AI is your fastest-growing cost. And your least accountable one.
Watt is the OpenAI-compatible platform your engineers already run on — one API for every model — that finally makes AI spend accountable to finance. See where every dollar went, whether it was spent efficiently, and how it belongs in your P&L and balance sheet.
A cost this big, and no one can account for it.
Finance gets a large, rising bill from a few providers — booked as an opaque lump in COGS or R&D. The questions you'd ask of any other line item this size go unanswered:
Where did it go?
Not split by product, team, feature, customer, or use case. Just a total.
What did we get?
No cost per user, per workflow, per ticket, per dollar of revenue.
Was it wasted?
Over-powered models, resent context, retries — invisible on the invoice.
How do we book it?
Which part is opex, which is a prepaid asset, which is a commitment?
Turn an unaccountable bill into a board-ready number.
Four jobs, built on the usage Watt already meters — because every call runs through it.
Where every dollar goes
Every dollar traced to product, team, feature, customer, and model — then turned into unit economics finance can defend: cost per user, per workflow, per resolved ticket, and AI cost as a % of revenue.
Agent efficiency & waste
Watt sees each call, so it flags spend that didn't need to happen — over-powered models, redundant context, retries and runaway loops — and quantifies the savings you can capture now.
The P&L & balance-sheet view
Maps the operating portion across COGS / R&D / S&M, and surfaces what behaves like an asset or commitment — prepaid credits, reserved capacity, and how much is going unused.
Board-ready in one click
It all collapses into a board pack: spend drivers, unit economics, efficiency, forecast, commitment coverage, and P&L/BS treatment — in language a board understands.
Spend by product
Spend by model tier
Efficiency score
Where the waste is
- Over-powered models$58k
Frontier model on tasks a mid model handles - Redundant context$34k
Full history resent every turn, not compressed - Retries & agent loops$25k
Failed calls and loops with no ceiling
P&L treatment — where AI spend lands
| Line | Driver | Quarter | % |
|---|---|---|---|
| COGS opex | Customer-facing inference | $297.2k | 61% |
| R&D opex | Model / prompt dev, evals | $131.5k | 27% |
| S&M opex | In-product & marketing | $58.5k | 12% |
| Total | $487.2k | 100% |
Balance sheet — compute as an asset
| Item | Value | |
|---|---|---|
| Prepaid compute credits | asset | $250.0k |
| Committed capacity (take-or-pay, 12mo) | commitment | $1.20M |
| Capitalizable model dev — review | review | $96k |
AI Spend & Efficiency — Board Pack
- Why it rose: Copilot launch drove 42% of spend; a 58% frontier-model mix is the largest single driver.
- What we got: cost per active user fell 6% QoQ to $0.29 — spend is scaling sub-linearly to usage.
- Efficiency: $117k/yr flagged wasteful; $28k already captured this quarter via automated routing.
- Financial treatment: 61% COGS / 27% R&D / 12% S&M; $250k prepaid credits and a $1.2M take-or-pay commitment tracked as balance-sheet items.
- Risk & ask: $468k of committed capacity under-utilized — renegotiate the tier or reallocate workloads next quarter.
Why the numbers are trustworthy: Watt is in the request path.
The accountability layer isn't a spreadsheet bolted on after the invoice. It's built on the platform your engineers already run on — one OpenAI-compatible API for every model. That's why Watt sees each call at the moment of spend, and can act on it.
One API, every model
A single OpenAI-compatible endpoint for Claude, GPT, Gemini, DeepSeek, MiniMax and GLM — choose the model per request, one key.
Drop-in integration
Change one base URL. Your existing OpenAI SDK code just works — no migration, nothing to rip out.
Centralized USD billing
One account, one consolidated USD invoice. Prepaid credits or subscription — the whole org on a single bill.
AI FinOps visibility
Spend, tokens and cost broken down by model, team, user and API key — in real time. This is the raw material the accountability layer is built on.
Enterprise controls
Teams and roles, per-key default & allowed models, plus rate and spend limits enforced by the policy engine.
One rate-limit pool
Every provider throttles you separately. Watt gives you one rate limit across all models — one number, not seven.
# pip install openai — same SDK, one base URL from openai import OpenAI client = OpenAI( base_url="https://api.watt.computenotes.co/v1", api_key="watt-live-sk-...", ) resp = client.chat.completions.create( model="claude-sonnet-4-6", # or gpt-4o, gemini-2.5-pro, deepseek-chat ... messages=[{"role":"user", "content":"Hello, Watt"}], )
Buy credits. Use any model. No subscription.
Watt is prepaid AI infrastructure: top up credits and spend them across every model through one key — credits never expire, volume bonus on larger packs. Org management (teams, budgets, the full FinOps & board reporting) lives in the Enterprise tiers.
Built for the person who has to answer for the number.
CFO / VP Finance
Turn an unaccountable bill into attributed cost, unit economics, and a defensible place in the accounts.
CEO
Walk into the board with a straight answer on AI spend, efficiency, and what it's returning.
Platform / Eng lead
Keep the one API and drop-in workflow you already like — and stop having to defend the bill by hand.
Get a free AI spend teardown.
Point Watt at your usage and get the picture back in days: where every dollar went, how much is waste, and how it belongs in your financials — ending on a board-ready one-pager.
No rip-and-replace — Watt reads your existing providers. The teardown is free.