The code intelligence market is crowded. Sourcegraph, CodeSee (shutting down), Glue, Stepsize, LinearB, Swimm, and a dozen smaller players all claim to help developers understand codebases. Some are code search tools wearing an AI hat. Others are documentation generators pretending to be intelligence platforms.
Here's how to tell the difference.
What to Evaluate
1. Depth of Analysis
- Surface level: Text search across files (glorified grep)
- Structural: Symbol extraction, import mapping, type resolution
- Semantic: Feature boundary detection, dependency graphs, call path tracing
- Knowledge: Git history analysis, tribal knowledge extraction, expertise mapping
Most tools stop at structural. The value is in semantic and knowledge.
2. Query Capability
Can you ask natural language questions and get traced answers?
Bad: "Here are 10 files that match your search" (search, not intelligence)
Good: "Authentication is handled by these 14 files. The flow starts at authMiddleware.ts line 24, calls sessionService.validate(), which checks Redis. The last change was in PR #847 by Sarah, which fixed a session leak."
The difference is traced understanding vs. keyword matching.
3. Integration Model
How does the intelligence reach developers?
- IDE plugin only → limited reach, one-developer-at-a-time
- Web dashboard → useful for exploration, not for daily workflow
- API/MCP → can feed intelligence to any tool (Claude Code, Cursor, custom agents)
- CI/CD integration → automated blast radius checking before merge
The best tools offer multiple integration points.
4. Freshness
How quickly does the intelligence update when code changes?
- Batch indexing (daily/weekly): always slightly stale
- Incremental indexing (on push): up-to-date within minutes
- Real-time (on save): current but computationally expensive
For most teams, incremental indexing is the sweet spot.
5. Security Model
Where does your code go?
- Cloud-only processing → code leaves your environment
- Hybrid (index locally, query remotely) → structured data leaves, raw code stays
- Self-hosted → nothing leaves your environment
This matters more as your security team gets involved.
How to Run a Proof of Concept
- Pick your most complex repository (not the simple one — that's where every tool looks good)
- Prepare 10 questions that new developers actually ask about that repo
- Time how long each question takes to answer with the tool vs. without
- Evaluate answer quality: traced and accurate, or vague and sometimes wrong?
- Check the edge cases: how does it handle monorepos, multiple languages, generated code?
The tool that saves the most time on the hardest questions is the right choice — not the one with the best demo on a sample repo.
Originally published on glue.tools. Glue is the pre-code intelligence platform — paste a ticket, get a battle plan.
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