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Find Your Calculator - Which Tool Fits Your Question

🚀 Open the calculator

Meet Anyone first time visiting. Trying to figure out what to use. "There are 26 calculators. Which one matches what I'm actually asking?"

🔥 Wasted 15 minutes in the wrong calc. Bounced.

The story

Most cost analysis fails at the first step: picking the wrong frame. 'How much will AI cost' is too broad to answer. 'How much will Claude cost for 10K customer support tickets/day' is answerable in 30 seconds with the right calc.

Think of these calcs in three layers. (1) Foundation - what does AI cost period (cost-calculator, scale-projection, annual-cost-forecaster). (2) Decisions - A vs B framing (RAG vs FT, batch vs realtime, self-host break-even). (3) Specialized - domain math (vision, audio, RAG pipeline, fine-tuning, vector DB).

Match your question to one of three patterns. 'How much will [thing] cost?' → Foundation calc. 'Which is better, A or B?' → Decision calc. 'What's the cost specifically for [domain]?' → Specialized calc. Most queries fit cleanly into one bucket.

🎮 Playground

Find Your Calculator Playground

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

Which calculator should you start with?

Tell us where you are and what you’re building — we point you to the right calculator.

✓
Use this calculator—

💡Maturity and use case pick the calculator; spend sharpens it — high spend + a mature org jumps you straight to full TCO.

Three real scenarios

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

$0.00 / month ≈ $0.00 / year

Pre-launch chatbot. Want to estimate. Use /tools/cost-calculator (full bill projection) and /tools/token-estimator (per-conversation math). 5 minutes total.

Healthy range: Start: cost-calculator + token-estimator

See inputs used
userMaturityLevel
1
currentMonthlyAiSpendUsd
0
primaryUseCaseScore
1

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.

$50,000 / month ≈ $600,000 / year

Strategic question, not tactical. Annual forecaster for board deck. Concentration risk for vendor strategy. Skip per-calc-task tools.

Healthy range: annual-cost-forecaster + vendor-concentration-risk

See inputs used
userMaturityLevel
5
currentMonthlyAiSpendUsd
50,000
primaryUseCaseScore
3

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 →

Top 3 vendors right now (most-used context)

Verified 13 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: Find Your Calculator - Which Tool Fits Your Question

26 calculators, one decision tree. Pick the right one based on your role, your spend, your AI maturity. Don't waste 20 minutes in the wrong calc.

Reading your result

Maturity 1-2 (early): Start with /tools/cost-calculator. Baseline what AI costs at your usage. Don't optimize yet.

Maturity 3 (optimizing): /tools/multi-model-router for routing, /tools/prompt-cache-roi for caching, /tools/token-reduction-analyzer for compression.

Maturity 4 (portfolio): /tools/vendor-concentration-risk for risk, /tools/scale-projection for growth, /tools/annual-cost-forecaster for budgeting.

Maturity 5 (strategic): /tools/self-host-breakeven, /tools/rag-vs-fine-tuning, /tools/agentic-ai-stack for architecture decisions.

What "good" looks like:
  • If your primary question is 'how much': cost-calculator, scale-projection, annual-cost-forecaster
  • If 'should I switch to X': rag-vs-fine-tuning, batch-vs-realtime, self-host-breakeven, annual-vs-monthly
  • If 'specifically for [domain]': vision, audio, embedding, vector-db, fine-tuning, RAG-pipeline
  • If 'how do I cut my bill': token-reduction-analyzer, prompt-cache-roi, multi-model-router

What this calculator can't tell you

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

For these, use: Browse the full guide library for any specific calc not surfaced here.

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. Foundation calcs first Get baseline before optimizing
  2. Decision calcs at architecture choice RAG vs FT, batch vs RT, self-host
  3. Specialized calcs at deep optimization Domain-specific math

Don't skip levels. Foundation → Decision → Specialized. Trying to use a specialized calc without baseline math is like optimizing the wrong loop.

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

Foundation: full bill projection →

Always start here if unsure.

 Most-asked decision →

Strategic choice, big impact.

 Most popular specialized calc →

Full RAG architecture.

Methodology

Source
/ai-cost-economics
Extraction
Calc routing logic from analysis of 1000+ user sessions across the calc library.
Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/20/2026, 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 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
Voyage AI
2026-07-21
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 →