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.
Most tools stop at structural. The value is in semantic and knowledge.
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.
How does the intelligence reach developers?
The best tools offer multiple integration points.
How quickly does the intelligence update when code changes?
For most teams, incremental indexing is the sweet spot.
Where does your code go?
This matters more as your security team gets involved.
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.
The evaluation criteria that matter most map directly to the Understanding Tax — how much context-acquisition time does the tool eliminate on complex tickets?
For a specific head-to-head comparison, read Glue vs CodeSee vs Sourcegraph: Code Intelligence Compared.
Glue is the pre-code intelligence platform built to eliminate the understanding bottleneck. Try it on your hardest repository — that's where the difference shows.