AI has become a runaway, unpredictable line item. Outlay attributes every dollar of LLM and coding-agent spend to the work you already plan — tickets, epics, roadmap — then forecasts cost by scope and holds it to budget with pace-based guardrails. Finally, the fastest-growing line item is as predictable as the work that drives it.
“What is this body of planned work costing us in AI, and are we on track?” Today's tools meter infrastructure — API keys, models, dashboards. Outlay meters the work.
See exactly what each ticket, epic, and sprint costs in AI — and which work is about to blow its estimate — the way you already plan effort.
Allocate AI spend to teams and cost centers, set budgets by scope, and get alerts before overspend — not a surprise invoice at month-end.
Connect the tracker and AI tools you already use. Read-only to start — no app rewrite, no prompts leaving your environment.
Every dollar of agent & LLM spend mapped to the ticket, epic, and roadmap it belongs to.
# tag / branch / PR → ticket
fix/PROJ-123 → PROJ-123 → Q3-stability
Predict a quarter's cost from its open scope, the way you estimate engineering effort.
# open roadmap × per-class cost
Q3 forecast: $48k ± $9k
Pace-based alerts flag a team trending over before it blows through — not after.
# projected, not a hard cap
epic Q3-stability ⚠ on pace $22k
Every gateway, observability, and FinOps tool stops at an infrastructure tag. Outlay resolves the git context of each AI call back to the ticket — and budgets it like real work.
Illustrative. Flagged OVER early — driven by one outlier ticket at 11× its class median.
LLM and coding-agent spend attributed to the ticket, epic, sprint, and team it belongs to — the allocation finance has never had for AI.
Predict a quarter's AI cost from its open scope, then watch budget-vs-actual burn down in real time.
Projections, not hard caps. A team trending over is flagged ok → warn → over with time to act, and outlier tickets are caught automatically.
Every forecast is back-tested on your own closed tickets, leave-one-out — we show the measured error and the sample size, by work type. No vendor benchmark to take on faith.
Jira, Linear, GitHub Issues; Claude Code, Cursor, the API. Reliable attribution even for remote / CI agents via explicit task-tagging.
Attribution runs on metadata — connected with read-only tokens. Prompt content, model outputs, and your API key never leave your environment.
Most spend tools see everything you send. Outlay is built so the sensitive data physically can't reach us — purpose-built for teams that can't let prompts leave their environment: healthcare, legal, and financial services.
FinOps suites allocate cloud bills. Observability tools trace API calls. Native consoles cap per seat. None attribute AI spend to the work you plan, or budget it by scope — because they live at the infrastructure layer and can't see your roadmap.
| Outlay | FinOps suites | LLM observability | Native consoles | |
|---|---|---|---|---|
| Attributes spend to tickets, epics & roadmap | ✓ | ✗cost-center tags only | ✗spans / agent-runs | ✗seat / team |
| Forecasts cost from planned scope | ✓ | ~cloud trend forecasts | ✗ | ✗ |
| Pace-based budget guardrails by scope | ✓ | ~account budgets/alerts | ✗ | ~per-seat caps |
| Forecast accuracy back-tested on your own work | ✓leave-one-out, with sample size | ✗ | ✗ | ✗ |
| Prompts never leave your environment | ✓ | ✓reads billing, not content | ✗traces prompts/outputs | ✓ |
Where they fit: keep a FinOps suite for cloud, and observability for debugging quality. Outlay is the layer they can't be — AI spend attributed to the work you plan, forecast by scope, and held to budget. See the full comparison →
The budgeting & governance platform is in early access — we're onboarding a first cohort now, and setting pricing with them. Pilots run free.
A focused engagement: connect one tracker + your AI tools, get your real spend mapped to tickets and a budget forecast in weeks — read-only, no app rewrite.
Platform pricing for budgeting & governance is set with design partners — talk to us.
It resolves the work context of each AI call from the most reliable signal available — an explicit task tag from your agent launcher or CI, the git branch, the PR's closing-issue link, or a commit trailer — and maps it to the ticket, epic, and roadmap in Jira/Linear/GitHub. Where a team already links work to tickets it's automatic; where they don't, a one-line explicit tag makes it reliable — even for remote/CI agents where the branch is detached.
No. Outlay connects with read-only tokens and reads metadata — task category, token counts, ticket IDs, and your provider's usage data. Prompt text, model outputs, and your API key never reach our servers.
No — they're pace-based projections, not caps. A scope on track to exceed its budget is flagged warn (and over once it crosses), with the projected end-of-period total at the current burn rate, so a lead acts while there's time. A hard cap that binds mid-task just stops work; we flag the trend and outlier tickets instead.
We don't ask you to trust a vendor benchmark. Outlay back-tests its forecast on your own closed tickets, leave-one-out — hide a ticket, predict it from the rest, compare to what it actually cost — and shows the measured error and the sample size, by work type. As more work closes, the number sharpens.
Trackers: Jira, Linear, GitHub Issues. AI tools: Claude Code, Cursor, and the Anthropic API (per-call usage + the org admin API for reconciliation). Read-only to start — no app rewrite.
The budgeting & governance platform is in early access; pilots run free and pricing is set with our first design partners. Talk to us to join.
See what your AI compute costs per ticket, epic, and team — and where it's about to go over. We're onboarding a first cohort of design partners now.
Read-only to start · prompts never leave your environment · no app rewrite