Will your AI budget survive the year?

Budgets set before agents are structurally wrong: re-forecast from your spend-to-date with the agentic share compounding, find the breach month, and size the variance reserve finance now expects.

What this calculator does

Re-forecasts your AI plan year from spend-to-date with the agentic share compounding under a named scenario — projected annual spend, the month you breach the baseline, and a variance reserve sized inside the 20-40% CFO band.

Why use it
  • Most 2026 AI budgets were set before agents changed consumption by an order of magnitude — whole-year budgets exhausted by April became the year's defining FinOps story.
  • The emerging CFO guidance is a 20-40% variance reserve on AI lines; this computes where YOUR drivers land in that band, plus the breach month that deadlines the finance conversation.

New to this calculator? Start with the ⚡ Playground — a few sliders, instant ballpark. Then switch to the 🧮 Calculator for your exact number.

Two ways to use this: visualize in the Playground, then get your number in the Calculator.

Budget Variance Playground
Drag the agentic share to watch the plan-year projection move.
Will the plan-year budget hold?

Drag the agentic share to watch the projected monthly spend move. The full calculator adds spend-to-date, the breach month, and the reserve.

Estimated monthly cost
Change the input sliders below to see new estimates.

How we got this estimate

💡Only the agentic share compounds — the bigger it is, the faster the projection bends away from the plan line. That bend is exactly what fall-2025 budgets missed.

Agentic Variance Reserve Calculator

Your baseline, spend-to-date, and agentic share → revised forecast + breach month + reserve.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; Ramp 2026: heaviest AI spenders see 50%+ single-month spikes in ~1 of 4 months - agentic workloads drive the variance.) so you can move forward now — your result gets more accurate as you replace them with your own measured numbers. Values marked * are rough estimates.

Sets baseline, months elapsed, spend-to-date, agentic share, and scenario.

Hint: The number in the plan — probably set last fall, before the agents arrived.
Hint: How far into the plan year you are. Use 0 to size a reserve for next year.
Hint: From your invoices. Leave 0 to assume you've spent exactly to plan so far.
Hint: Share of today's spend that is agents / coding assistants / automated pipelines — the part that compounds.
Hint: Named anchors, not predictions: agentic tasks burn 5-30× a chatbot; per-dev consumption rose ~18.6× in 9 months in 2026 telemetry.

Results

Enter your baseline, months elapsed, spend-to-date, and agentic share, then re-forecast to get the projected year, the breach month, and the reserve to hold.
📖 Data sources & methodology 158 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-21

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-21.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-07-21
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 624 days captured
Anthropic Docs
2026-07-21
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 624 days captured
OpenAI
2026-07-21
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 625 days captured
Google AI
2026-07-21
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 600 days captured
Google Vertex
2026-07-21
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 600 days captured
DeepSeek
2026-07-21
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 539 days captured
xAI
2026-07-21
https://x.ai/api
Daily snapshot since Nov 2024 · 457 days captured
Mistral
2026-07-21
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 598 days captured
Cohere
2026-07-21
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 624 days captured

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →