AI Prompt Cost Per User 2027 Calculator
Calculate the AI prompt cost per user (PPU) for your product — tokens per request times requests per user times monthly active users times the per-token model price. Compare GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Flash before locking your 2027 LLM budget.
What Is AI Prompt Cost Per User?
AI prompt cost per user is the per-user, per-month spend on LLM inference required to deliver your product feature. It is the most defensible unit-economics input for AI products — it tells you whether each user is gross-margin positive at your current price point, and how much pricing or routing headroom you have before token prices change. Frontier model pricing dropped 80 percent between 2023 and 2025, but volume per user grew faster, so PPU is climbing for most teams.
PPU Formula
PPU = (Input Tokens + 4 × Output Tokens) ÷ 1,000,000 × Model Price per Million × Requests per MAU per Month
Note: Output tokens are ~4x more expensive than input on most frontier models.
Why Output Tokens Dominate Cost
On GPT-4o, output tokens cost USD 10 per million versus USD 2.50 for input — a 4x premium. On Claude 3.5 Sonnet, output is USD 15 versus USD 3 (5x). Gemini 1.5 Flash is the cheapest frontier model at USD 0.30 input and USD 1.20 output per million tokens. Any output reduction (shorter system prompts, structured output, tool calls instead of prose) drops PPU faster than caching input. Cache the long stuff, trim the short stuff.
Frontier Pricing as of 2026
Based on official pricing (OpenAI, Anthropic, Google AI Studio, April 2026): GPT-4o input USD 2.50, output USD 10. GPT-4o-mini input USD 0.15, output USD 0.60. Claude 3.5 Sonnet input USD 3, output USD 15. Claude 3.5 Haiku input USD 0.80, output USD 4. Gemini 1.5 Pro input USD 1.25, output USD 5. Gemini 1.5 Flash input USD 0.075, output USD 0.30. Most B2B SaaS teams should default to a smaller model and only escalate on signal — 70 percent of prompts do not need a frontier model.
How to Use This Calculator
Enter your typical input tokens per prompt, output tokens per response, average requests per active user per month, and the model price for your chosen provider. The calculator will multiply through to give you monthly PPU, then scale by MAU for total monthly inference spend. Add a 30-40 percent buffer for retries, prompt caching misses, and embedding generation if your product uses RAG. Compare versus your average revenue per user (ARPU) — if PPU is more than 25 percent of ARPU, your AI margin is structurally thin.
Sources: OpenAI Platform Pricing (2026), Anthropic Pricing (2026), Google AI Studio Pricing (2026), a16z LLM Cost Report 2026. Last updated: April 2026.