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MSP Economics · AICost.ai cost decision engine

🩸 Do you have unbilled licenses and services?

Margin leak = revenue you earned but never invoiced, plus costs you paid but never needed. Twelve questions, five leak areas: license billing sync, unreviewed distributor lines, dormant premium seats, cloud cost leakage, and unmetered AI services. You get a score, a ranked list, a workflow, and a recoverable range.

Grounded typical ranges, sourced* Decision-first — a verdict and a next step MCP engine: aicost.msp-margin-leak
Margin-leak score (/100)
55
What this calculator does

Margin leak = revenue you earned but never invoiced, plus costs you paid but never needed. Twelve questions, five leak areas: license billing sync, unreviewed distributor lines, dormant premium seats, cloud cost leakage, and unmetered AI services. You get a score, a ranked list, a workflow, and a recoverable range.

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 →
⚡ MSP Margin Leak Check Playground
The 4 factors that move this the most — drag and watch the decision change.
Margin-leak score (/100)
55 LEAKING
→ FIX THE TOP TWO AREAS THIS QUARTER
40 %
12 %
30 %
1
Try a scenario: 📄 Print one-pager ✉ Email me this

MSP Margin Leak Check — 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) 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.
Total billable users across all clients.
Blended per-user price across the base.
0 = automated reconciliation tool, 1 = manual monthly, 2 = ad-hoc or never.
Line-level review coverage of distributor invoices.
Licensed but not actively used (Copilot, addons).
Azure/AWS/GCP you resell or manage.
Managed-services clients.
Change volume drives sync drift.
Copilot $ 30, security addons, etc.
Total premium/addon seats billed.
Idle, right-sizing, unbilled pass-through checks.
Managed AI / Copilot services you bill.
1 = usage metering + budgets in place.
Verdict
LEAKING

Estimated leak ≈ 5.0% of managed revenue across 5 areas (score 55/100).

Margin-leak score (/100)
55
Estimated leak / mo
$2,282
Leak as % of managed revenue
5%
Recoverable / yr (conservative)
$13,695
Recoverable / yr (aggressive)
$38,346
Top leak area
Cloud cost leakage (unreviewed spend)
What to do
FIX THE TOP TWO AREAS THIS QUARTER

Method memo

Score 55/100. Estimated leak $2,282/mo (5.0% of $45,700 managed revenue/mo). Top area: Cloud cost leakage (unreviewed spend) ≈ $756/mo. Recoverable range $13,695–$38,346/yr*.

Questions to pressure-test with

  1. Which distributor lines were reviewed last month, and by whom?
  2. When a client adds seats mid-month, what updates the PSA agreement, and when?
  3. Which flat-fee AI services have usage you cannot see?

Assumptions

  • Leak coefficients are analyst estimates* from 2026 MSP reconciliation patterns; distributor/PSA sync commonly consumes 10–40 hrs/mo of admin labor and frequent product/rate changes are a recognized source of unbilled services.
  • The free Historical Margin Audit replaces these estimates with deterministic findings from your actual data, each with evidence.
  • Range shown is conservative-to-aggressive (0.5×–1.4× of point estimate).

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 2026-09-13

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-09-13.
  • 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
2026-09-13
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 677 days captured
Anthropic Docs
2026-09-13
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 677 days captured
OpenAI
2026-09-13
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 678 days captured
Google AI
2026-09-13
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 653 days captured
Google Vertex
2026-09-13
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 653 days captured
DeepSeek
2026-09-13
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 592 days captured
xAI
2026-09-13
https://x.ai/api
Daily snapshot since Nov 2024 · 510 days captured
Mistral
2026-09-13
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 651 days captured
Cohere
2026-09-13
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 677 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 →