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

⏱️ What does an outage hour actually cost?

Lost contribution plus idle payroll plus recovery labor, by scenario — with an untested-restore multiplier, because restores that were never tested take longer.

Grounded typical ranges, sourced* Decision-first — a verdict and a next step MCP engine: aicost.msp-downtime-bcdr
Cost per outage hour
$2,884
What this calculator does

Lost contribution plus idle payroll plus recovery labor, by scenario — with an untested-restore multiplier, because restores that were never tested take longer.

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 →
⚡ Downtime & BCDR Impact Playground
The 3 factors that move this the most — drag and watch the decision change.
Cost per outage hour
$2,884 EXPOSED
→ FIX BCDR NOW
72
1.35
8000000
Try a scenario: 📄 Print one-pager ✉ Email me this

Downtime & BCDR Impact — 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; Downtime impact (2026): productivity loss 60-80% during IT outage; recovery labor $150-225/hr emergency rates; untested-restore factor 1.15-1.35. Scenario arithmetic, not breach probability. * = 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 business affected by the outage.
Headcount idled by an IT outage.
Current, honest number, in hours.
1.0 tested <90d · 1.15 within a year · 1.35 never/unknown.
Share of revenue lost when work stops.
Fully loaded.
People still do something offline.
2,500 for extended hours; 8,760 for true 24/7 operations.
Current, honest number, in hours.
Emergency response rate.
Verdict
EXPOSED

A 72h ransomware recovery at $2884/hr is a $301903 event*. Modern BCDR targets 4–8h — the delta typically funds the service many times over.

Cost per outage hour
$2,884
Server-loss event
$100,634
Ransomware event
$301,903
What to do
FIX BCDR NOW
Why
Slow recovery AND untested restores compound: the multiplier applies to your longest window. Modernize BCDR and put a tested 4–8h ransomware RTO in the contract.
Insurance note
Cyber insurers increasingly require documented RTO/RPO and restore-test evidence* — this model plus test logs is that evidence pack.

Method memo

$/outage-hour = revenue x margin / operating hours + affected employees x payroll x productivity loss. Events add recovery labor (2 techs at 60% of the window) and apply the restore-test factor (1.35x) — untested restores take longer; everyone knows it, few price it.

Questions to pressure-test with

  1. When was the last successful full restore test, and was it timed?
  2. Which systems drive the revenue line? Tier them — not everything needs the premium RTO.
  3. Does cyber insurance require specific RTO/RPO evidence? The certificate trail helps there.

Assumptions

  • Restore-test factor: 1.0 tested <90d, 1.15 within a year, 1.35 never/unknown (estimated*).
  • Not a breach-probability model; scenario arithmetic on your inputs only.

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 2026-09-20

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-09-20.
  • 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
2026-09-20
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 684 days captured
Anthropic Docs
2026-09-20
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 684 days captured
OpenAI
2026-09-20
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 685 days captured
Google AI
2026-09-20
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 660 days captured
Google Vertex
2026-09-20
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 660 days captured
DeepSeek
2026-09-20
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 599 days captured
xAI
2026-09-20
https://x.ai/api
Daily snapshot since Nov 2024 · 517 days captured
Mistral
2026-09-20
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 658 days captured
Cohere
2026-09-20
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 684 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 →