The optimization layer for AI coding tools
Your AI coding bill is bigger
than it needs to be.
Your team spends $200-$2,000 per engineer per month on AI coding tools. Codelitics shows you how to optimize that AI spend: per developer and per repo, where a cheaper model, a sharper prompt, or more work in parallel gets the same result for less. We onboard a few teams a month.
Claude
Cursor
Codex
Copilot
Total spend this month · $8,000 tracked
40% of this team's AI spend would deliver the same work on cheaper settings. From changing defaults like model choice, not from capping anyone's usage.
Top priorities to improve spend
every finding ends in a move- +$1,700Model optimizationRoute simple edits off Opus onto Sonnet by default
- +$900Prompt qualityAdd a definition of done to under-scoped prompts
- +$600Caching efficiencyFront-load stable context once, keep topics in separate sessions
+ 6 more findings across model, prompt, and parallelization.
View opportunitiesThe problem
Every tool shows you usage. None of them show you how to use it better.
Your team runs:
Each ships its own analytics: lines generated, acceptance rate, tokens consumed. None of them tell you which model was overkill for the task, which prompts wasted a run, or where the same work would have cost less.
Premium tiers, routine work.
A large share of spend runs on your most expensive models, including on tasks a cheaper tier would ship just as well.
Under-scoped runs cost double.
Vague prompts send the agent in circles. Context plus a clear definition of done delivers roughly twice the work per dollar.
Every finding is fixable.
These are defaults, not habits set in stone. Codelitics shows the change and what it's worth, per developer and per repo.
Statement
#ENG-2026-05
Billing period
May 2026
- Claude CodeTeam · 22 seats$3,200
- CodexPro · agentic usage$3,100
- CursorBusiness · 22 seats$900
- CopilotBusiness · 22 seats$800
The spend, you can see. The savings, you can't. That's what Codelitics finds.
The fix, in two minutes
Watch the leak get found.
Two minutes through the demo workspace: $10.5k of monthly AI spend, $1.8k of it leaking through defaults nobody had time to question, and the exact levers that get it back. Nobody's usage gets capped.
Why now
Adoption is done. Optimization is the next phase.
The question moved from "are we using AI?" to "are we using it well?" Budgets are real, usage keeps climbing, and the teams pulling ahead are the ones tuning how they work, not the ones using AI less.
The VergeMicrosoft started canceling Claude Code licenses after adoption ran ahead of the plan.
FortuneUber burned through its 2026 AI budget in four months. Now its COO is asking whether it was worth it.
TechCrunchTokenmaxxing turned the industry argument from usage to measured productivity.
XThe token-burn conversation has moved from finance meetings into the builder timeline.
METRDevelopers believed AI made them faster while controlled data showed the opposite.
Token usage is now material enough for finance teams and platform leads to care.
More usage does not have to mean a bigger bill. The same work often runs for less on the right settings.
Tuning usage extends the budget and frees capacity to run more work in parallel, without capping anyone.
The five levers
Five ways your team gets more from the same spend.
The right tier for the task
Premium models cost several times a mid tier for the same tokens. Set a cheaper default and auto-route by task; engineers keep the override.
Context plus a definition of done
Well-scoped prompts deliver roughly twice the work per dollar. Codelitics flags under-specified runs and gives your team the scaffold.
Stop re-sending context
Cached context reads back at a fraction of the price. Front-load it once at session start and keep separate topics in separate sessions.
A lean CLAUDE.md and AGENTS.md
Keep the root context file a tight index that lists build, test, and lint commands. The sweet spot is 80 to 120 lines.
More in flight, not more waste
Split independent tasks across parallel sessions and subagents, and put idle seats to work. It's headroom you already pay for.
Every lever is scored per developer and per repository, then rolled up so your team sees its biggest opportunities first. Coaching first: each score ends in a move, not a grade. The measurement behind it is public in the Return on Code standard.
How it works
Your team's priorities in minutes.
Three steps to get there.
Install Codelitics
Install the Codelitics agent across your team and connect your repositories. It runs on each developer's machine alongside your AI coding tools, reading the telemetry they already produce.
Plug in your tools
Connect the AI coding tools you already use. Claude Code, Cursor, Codex, Copilot. Multi-tool from day one, no migration required.
Get your priorities
You don't start from zero. Codelitics reads the session history already on each machine, so ranked, costed moves appear minutes after install, per developer and per repo.
The live demo
Open the dashboard your invoice can't show you.
The same workspace from the walkthrough, live. Click through the findings, the levers, and the per-developer coaching view, and see how a leak turns into a move.
The live demo
Behind the blur: a team's month of AI spend, with the leak marked.
Your work email unlocks it right here. We'll also send you one email with a link to book a walkthrough.
Trust & security
Full visibility. Built for security review.
Codelitics measures AI work from real activity across the tools and repositories you connect. The questions your security team asks first:
Runs where AI work happens
A Codelitics agent installs with your team's AI tools and connects to the repositories you choose. The guidance comes from real activity, not surveys or self-reports.
You control the scope
Codelitics reads only the repositories and AI tools you connect it to, and you decide what's in scope from day one.
Tool, workflow, and team level
See where AI spend can be tuned across tools, repos, and teams. Granular enough to act on, defensible enough to take to the board.
Numbers you can defend
Every figure is exportable and traceable to how it was calculated, so finance and security can verify it instead of taking it on faith.
FAQ
The questions engineering leaders ask first.
We onboard a few teams a month
Find the savings your bill is hiding.
Book a 30-minute fit call. We install on one repo and walk you through your team's biggest savings, with the number next to each.
We onboard a few teams a month, hands-on
- You control which repos are in scope
- One install on one repo
- Leave anytime, keep everything