native search. OpenGrok has done it for decades.
But here's what none of them tell you: What features exist in your codebase? Which ones are fragile? Who actually owns the authentication flow? What gaps exist between what product thinks you built and what actually shipped?
That's the difference between code search and product intelligence.
The Search-First Generation: Powerful But Incomplete
Sourcegraph is impressive. Universal code search across every repository. Symbol navigation. Batch changes. If you need to find where is called across 200 repos, it's your tool.
The problem: You still don't know what the email validation feature does. You don't know it has 47% code churn in the last quarter. You don't know the original author left six months ago and nobody really owns it now. You don't know product thinks there's a "magic link authentication" feature that was scoped but never actually built.
Search tools index code as text. Smart text with syntax awareness, but still text. They answer "where is this code?" not "what does this system do?"
I've seen teams spend $50k/year on Sourcegraph and still maintain feature inventories in Notion. That's not a Sourcegraph failure — it's working as designed. It searches code. It doesn't understand products.
The Visualization Approach: Pretty Pictures, Same Problem
CodeSee takes a different angle. Generate architecture diagrams automatically. Show service dependencies. Map how data flows through your system.
This helps. Especially for onboarding or incident response. When production breaks at 2am, a service map beats scrolling through kubernetes configs.
But visualization tools have the same conceptual limitation. They show you structure without meaning. You get boxes and arrows. You still manually document what each box does, who maintains it, whether it's healthy.
A CodeSee diagram might show AuthService connects to UserDB and SessionCache. Great. What features does AuthService actually provide? Is it just login/logout or does it handle OAuth, MFA, password resets, session management, API tokens? Is the OAuth integration complete or half-finished?
These tools don't know. They draw what exists. They don't interpret what it means.
What Product Intelligence Actually Means
Real product intelligence works differently. It starts with a simple question: If someone asked "what features does this codebase implement?", could your tooling answer?
Not "what files exist" or "what services connect to what." What user-facing capabilities does this code provide?
This is where Glue.tools diverges from the search-and-visualize generation. It indexes code but then uses AI to discover features. Not by reading documentation (which is always stale). By analyzing actual implementation.
Here's a concrete example. Your payments service has:
Traditional tools would find all these pieces if you searched for them. Glue automatically surfaces: "Full refund feature exists and is tested. Partial refund feature is documented but not implemented — gap detected."
Code health matters more than most technical debt dashboards admit. You can have zero linting errors and still have a disaster waiting to happen.
The refund processor I mentioned? Let's say it has:
67% code churn in 90 days (high volatility)
Cyclomatic complexity of 43 (very complex)
Original author departed (orphaned ownership)
Changes from 8 different engineers (scattered knowledge)
Traditional tools might tell you some of this. SonarQube tracks complexity. Git shows churn. But nothing connects these signals into "this feature is fragile and risky."
Glue maps code health at the feature level. Not "file X has high complexity" but "the refund feature combines high complexity with high churn and unclear ownership." That's actionable. That tells you where to invest in refactoring or documentation or team changes.
I've used complexity analyzers for years. They generate CSV reports that nobody reads. Health scoring works because it's tied to features engineers actually care about. "Authentication is healthy. Payment processing needs attention. User search is a dumpster fire." That language resonates.
Team Insights: Who Really Knows What
Ownership in code is fiction. CODEOWNERS files lie. They list who should maintain something, not who actually does.
Real ownership emerges from behavior:
Who commits changes?
Who reviews PRs?
Who fixes bugs?
Who answers questions in Slack?
Glue surfaces this automatically. It shows you that although the payments team "owns" the refund feature, 60% of recent changes came from the checkout team. Maybe that's fine. Maybe it's a problem. Either way, you should know.
This matters for planning. You're building a new subscription cancellation flow. Traditional tools don't help scope who should work on it. Glue shows you: "Subscription management is primarily maintained by Alex and Jordan, with occasional contributions from the billing team. Complexity is medium, health score is good."
Now you know who to involve, whether it's risky, what dependencies exist. Before writing a single line of code.
The MCP Integration Angle: Intelligence Where You Work
Here's where product intelligence gets practical. Sourcegraph gives you a search box. CodeSee gives you a dashboard. Both require context switching.
Glue integrates directly into your IDE through Model Context Protocol. You're in Cursor writing code and ask: "What's the current state of our OAuth implementation?" Glue answers from indexed knowledge. Not by searching docs. By understanding what actually exists in code.
Or you're in Claude planning a refactor. "Show me all features that depend on the legacy user database." Glue provides that context instantly. The AI in your editor gets accurate, current product intelligence without you leaving the conversation.
This is different from Copilot autocomplete or cursor prediction. Those are code generation tools. MCP integration is code understanding — giving AI assistants the same product intelligence you'd get from talking to a senior engineer who's read the whole codebase.
Gap Analysis: The Feature Nobody Knew They Needed
The most underrated capability in product intelligence: automatically detecting what's supposed to exist but doesn't.
Every codebase has ghosts. Features that were planned, partially built, then abandoned. APIs that are documented but not implemented. Integration points that exist in the frontend but not the backend.
Glue finds these gaps by cross-referencing multiple sources:
Code comments mentioning future features
API documentation for non-existent endpoints
Frontend code calling backend routes that don't exist
Product requirements without corresponding implementation
Test descriptions for unwritten code
I've seen this catch real problems. A team shipped a feature flag but never built the feature behind it. The flag was documented, tested, deployed to production. But it didn't do anything. Traditional monitoring didn't catch it because nothing broke. Glue flagged it as a gap between documented capability and actual implementation.
When You Still Need Traditional Tools
Product intelligence doesn't replace everything. Sourcegraph is still the best universal code search. If you need to find every instance of a function call across 500 repos, use Sourcegraph. Glue won't beat that.
CodeSee's real-time collaboration features during incidents are valuable. When debugging a production issue with distributed tracing, architectural diagrams help.
SonarQube's security scanning catches vulnerabilities Glue doesn't look for. Snyk finds dependency risks. These are important.
The difference: Those tools tell you about your code. Glue tells you about your product. What features exist. What's healthy. What's missing. Who maintains what.
You might run Sourcegraph for search, SonarQube for security, and Glue for product intelligence. That's fine. They're solving different problems.
The Real Comparison: Questions Answered
"Where is this function used?"
Sourcegraph: Excellent
Glue: Basic
"How does this service connect to others?"
CodeSee: Excellent
Glue: Good (through feature dependencies)
"What features does this codebase implement?"
Sourcegraph: Can't answer
CodeSee: Can't answer
Glue: Primary use case
"Which features are high-risk due to churn and complexity?"
All traditional tools: Can't answer
Glue: Core capability
"Who actually maintains the OAuth implementation?"
GitHub insights: Shows commits
Glue: Shows commits plus expertise mapping plus health scores
"What features are documented but not built?"
All traditional tools: Manual detective work
Glue: Automated gap detection
What This Means for Your Team
If you're purely building developer tooling for internal use, traditional code search might suffice. You live in code. You know what exists.
If you're building products — especially with product managers, designers, non-technical stakeholders — product intelligence changes planning and communication. No more "wait, do we actually support OAuth or just basic auth?" conversations. No more discovering half-built features during release planning.
Engineering leadership gets visibility into health and ownership without manually surveying teams. Product gets accurate feature inventories without maintaining spreadsheets. New engineers get real understanding of what exists, not just architectural diagrams.
That's the difference. Code search finds needles in haystacks. Product intelligence tells you what's in the haystack and whether you should be worried about it.
Glue.tools is building this category. Not because code search is bad, but because understanding your product is a different problem than understanding your code. Both matter. Tools finally exist for both.