Guides → Playground & Guide → AI Currency Converter - Pricing Across 12 Currencies
Meet Hannah Schmidt. Procurement Manager at a 400-person German enterprise. "Anthropic and OpenAI quote USD. Our budget is in EUR. What's the real cost in our currency, including FX volatility?"
🔥 FX swing in 2024 added 8% to AI costs without warning.
AI vendor pricing is USD-denominated. Anthropic, OpenAI, Google all bill in USD. Your finance team works in your local currency. Without FX awareness, budgets miss reality by 5-15% per year - sometimes more in volatile periods.
Hannah's German enterprise: $50K USD/mo AI bill. At 0.92 EUR/USD: €46K. At 0.98: €49K. At 0.85: €42.5K. The range across one year is real money - and it's not modeled in most procurement budgets, which assume a single conversion rate.
Three FX strategies for AI procurement. (1) Spot-rate budgeting (current rate × 12) - simple but exposed to FX swings. (2) Forward-rate hedging (lock in for 6-12 months) - pricier but predictable. (3) USD-denominated budget line (track in USD, convert at month-end) - works for finance teams comfortable with FX volatility.
AI vendor pricing is USD-denominated. Convert to your local currency for budgeting, finance, and procurement. Live FX rates, current vendor pricing.
currency
Below: live sliders. Move them to see numbers change in real time. * Output uses the generic compute model — for precise numbers use the full calculator below.
Each input shapes your cost. Move the slider — see the impact.
Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Current converted: spend × FX rate. $50K × 0.92 = €46K/mo. €552K annual. Straightforward.
Forward range: ±FX volatility. €46K ± 8% = €42.3K-€49.7K. Annual budget should plan to the upper bound to avoid surprises.
USD spend baseline. Always track AI spend in USD too - vendor invoices, year-over-year comparisons, vendor negotiations all happen in USD. Local currency is for finance reporting only.
Same calculator, three different team sizes. Click a tab to see how the numbers shift.
Hannah's $50K/mo at 0.92 = €46K. With 8% FX buffer, plan budget €49.7K/mo. Annual €596K vs €552K nominal - €44K buffer for FX surprises.
Healthy range: Plan budget at €50K/mo (vs €46K current) for buffer
Indian SaaS company. $12K × 83 = ₹996K. With 6% buffer, plan ₹1.05M monthly = ₹12.6M annual.
Healthy range: Budget ₹10.5L/mo with 6% buffer
$8K with 25% expected swing. At ₺32 = ₺256K. Range: ₺192K-₺320K/month. Consider USD-denominated contract or FX forward to lock in.
Healthy range: Hedge or USD-denominate contract
Cost isn't the only dimension. Click any constraint — see how recommendations change.
FX exposure on AI is unavoidable for non-USD businesses. Either hedge it (forward contract), absorb it (USD-denominated budget), or buffer it (10-20% extra in local currency budget).
Currency conversion is finance, not AI. Quality unchanged.
Some accounting jurisdictions require local-currency invoices. Vendors will provide on request.
Currency conversion is public information.
Not applicable.
If your AI bill grows to 2% of your operating expenses, FX exposure becomes a board-level discussion. Plan for it before you get there.
Don't conflate USD spend (vendor metric) with local cost (finance metric). They diverge over time as FX moves.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button to load it into the calculator above.
Annual budget submission. $25K/mo × 0.78 = £19.5K. Plus 10% FX buffer = £21.5K/mo budget request. £258K annual ask.
Healthy range: Submit budget at +10% of current converted
Australian SaaS at $80K/mo. AUD weakened against USD over 12 months - local cost up 12% with no usage change. Use this analysis to justify negotiating volume discount or annual contract.
Healthy range: Quantify USD vendor exposure
Korean B2C app, $200/mo cost per user. Price at ₩300K (vs ₩276K spot) for 7% FX buffer. Customer pays predictable local price; you absorb minor FX wins/losses.
Healthy range: Set local price with FX buffer
Global SaaS. Track USD spend centrally. Convert to GBP for UK office, EUR for Germany, JPY for Tokyo at month-end FX. Saves the alternative: tracking 4 currencies separately.
Healthy range: Track USD baseline across regions
Honest limitations — every model is wrong; some are useful. Where this one falls short:
For these, use: Cost Calculator for USD baseline. Annual Forecaster for FX buffer planning.
Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.
Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.
View 3-year history for →
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.
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 →