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, holds it to budget with pace-based guardrails, and safely drives it down. The predictability of a salaried engineer, with the leverage of AI.
“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
Route each work-type to the cheapest model proven good enough — shadow, then quality canary.
# earned, never on faith
feature → sonnet ✓ −$5.5k/mo
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.
Per work-type, a cheaper model is logged in shadow and proven by a quality canary before it enforces. It never downgrades on faith.
Jira, Linear, GitHub Issues; Claude Code, Cursor, the API. Reliable attribution even for remote / CI agents via explicit task-tagging.
Attribution and routing run on metadata. 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 |
| Drives spend down (proven model routing) | ✓shadow → quality canary → enforce | ✗ | ✗ | ✗ |
| 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 governed against the work you plan, with an optimization engine that bends the curve down. See the full comparison →
The budgeting & governance platform is in early access — we're onboarding a first cohort now, and setting pricing with them. The optimization engine already bills the simplest way: only on realized savings.
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. Attribution and routing run on metadata — task category, token counts, ticket IDs. Prompt text, model outputs, and your API key never reach our servers, and our endpoints reject any payload that contains them.
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.
Per work-type, a cheaper model is first logged in shadow (what it would have cost) and then validated by a quality canary (proven non-inferior on your own work) before it's allowed to enforce. It only moves down the capability ladder when proven good enough, and keeps hard reasoning and structured-output / tool calls on a capable model.
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 — with the optimization engine as an optional drop-in layer when you're ready to act on the spend.
The budgeting & governance platform is in early access; pricing is set with our first design partners. The embedded optimization engine bills only on realized savings (metered from real tokens, baseline minus actual) — no savings, no bill. 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