Five steps, on an example ~75-engineer team — connect your sources, then watch AI spend get mapped, forecast, budgeted, and routed down. Click through at your own pace. No prompts ever leave the box.
One tracker and your AI usage. About 30 minutes, no app rewrite, no proxy required to see the picture. Outlay reads metadata, never your prompts.
Outlay resolves each AI agent's spend back to a ticket → epic → team — via the git branch and PR→issue link, plus an explicit task tag for CI/remote agents. Costs are cache-aware, so cached agentic workloads aren't overstated 5–10×.
Outlay learns a cost distribution per work-type and costs your open roadmap bottom-up — a realistic range, not last month's bill extrapolated. And it measures its own accuracy with a leave-one-out backtest, instead of asserting it.
Set budgets by scope (team, epic, cost center). Outlay tracks actuals against them and projects to end-of-period by pace, so a team about to blow its budget is flagged weeks early — not at month-end. Guardrails bind on outliers, never as hard caps that fail you mid-task.
For each work-type, Outlay learns which cheaper model is provably good enough: it scores a candidate in shadow, proves non-inferiority in a quality canary, then enforces the downgrade. You're billed only on realized savings, and it always fails open.
Illustrative example data for a ~75-engineer team. In a pilot, every number here is yours — read-only, in ~2 weeks.
Two weeks, read-only, prompts never leave your environment. We'll map your real AI spend to your roadmap and show you where it's about to go over.