Beta aicost.ai cost engines are in beta. Estimates only. See Terms.
MSP Economics · AICost.ai cost decision engine

🤖 What does AI actually save — after review, rework, and model costs?

The honest version: savings net of human review, rework, failed-run cleanup, and API/platform/governance costs. Supply a model + tokens and the API cost is priced vendor-exact from the pricing SSOT.

Grounded typical ranges, sourced* Decision-first — a verdict and a next step MCP engine: aicost.msp-ai-roi
Net benefit / month
$3,229
What this calculator does

The honest version: savings net of human review, rework, failed-run cleanup, and API/platform/governance costs. Supply a model + tokens and the API cost is priced vendor-exact from the pricing SSOT.

Every number recomputes live; the same logic answers on the web, in the PDF one-pager, and to AI agents over MCP — identical results on every surface.

Why use it

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 →
⚡ AI Automation ROI Playground
The 3 factors that move this the most — drag and watch the decision change.
Net benefit / month
$3,229 POSITIVE ROI
→ DEPLOY WITH REVIEW GATES
80 %
1.5
1200
Try a scenario: 📄 Print one-pager ✉ Email me this

AI Automation ROI — your exact numbers

Enter your shop's numbers. Everything recomputes live.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; AI automation ROI (2026): success rates 75-88% measured (not brochure); review 0.3-5 min/task by type; rework 8-15%; loaded tech $58-80/hr. Model+tokens path prices API from pricing SSOT. * = typical.) 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.
The task you want to automate.
Human time today.
Completed without human takeover — measured, not brochure.
Fully loaded.
The automation platform.
Not everything qualifies.
Someone checks the output.
Haircut on gross savings.
With tokens below, API cost is priced from the SSOT instead of the manual figure.
Optional workload.
Optional workload.
Used only when no model is selected (estimated*).
Keep non-zero — someone watches the machine.
For payback.
Verdict
POSITIVE ROI

Net $3229/mo at expected settings and still $1683/mo at conservative — the automation clears its own costs.

Net benefit / month
$3,229
Hours released / month
57.3 h
Technician-equivalents
0.41
Payback on integration
1.9 months
API cost / month
$220
API cost basis
manual input (estimated*)
What to do
DEPLOY WITH REVIEW GATES
Why
Clears costs even at conservative settings. Deploy with human review gates and track actual vs modeled monthly.
Pricing link
Deflection is repricing seat labor across the industry (2026: $90/seat labor dropping to $60–$70 with AI-assisted triage*) — feed the released hours back into the Minimum Profitable Price calculator.

Method memo

Gross minutes saved are haircut by review (1.5 min/task), rework (12%), and failed-run cleanup. API basis: manual input (estimated*). Released hours are capacity, not cash, unless a hire is avoided or the time is billed.

Questions to pressure-test with

  1. Is the success rate measured on your tickets or quoted from a vendor deck?
  2. Who reviews automated output, and is their time in the model?
  3. What is the rollback plan when the model degrades? Governance cost covers monitoring — keep it non-zero.

Assumptions

  • Failed-run cleanup assumed at 30% of task time for non-completed automations.
  • Technician-equivalent uses 140 productive hours/month.
  • Manual API figure is estimated*; supply a model + tokens for SSOT pricing.

Values marked * are analyst estimates rather than vendor-verified data.

📖 Data sources & methodology 150 text models · 9 embeddings · 40 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-09-19

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-09-19.
  • 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
2026-09-19
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 683 days captured
Anthropic Docs
2026-09-19
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 683 days captured
OpenAI
2026-09-19
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 684 days captured
Google AI
2026-09-19
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 659 days captured
Google Vertex
2026-09-19
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 659 days captured
DeepSeek
2026-09-19
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 598 days captured
xAI
2026-09-19
https://x.ai/api
Daily snapshot since Nov 2024 · 516 days captured
Mistral
2026-09-19
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 657 days captured
Cohere
2026-09-19
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 683 days captured

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.
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 →