Guides → Playground & Guide → Region Cost Map - Where to Run AI Workloads by Region

Region Cost Map - Where to Run AI Workloads by Region

🚀 Open the calculator

Meet Sven Larsen. Cloud Architect designing a multi-region AI platform. "AWS Bedrock costs differ by region. Where should I run inference for our European customers without breaking GDPR?"

🔥 Eu-west-1 is 15% more expensive than us-east-1 - but routing through US is GDPR-risky.

The story

Same model, different prices by region. AWS Bedrock charges 5-25% more in Europe than US. Azure OpenAI charges differently in Sweden vs East US vs Australia. Google Cloud has 3-tier pricing (Tier 1 cheap, Tier 3 premium). The naive 'pick the cheapest region' breaks GDPR/data residency for many workloads.

Sven's challenge: serve European customers with GDPR-compliant residency, but minimize cost. Eu-west-1 (Ireland) is the cheapest GDPR-compliant region (15% premium over us-east). Eu-central-1 (Frankfurt) is 18% premium. UK regions (london) are 20% premium post-Brexit.

Three regional strategies. (1) Single region, residency-compliant - simple, may overpay. (2) Multi-region routing by user - cheapest-compliant per user. (3) Region-by-workload - sensitive workloads in residency, non-sensitive in cheapest. Most teams converge on (3).

🎮 Playground

Region Cost Map Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

What does your region cost vs the cheapest?

Takes baseline (cheapest-region) cost and adds a premium for your region and compliance constraints.

Estimated monthly cost
—

💡Cost = base × (1 + region premium × compliance constraint). The effective premium is in “Why this number”.

Three real scenarios

Same calculator, three team sizes. Click a tab to see how the numbers shift.

$8,400 / month ≈ $100,800 / year

Pure US workload. Single region (us-east-1 or us-west-2). Pick by latency to your users. ~$3.5K/mo.

Healthy range: $3.5K/mo, single region

See inputs used
monthlyVolumeRequests
500,000
avgInputTokens
2,000
avgOutputTokens
400
regionPremiumPct
0
complianceConstraintRatio
0
cheapestRegionPricePer1MUsd
7

Use cases

Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.

$38,640 / month ≈ $463,680 / year

100% European customer data. Eu-west-1 (Ireland) - cheapest GDPR-compliant. Single region for simplicity. ~$32K/mo. Don't try to save 10% by routing to US - GDPR fines dwarf the savings.

Healthy range: $32K/mo single EU region

See inputs used
monthlyVolumeRequests
2,000,000
avgInputTokens
2,000
avgOutputTokens
400
regionPremiumPct
15
complianceConstraintRatio
100
cheapestRegionPricePer1MUsd
7

Ready to run the numbers?

Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Vendors with explicit regional pricing

Verified 11 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    gpt-5.1-codex-mini
    $0.250 in · $2.00 out ·
  3. 3
    Command
    $1.00 in · $2.00 out ·

About this calculator: Region Cost Map - Where to Run AI Workloads by Region

Same model, dramatically different costs by region. AWS Bedrock us-east vs eu-west, Azure OpenAI Sweden vs East US, Google Cloud us-central1 vs asia-southeast1. Pick the cheapest compliant region for your workload.

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.

▸ Model: The model you want to price. The same model is offered in multiple regions, often at different prices.
How to choose: Pick the model you run; the tool compares its price across every available region.
▸ Access via: How you call the model: the vendor’s direct API, or through a cloud platform like AWS Bedrock, Azure, or Google Vertex. Platform access can change both price and available regions.
How to choose: Choose how you actually access it. If data must stay in a specific region for compliance, platform access there may be your only option — at a premium.
▸ Input tokens / req: Tokens you send per request (your prompt). ~750 words = 1,000 tokens.
How to choose: Estimate a typical request size; multiplied by requests/day, this is half your token bill.
▸ Output tokens / req: Tokens the model generates per request; output usually costs more than input.
How to choose: Estimate a typical response length.
▸ Requests per day: How many requests you send per day — the workload multiplier.
How to choose: Use real or projected daily volume; the regional price gap scales with this.
📋 Typical values & starting points Don’t know a value yet? Start with these broad, sourced ballparks — the calculator’s ▾ Typical menus pre-load the same options.
Input Default Typical ballparks
avgInputTokens 2,000 Chat turn · ~500 in = 500 · Typical task · ~2K in = 2,000 · RAG-heavy · ~3.5K in = 3,500 · Long-context · ~10K in = 10,000
avgOutputTokens 400 Classification / label · ~50 out = 50 · Short answer · ~300 out = 300 · Typical response · ~800 out = 800 · Long-form · ~1.5K out = 1,500
monthlyVolumeRequests moves the needle 1,000,000 —
regionPremiumPct moves the needle 15 —
complianceConstraintRatio moves the needle 60 —
cheapestRegionPricePer1MUsd 7 —

Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers. Try them live in the calculator; API & agent users get the same data from the MCP resource aicost://input-reference/region-cost-map.

Reading your result

Single-region cost = volume × per-request × (1 + premium). Sven: 1M req × $0.014/req × 1.15 = ~$16K/mo single-region in EU.

Multi-region savings = volume × non-compliant-share × premium. Sven: 1M × 40% × 15% × $0.014 = ~$840/mo savings (5% of bill) by routing 40% of non-residency traffic to US.

Operational complexity scales with regions. 1 region: simple. 2 regions: noticeable. 3+ regions: real platform investment. Pick the smallest set of regions that hits compliance.

Latency benefits compound with cost benefits. EU users hitting EU region get 50-100ms latency benefit. If your UX is latency-sensitive, multi-region is mandatory regardless of cost.

What "good" looks like:
  • Single-region default: Cheapest US region for non-residency, single EU region for residency
  • Multi-region budget: 1M+ monthly requests AND 30%+ residency-bound traffic
  • Vendor regional spreads: AWS 5-25%, Azure 0-20%, Google 0-30% (Tier 3)
  • Watch out: Some 'global' models route through specific regions silently (check the docs)

What this calculator can't tell you

Honest limitations. Every model is wrong; some are useful. Where this one falls short:

For these, use: Cost Calculator for full-bill projection. Vendor Concentration Risk for multi-region hedging.

Trade-offs

Cost isn't the only dimension. Click any constraint to see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Single region simplest Pick cheapest compliant
  2. Multi-region savings 5-15% at scale Worth complexity above 1M req/mo
  3. Routing logic is real engineering Account for it

Multi-region is rarely cost-justified below 1M requests/month. Above that, you're leaving 5-15% on the table by staying single-region.

Most popular

Everything above is the 80% case. The last 20% is where the money is.

The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.

  • An hour with the people who built the engines
  • Report the same day
  • The fee credits toward any AICost plan
  • Two slots a week
Book a Solution Session: $299 → or $99 for small business →

Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →

Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

🚀 Open the full calculator →

Where to go next

Project full bill at chosen region →

Validate the regional cost in full context.

 Hedge vendor + region together →

Multi-vendor multi-region for resilience.

 Self-host breakeven by region →

Compute capacity differs by region too.

Methodology

Source
/ai-cost-economics
Extraction
Regional pricing pulled monthly from AWS, Azure, Google Cloud documentation.
Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/27/2026, 8:00:00 PM

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.

3 years of pricing history

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 →
📖 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 →