Quarterly AI Spend Forecaster

Your AI bill is about to surprise your CFO. As your team shifts from chat-style usage (bounded by human attention) to agentic workflows (bounded only by your API budget), monthly spend can jump 5-10× without a single new seat. This calculator forecasts the impact and surfaces optimizations from V2.1 vendor data.

Why CFOs care now: Q1 2026 Anthropic API bills hit companies 5-10× their budget because Claude Code agentic workflows started running 24-hour sessions instead of 5-minute chat turns. Cursor users hit similar shocks. Most FinOps tools show last month's spend; none forecast the agentic shift forward. We do.
What this calculator does

Forecast next quarter's AI bill including the agentic shift — when teams move from chat-style usage to 24-hour agent sessions, monthly spend can jump 5-10× without a single new seat.

Why use it
  • Q1 2026 Anthropic API bills hit companies 5-10× their budget because Claude Code sessions replaced chat turns
  • Most FinOps tools show last month's spend; none forecast the agentic shift forward
  • Lets you walk into the CFO conversation with a defensible projection, not a surprise

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.

▶Playground A quick, visual way to see which factors move your result the most. Open the playground → ▤Calculator Enter your real workload for a precise result you can apply to your own usage. Go to the calculator →
Quarterly Spend Forecaster Playground
Drag the sliders for an instant estimate.
What will next quarter cost?

Projects next quarter from this one with growth, seasonality, and a price-variance buffer. Buffer fixed at 10%.

Projected next quarter
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Change the input sliders below to see new estimates.

How we got this estimate
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💡Next quarter = this quarter × growth × seasonality × buffer. Negative growth is allowed if you expect a slowdown.

Quarterly Spend Forecaster Calculator

Enter your exact numbers for a precise result.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges) 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.
🎮 Interactive Guide & Calculator Playground →

Start from a scenario (sets team + agentic assumptions; pick your tools below)

1. Which AI tools are you using?

Click to select. Enter your last month's actual spend if you have it; otherwise we estimate from team size.

2. Your team

Number of people using AI tools today.How to choose: Count active users, not licensed seats. Idle seats inflate the forecast.Read the full guide → Hint: How many people use AI today.
People using AI tools
Expected headcount growth that touches AI tools, percent per quarter.How to choose: Match your hiring plan. 0% if flat. Be honest — most teams over-plan growth.Read the full guide → Hint: How many you'll add this quarter.
% increase by end of quarter

3. The agentic shift

What % of your team's AI usage is agentic (vs traditional chat)? Most teams are at 15-30% today and will be at 50-70% by mid-2026.
Share of AI traffic that is autonomous (agents, long-session) vs interactive (chat).How to choose: 0-10% if mostly Cursor/ChatGPT chat. 30-50% if Claude Code or workflow agents in production.Read the full guide → Hint: Share of work done by agents today, as a %.
% of usage today
Expected agentic share next quarter — the lever that drives the spend shock.How to choose: Trend honestly: if you are piloting Claude Code this month, agentic share doubles by next quarter. Most CFOs miss this jump.Read the full guide → Hint: Where you want that share by quarter-end, as a %.
% you're aiming for

Results

Quarterly forecast

Optimization opportunities

Sourced from V2.1 vendor pricing data - modifier rates, dual-pricing discounts, peer plan comparisons. All savings stack.

Per-tool breakdown

ToolCurrent /moQ low /moQ mid /moQ high /mo
🎯 Use your Quarterly Spend Forecaster results to…
  • 📅 Plan the quarter — Set a defensible quarterly AI budget with low, mid, and high bands.
  • ⚡ Model the agentic shift — See how moving from chat to agentic usage multiplies per-seat spend.
  • 💡 Find optimization headroom — Spot the savings available if you tune tools before the quarter scales.
  • 🔌 Integrate with your AI agents — MCP available for agentic workflow integration. Plug AICost.ai into your agents to surface real-time cost intelligence.
📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-28.
  • 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
2026-07-28
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Anthropic Docs
2026-07-28
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 631 days captured
OpenAI
2026-07-28
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 632 days captured
Google AI
2026-07-28
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 607 days captured
Google Vertex
2026-07-28
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 607 days captured
DeepSeek
2026-07-28
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 546 days captured
xAI
2026-07-28
https://x.ai/api
Daily snapshot since Nov 2024 · 464 days captured
Mistral
2026-07-28
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 605 days captured
Cohere
2026-07-28
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Voyage AI
2026-07-28
https://docs.voyageai.com/docs/pricing

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 →