Annual Cost Forecaster · for CFOs and finance teams

Project your AI spend for the next 12 months

Month-by-month forecast with growth assumptions and seasonality. See when you'll breach your budget.

Pricing verified: 2026-06-05 Seasonality patterns built in
📅 Schedule a meeting via AvatarVA ✉️ Email [email protected]
Annual Cost Forecaster full size
What this calculator does

Project your next 12 months of LLM spend based on current usage + expected growth.

Why use it
  • Most "AI budget" conversations use last month × 12 — which misses seasonality and growth
  • Model usage growth at realistic rates: 10% MoM = 213% annual growth, not 120%
  • Generate a board-ready chart of expected vs worst-case vs best-case
📊 Calculator at a glance
🎛 CALCULATOR
📊 Baseline + growth assumptions
Take last month's bill as the baseline.
8%/mo compounds to 2.5x/yr. Early-stage: 10-15%. Established: 3-8%. Mature: 0-3%.
Historically prices decline, but newer flagship models can push average up. Pick your view.
What you've allocated for the next 12 months of AI spend.
Simulate optimization kick-in.
📈 RESULTS
📋 Example Workload - change any field to see your actual cost
12-month total forecast
-
-
Peak month
-
Budget status
-
Breach month
-
Avg monthly
-

12-month forecast curve

Monthly cost projection vs budget ceiling (dashed red line).

💡 Recommendations
    📅 Month-by-month breakdown

    Red rows indicate budget breach (cumulative spend exceeds annual ceiling).

    Month Monthly cost vs baseline Cumulative Budget used
    Budget planner → Find the savings → Get a FinOps retainer →
    🎯 Use this result to
    📅 Schedule a call to apply this to your workload

    Go deeper

    Our playbooks on cutting this number.

    💰
    Budget Planner
    Allocate across use cases
    📈
    Scale Projection
    Single-step 10x projections
    🔀
    Multi-Model Router
    Biggest optimization lever
    📊
    AI Unit Economics
    The fundamentals

    The calculator's an estimate. Want the real number?

    A 5-day Quickscan ($1,500) reviews your actual usage across every pillar — financial, reliability, governance, privacy, MLOps, observability — and returns a concrete savings plan.

    Book a Quickscan →
    📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

    Methodology

    • All prices are USD per 1 million tokens, current as of 2026-06-05.
    • 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
    2026-06-05
    https://www.anthropic.com/pricing
    Daily snapshot since Sep 2023 · 578 days captured
    Anthropic Docs
    2026-06-05
    https://platform.claude.com/docs/en/about-claude/pricing
    Daily snapshot since Sep 2023 · 578 days captured
    OpenAI
    2026-06-05
    https://openai.com/api/pricing/
    Daily snapshot since Sep 2023 · 579 days captured
    Google AI
    2026-06-05
    https://ai.google.dev/gemini-api/docs/pricing
    Daily snapshot since Dec 2023 · 554 days captured
    Google Vertex
    2026-06-05
    https://cloud.google.com/vertex-ai/generative-ai/pricing
    Daily snapshot since Dec 2023 · 554 days captured
    DeepSeek
    2026-06-05
    https://api-docs.deepseek.com/quick_start/pricing
    Daily snapshot since May 2024 · 493 days captured
    xAI
    2026-06-05
    https://x.ai/api
    Daily snapshot since Nov 2024 · 411 days captured
    Mistral
    2026-06-05
    https://mistral.ai/pricing
    Daily snapshot since Dec 2023 · 552 days captured
    Cohere
    2026-06-05
    https://cohere.com/pricing
    Daily snapshot since Sep 2023 · 578 days captured
    Voyage AI
    2026-06-05
    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 →