LLM Cost Per Month: How to Estimate Your Monthly AI Bill
Estimate your LLM cost per month from request volumes, average tokens per call, and model prices. A simple calculator approach plus budget tips for realistic monthly AI spend.
Your monthly LLM bill equals requests per day × average tokens per request × price per token × days. You do not need a spreadsheet for this: the estimate is three numbers multiplied together.
The mistake most teams make is estimating from the cost of a single cherry-picked example instead of their real average workload. Here is a better way.
The estimate formula
- Count requests per day across all apps and users.
- Measure average tokens per request (input + output).
- Pick a price per million tokens for your main model.
- Multiply: monthly tokens = requests × avg tokens × 730 hours of active usage.
- Add 20-30% headroom for retries, tool loops, and context bloat.
Worked example
A small app runs 5,000 requests/day at 3,000 tokens each (~2,000 input, 1,000 output) on a mid-tier model at roughly $1.25 per 1M input and $5 per 1M output. That is close to 10B output tokens and 20B input tokens a month → about $12.50 + $50 = $62.50, plus headroom ≈ $80/month.
What changes the number most
- Model choice: routing easy requests to a cheap model can cut the bill 5-10x.
- Output length: capping max tokens on completions caps the most expensive part.
- Context size: truncating history and caching prefixes reduces repeated input cost.
- Batch jobs: moving background work to async batch endpoints halves the rate.
FAQ
How do I estimate LLM cost per month?+
Multiply requests per day × average tokens per request × price per million tokens × days in the month, then add 20-30% headroom.
What is the biggest cost driver?+
Output tokens and model choice. Output tokens cost 2-5x input, and frontier models cost 10-50x small models.
How do I avoid a surprise AI bill?+
Set hard budget limits with alerts and blocking in a gateway, and keep per-project spend analytics on.
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