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MSP Economics · AICost.ai cost decision engine

🧰 What is your stack really costing per endpoint — and what is pure drag?

The typical MSP runs 15–20 vendors. This prices your stack per endpoint against 2026 bands, dollarizes shelfware, duplicate functions, and admin drag — and calls the consolidation decision with the concentration-risk caveat built in.

Grounded typical ranges, sourced* Decision-first — a verdict and a next step MCP engine: aicost.msp-tool-stack-optimization
Stack cost / endpoint
$12
What this calculator does

The typical MSP runs 15–20 vendors. This prices your stack per endpoint against 2026 bands, dollarizes shelfware, duplicate functions, and admin drag — and calls the consolidation decision with the concentration-risk caveat built in.

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 →
⚡ Tool-Stack Cost & Consolidation Playground
The 3 factors that move this the most — drag and watch the decision change.
Stack cost / endpoint
$12 IN BAND
→ TRIM SHELFWARE & DUPLICATES
21500
14
12 %
Try a scenario: 📄 Print one-pager ✉ Email me this

Tool-Stack Cost & Consolidation — 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; Tool stack (2026): typical MSP runs 15-20 vendors, 36% run 10+ tools*; full-stack $8-25/endpoint/mo*; 61% of orgs find unused/unauthorized SaaS monthly*. * = typical, replace with your invoices.) 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.
Across all clients.
RMM, PSA, EDR, backup, email sec, docs, network…
All vendor invoices.
Licenses paid but unused — 61% of orgs find unauthorized/unused SaaS monthly*.
Two backup products, two doc systems…
For the overlap estimate.
Updates, consoles, billing, vendor calls.
When the connectors fall over.
For the labor drag.
Verdict
IN BAND

$11.94/endpoint across 14 tools (band: $8–$25*), with $8298/mo of drag: shelfware $2580 + duplicates $2250 + admin/integration labor $3468.

Stack cost / endpoint
$12
2026 band
$8–$25/endpoint/mo typical 2026*
Total drag / month
$8,298
Total drag / year
$99,576
Shelfware / month
$2,580
Duplicate spend / month
$2,250
Admin drag / month
$3,468
What to do
TRIM SHELFWARE & DUPLICATES
Why
Per-endpoint cost is inside the band, but $4830/mo of shelfware and overlap is pure drag — renewal calendar first, migrations later.

Method memo

Per-endpoint stack cost vs typical 2026 band; drag = unused licenses + half of each duplicate-function tool + tool-admin and integration-breakage labor. Per-VENDOR price checks (are you overpaying for Huntress/NinjaOne at your volume?) run in the msp-vendor-price-benchmark engine — this calculator is the stack-level view.

Questions to pressure-test with

  1. Which two tools overlap the most — and which one does the team actually open?
  2. What does the renewal calendar look like? Consolidation timed to renewals avoids penalty stacking.
  3. If you consolidated to one platform, what single-vendor outage would take down every client at once?

Assumptions

  • Band and duplicate-redundancy share are typical* 2026 values — replace with quotes at renewal.
  • The platform (SeatSource) computes deployed-vs-billed per vendor from real exports.

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 →