The platform

Five products. The depth behind each.

The same five products you meet on the home pageLedger, Forecast, Controls, Procure, Route — and the engineering under each: how Outlay costs agentic AI correctly, prices planned work, enforces budgets, buys compute efficiently, and routes every request to the cheapest model that's good enough. Plus the platform layer that takes it all anywhere.

1.1 · Ledger — every dollar, costed right

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 Gemini / Vertex, and OpenAI / Azure.
Reconciled to the invoice
The cache-aware total is checked against the provider's billed figure — so business 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.
1.2 · Forecast — price the quarter, not guess it

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.
1.3 · Controls — budgets that act, not just alert

From line item to control plane.

Group teams and projects into programs with one budget. By default Controls is read-only — pace-based alerts (ok → warn → over) fire before a program lands over. Flip on the opt-in gateway and a hard cap blocks or routes over-budget work down automatically. It fails open: our downtime can never block your traffic — only a budget you set being exceeded can.

Program budgets · this monthexample
Platform hard cap · enforced$61k / $50k
Launch — Q3 hard cap · on track$28k / $40k
Growth experiments alert-only$12k / $30k

Platform crossed its cap — 1,240 calls routed down to a cheaper model this month, no work blocked.

Alert, or enforce
Pace projections flag a scope trending over — read-only, Outlay never touches your traffic. Or opt into the gateway for a true hard cap.
Budget by what matters
Teams and projects roll into programs with one budget; budget moves where priorities move.
Work vs. non-work, stopped
Tag a side-project key once; stop non-work usage per team via the gateway. Work is never blocked — and no prompt is read.
Fails open
Enforcement is opt-in and degrades safe: only your own budget ceiling can ever stop a call, never our uptime.
1.4 · Procure — buy compute efficiently Coming soon

The most efficient mix of seats and API credits.

Per-person spend shows who belongs on a flat vendor seat and who belongs on API credits. Procure computes the most efficient split from your real usage — plus commitment sizing and a renewal pack you take to the vendor. It's the analysis nobody buying blind can run. Coming soon.

23 people on AI tools · priced two waysexample
All-API status quo$27.5k /mo
Optimal · 19 seats + 4 on API$6.0k /mo

Illustrative — save $21.5k/mo, shown with its assumptions. In the app, Procure runs on your real per-person spend.

Seat vs. API, per person
Heavy users belong on a flat seat; light users on credits. Procure finds the crossover from your own spend.
Commitment sizing
Should you commit, and how much? Procure sizes the commitment against your usage curve — not a vendor's pitch.
The renewal pack
Export the negotiation-ready pack — the artifact you take to the vendor at renewal.
Assumptions on the table
Every number shows the rate and headcount behind it — no black-box savings claim.
1.5 · Route — the cheapest model that works Coming soon

Route every request to the cheapest model that's good enough.

Route scores each request and sends it to the least-expensive model that clears your quality bar — with automatic fallback when a cheaper model would miss it. You keep the output quality and cut the model bill, and you're billed only on the savings Route actually realizes. Coming soon.

Model spend · before vs. after routingexample
All-frontier status quo$40k /mo
Routed · cheapest good-enough$15k /mo

Illustrative — save $25k/mo with quality held at your bar. In the app, Route measures realized savings on your own traffic.

Provably good enough
Each task class gets a quality gate; Route only downgrades a request when it clears the gate.
Automatic fallback
If a cheaper model misses the bar, Route escalates — so quality never silently drops.
Billed on realized savings
You pay a share of what Route actually saves — never a flat fee for a promise.
Metadata-only routing
Routing decisions run on your box; prompt content and keys never leave your environment.
Under all five

The platform layer.

What every product shares: the connectors, the accuracy proof, and the privacy architecture.

Works with your stack

Jira, Linear, GitHub Issues; Claude Code, Cursor, the API — across Anthropic, AWS Bedrock, Google Vertex, and OpenAI / Azure. Reliable attribution even for remote / CI agents via explicit task-tagging.

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.

Privacy by architecture

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

Take it anywhere

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 Gemini native token accounting
  • 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 business 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