Workload name (optional)Short description (optional)

Input tokens / callOutput tokens / callCalls / day

Per call	Per day	Per month	Per year
$0.0340	$340	$10,200	$124,100
$0.0170	$170	$5,100	$62,050
$0.0051	$51.00	$1,530	$18,615
$0.0014	$14.00	$420	$5,110
$0.0300	$300	$9,000	$109,500
$0.0300	$300	$9,000	$109,500
$0.0180	$180	$5,400	$65,700
$0.0060	$60.00	$1,800	$21,900

Defaults reflect published list pricing as of 2026-04 from OpenAI and Anthropic; verify before relying on these for budgeting and edit any cell to match your contracted rate. Calculation: (input_tokens × input_$/M + output_tokens × output_$/M) ÷ 1,000,000 × calls/day. Excludes prompt caching, batch discounts, fine-tuning, fast-mode, and data-residency premiums.

Now what?

A cost projection on a screen does not enforce anything by itself. The projection becomes a hard ceiling when you put a runtime gate in front of every LLM call — Cycles is that gate.

Try it locally10-minute end-to-end tutorialWrap one LLM call. Watch the budget enforce. Apache 2.0, self-hosted.Start tutorial →See it runRunaway-agent demodocker compose up. No API keys. $10 → $1 budget enforcement in 30 seconds.Run the demo →Read the modelLLM Cost Runtime Control ReferenceCost-incident taxonomy, multi-tenant isolation, estimation accuracy, unit economics, and rollout.Open guide →Talk to the teamWalk through your numbers with usSend the share link from this calculator and we'll respond with a sanity check on the assumptions and the rollout shape.Contact →