The platform

Cost it right, plan it, take it anywhere.

The depth behind the dashboard — how Outlay costs agentic AI usage correctly, forecasts and estimates planned work, and lets your data flow to any BI tool, warehouse, or SIEM. The attribution and governance story lives on the home page; this is everything under it.

The math

The same usage, costed two ways.

Agentic coding re-sends a large cached prefix every turn, so cache-read tokens — billed at roughly a tenth of base input — dominate the count. Price every token at the base rate, the way most spend tools and spreadsheets do, and the number balloons. We costed our own Claude Code usage both ways.

Our build · ~$340 of real Claude usagemeasured
Naive token-count tracker$2,516
Outlay · cache-aware (correct)$340

Measured on our own build sessions: 98% of input-side tokens were cache reads, so the naive method overstates by 7.4×. Reproduce it yourself: python -m outlay.dogfood --proof-only.

Cache reads are most of your bill
On agentic workloads, cache reads are the large majority of tokens — and they bill at ~10× less. Costing them right is the difference between a real number and fiction.
Per token class, per model
Input, output, cache read, and cache write are priced separately, per model — across Anthropic, AWS Bedrock, Google Vertex, and OpenAI / Azure.
Reconciled to the invoice
The cache-aware total is checked against the provider's billed figure — so finance trusts the number, not a token estimate.
Shows up the moment you connect
Your own cache-aware-vs-naive gap appears on your dashboard as soon as real usage lands — no proxy, no setup.
Plan with numbers, not guesses

Estimate a project's AI cost before you build it.

Roadmap and sprint planning shouldn't guess at compute. Hand Outlay the planned work — epics and tickets with their requirements and design docs — and it prices each one against the cost model it learned from your own delivered work, with a confidence range you can budget against.

Planned Q3 backlog · estimatedexample
SSO — SAML + SCIM requirements + design doc$3,800
Billing v2 migration large · thin scope$9,400
Flaky-test cleanup well-specified$1,200
Total estimate~$14,400
Likely range$9,900 – $19,900

Illustrative. Each item priced from your own per-work-type history; thin-scope items get a wider band, not a false-precise number.

From your own history
Each item is priced against the cost-per-work-type model learned from your delivered tickets — not a generic benchmark.
Requirements + design docs
Feed the scope you already wrote. The more context — requirements, design docs, story points — the tighter the range.
Confidence, not false precision
Every estimate carries a low–high band and a confidence tier; under-specified work is flagged to tighten, never silently guessed.
Budget before you commit
See what an epic or a sprint will cost in AI before it's built — so you can rescope, resource, or defer with eyes open.
In the box

From line item to control plane.

Spend mapped to your roadmap

LLM and coding-agent spend attributed to the ticket, epic, sprint, and team it belongs to — the allocation finance has never had for AI.

Forecast by scope of work

Predict a quarter's AI cost from its open scope, then watch budget-vs-actual burn down in real time.

Guardrails — alert or enforce

Pace-based alerts (ok → warn → over) by default, read-only. Or set a program hard cap the opt-in gateway enforces — block or route-down over-budget work automatically.

Accuracy you can check

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.

Works with your stack

Jira, Linear, GitHub Issues; Claude Code, Cursor, the API. Reliable attribution even for remote / CI agents via explicit task-tagging.

Privacy by architecture

Attribution runs on metadata — connected with read-only tokens. Prompt content, model outputs, and your API key never leave your environment.

Your data, your way

It's your spend data — take it anywhere.

Outlay is a hub, not a silo. Export FOCUS-aligned cost rows for any FinOps/BI tool, pull them programmatically through a read-only API, and stream your audit log to your SIEM — all reconciled to the provider invoice, to the cent. No lock-in, ever.

Outlayspend mapped to work FOCUS CSV · BI APISnowflake, BigQuery, your BI Audit-log APISplunk, Datadog, S3

FOCUS-aligned export

Per-ticket charge rows using the FinOps Open Cost & Usage Spec column names — load straight into any FOCUS-aware FinOps or BI tool.

# spec column names · per-ticket rows BilledCost,ServiceCategory,Tags{team,work_type}

BI & warehouse API

A token-authed, rate-limited endpoint returns your latest report as JSON for any warehouse or BI pipeline — the same attributed numbers as the console.

GET /api/v1/spend → { total_usd, rows[], data_quality }

Audit log → your SIEM

Stream every security event to Splunk, Datadog, or S3 — poll the audit API with a cursor for gap-free ingestion, or export CSV for GRC.

GET /api/v1/audit?since=cursor

Plus HMAC-signed webhooks for budget & anomaly events (retried, with a delivery log), and a printable month-end close pack — emailed automatically with the FOCUS CSV attached.

Integrations

Works with every model and every tool in your stack.

Outlay reads spend from every major provider and connects it to the systems where the work actually happens — read-only, no app rewrite, no prompts leaving your environment.

AI providers
  • Anthropic API + admin usage
  • OpenAI
  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex
Coding agents
  • Claude Code session transcripts
  • Cursor admin usage
  • CI / remote agents task-tagging
Code & trackers
  • GitHub issues + PRs
  • Jira
  • Linear
Warehouse, BI & SIEM
  • FOCUS CSV export
  • BI / warehouse API
  • SIEM audit-log export
  • Webhooks

Reconciles against your provider invoice — Anthropic, AWS Cost Explorer, GCP Cloud Billing, OpenAI Costs — so finance trusts the number, not a token estimate. Become a customer →

See it on your own spend.

Connect read-only in minutes — your real AI spend mapped to work, costed correctly, forecast, and yours to export.

Read-only to start · prompts never leave your environment · no app rewrite