MSP AI Policy · AICost.ai cost decision engine

🛣 Which gateway or router, for this client?

Requirements first, price second. Products that cannot meet a hard requirement are eliminated and the reason is shown, rather than being ranked lower and quietly recommended. An empty shortlist is a real answer: it means the stated requirements cannot all be met, and it names the one doing the eliminating.

Grounded typical ranges, sourced* Decision-first — a verdict and a next step MCP engine: aicost.router-compare
Best net saving / mo
$860
What this calculator does

Requirements first, price second. Products that cannot meet a hard requirement are eliminated and the reason is shown, rather than being ranked lower and quietly recommended. An empty shortlist is a real answer: it means the stated requirements cannot all be met, and it names the one doing the eliminating.

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 →
⚡ Router and Gateway Comparison Playground
The 4 factors that move this the most — drag and watch the decision change.
Best net saving / mo
$860 TWO-HORSE RACE
→ Compare LiteLLM and Vercel AI Gateway on capabilities, not price.
6000
40 %
60 %
145
Try a scenario: 📄 Print one-pager ✉ Email me this

Router and Gateway Comparison — your exact numbers

Enter this client'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.
Used to price percentage-of-spend fees.
Anything ticked eliminates products that lack it. Tick only what is genuinely non-negotiable.
Products the client has already rejected.
Drives the gross saving each product is credited with.
How much cheaper the cheaper model is.
Self-hosted products trade licence fee for this.
Fully loaded.
Verdict
TWO-HORSE RACE

LiteLLM and Vercel AI Gateway land within 15 percent of each other on net monthly saving. Pick on what each gives up, not on the number.

Best net saving / mo
$860
Leader
LiteLLM
Products surviving
7
Products considered
7
Leader fee / mo
$0
Runner-up
Vercel AI Gateway
Runner-up net / mo
$860
What to do
Compare LiteLLM and Vercel AI Gateway on capabilities, not price.

Method memo

At $6,000 a month and no hard requirements, 7 of 7 captured products survive. LiteLLM leads on net monthly saving at $860, on a fee basis of no licence fee, self-hosted. Router pricing was captured 2026-07-26 and moves faster than model pricing, so re-check before any of this is quoted.

Questions to pressure-test with

  1. Which of these requirements is genuinely hard, and which is a preference someone wrote down?
  2. If the winning product raised its fee 2 points, would it still win?
  3. Who operates this layer day to day, and is that time billable to the client?
  4. Does the client already hold a cloud agreement that one of these can be bought on?
  5. What does the shortlisted product give up, and who provides that instead?

Assumptions

  • 40 percent of traffic eligible for a cheaper model *
  • 60 percent saving on that eligible traffic *
  • 4 engineering hours a month to operate the layer *
  • Engineering time valued at $145 an hour *
  • Product pricing and capabilities captured 2026-07-26 from primary sources.

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

📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-28.
  • 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
2026-07-28
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Anthropic Docs
2026-07-28
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 631 days captured
OpenAI
2026-07-28
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 632 days captured
Google AI
2026-07-28
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 607 days captured
Google Vertex
2026-07-28
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 607 days captured
DeepSeek
2026-07-28
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 546 days captured
xAI
2026-07-28
https://x.ai/api
Daily snapshot since Nov 2024 · 464 days captured
Mistral
2026-07-28
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 605 days captured
Cohere
2026-07-28
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 631 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.
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 →