Your LLM logs say what was asked. We add what happened next.
GitClear runs as a container inside your own Databricks pipeline and joins LLM request logs to git, pull requests, and org data—so every dollar of inference can be traced to code that did or didn't last.
The missing join
Millions in LLM spend, and no way to say what it bought.
Vendor dashboards report acceptance rates and lines suggested. Those measure enthusiasm, not durable output, so renewals get approved on anecdote.
Is your biggest token spender an anti-pattern to rein in, or the prototype every team should copy?
Finance sees
The invoice, by vendor and month.
Your git provider sees
Commits and pull requests, by author.
Nobody sees the link
Which tokens produced which lines, whether they survived review, and whether they were still present months later.
The opportunity
You're already logging the evidence.
Legal, Security, or a Works Council usually requires that LLM traffic never leaves the network unobserved. Every request and response is therefore already landing somewhere.
AI gateways and proxies
LLM observability
Cloud invocation logs
Vendor telemetry
bronze.llm_gateway_requests
prompt, completion, model_id,
tokens_in, tokens_out, timestamp
Whatever the source, it ends up in a Delta Lake table. It was built for compliance, and it is accidentally the most complete record of how your engineers work. Joining it to what happened next is the opportunity.
Deployment
A watermark processor that runs inside your pipeline.
You export nothing to GitClear. Our container reads from your tables and writes enriched silver and gold tables back to your account. Hourly or nightly runs resume from the last watermark.
bronze Sources never joined before
GitClear container
$ gitclear-enrich run
resume from watermark
classify prompts in-cluster
write silver.*, gold.*
- Astronomer / Apache Airflow
- Databricks Workflows
- Kubernetes, Argo, Dagster, or Prefect
silver gold Question-shaped tables
Row by row, the output connects this model to this task and the code that did—or did not—survive.
Decision-ready output
Every graph answers a decision someone has to make.
Is the frontier model worth its price?
Task × model ROI
Compare productivity, all-in cost, speed, revision risk, and steering by task. A less expensive model may match the frontier model on tests while the frontier earns its premium on cross-file refactors.
| prompt category |
Apex-R 2.4
premium reasoning
|
Apex-F 2.4
premium fast
|
Core 3.1
balanced workhorse
|
Lite 1.9
cheap, verbose
|
Local-32B
self-hosted
|
|---|---|---|---|---|---|
|
implement_feature
outcome: durable Diff Delta @ 90d
|
1.85×
$79.2 / success
80% @30d · n=262
|
1.70×
$79.8 / success
78% @30d · n=87
|
0.97×
$98.9 / success
74% @30d · n=612
|
0.37×
$212 / success
60% @30d · n=349
|
0.11×
$436 / success
49% @30d · n=146
|
|
root_cause_bug
outcome: verified diagnosis, no recurrence
|
2.34×
$81.9 / success
82% @30d · n=131
|
1.62×
$109 / success
71% @30d · n=44
|
0.81×
$154 / success
64% @30d · n=306
|
0.19×
$558 / success
44% @30d · n=175
|
0.04×
$1381 / success
34% @30d · n=73
|
|
dry_cleanup
outcome: duplication removed, retained @ 90d
|
1.63×
$52.1 / success
90% @30d · n=66
|
n=22 insufficient
|
1.00×
$55.5 / success
90% @30d · n=153
|
0.60×
$75.8 / success
86% @30d · n=87
|
0.17×
$156 / success
70% @30d · n=36
|
|
explain_code
outcome: explanation accepted, next task lands
|
1.60×
$21.0 / success
97% @30d · n=112
|
1.74×
$18.0 / success
97% @30d · n=37
|
1.00×
$21.5 / success
94% @30d · n=262
|
0.51×
$34.8 / success
83% @30d · n=150
|
0.15×
$70.0 / success
69% @30d · n=62
|
|
address_pr_feedback
outcome: thread resolved without reopen
|
1.71×
$50.3 / success
89% @30d · n=103
|
1.78×
$44.7 / success
92% @30d · n=34
|
0.98×
$57.1 / success
87% @30d · n=240
|
0.42×
$110 / success
73% @30d · n=137
|
0.11×
$238 / success
59% @30d · n=57
|
|
build_failing_test
outcome: fails before fix, passes after
|
1.66×
$32.1 / success
94% @30d · n=47
|
n=16 insufficient
|
1.04×
$33.6 / success
91% @30d · n=109
|
0.48×
$59.9 / success
78% @30d · n=62
|
0.14×
$117 / success
65% @30d · n=26
|
How many of our tokens never ship?
Token release funnel
Follow generated lines through commit, review, merge, release, and survival at 90 days. Drop-off between stages shows exactly where spend stops producing value.
Security
Aggregated by design, so projects don't stall in review.
Enterprise results roll up by team, model, or cost center. This keeps the enriched tables clear of unnecessary PII and the individual surveillance concerns that stall AI-measurement projects.
SOC 2 Type II and ISO 27001 certified, penetration tested in 2026. Review GitClear security
Inside your Databricks account
- Prompt and completion text
- Source code and commit history
- In-cluster prompt classification
- Enriched silver and gold tables
Find out what your LLM spend has been paying for.
If you already capture LLM traffic, we'll scope an enrichment pipeline and quote it for free.