# LLM Cost per User: The SaaS Math Nobody Shows

Cost per user = actions x calls x tokens x price. Worked example: $1.82 at the mean, $18 at p95 against $29 ARPU. Margin bands, and how flat capacity caps tail risk.

*Published 2026-06-12 · https://proxyllm.ai/blog/llm-cost-per-user-saas*

LLM cost per user is actions per user x calls per action x tokens per call x token price, and for a typical AI SaaS feature on GPT-5 it lands near $1.82 a month for the mean user: 6.3% of a $29 plan. The mean is not the risk. The p95 user costs $18, the p99 user costs more than their subscription, and AI pricing survives or dies on that tail, not on the average.

## The formula and a worked baseline

Take an AI writing product at $29 a month. The mean user runs 40 generations a month; each generation makes 3 model calls (draft, refine, title) averaging 2,500 input and 1,200 output tokens. On GPT-5 at $1.25 input / $10 output per million tokens (June 2026, [live prices at openai.com/api/pricing](https://openai.com/api/pricing)):

```text
per call:       0.0025 x $1.25 + 0.0012 x $10 = $0.0151
per generation: 3 x $0.0151                   = $0.0454
per mean user:  40 x $0.0454                  = $1.82/month
```

That is the number most teams put in the deck: 6.3% of ARPU, healthy software margins. It is also the least informative number in the model.

## The distribution is the business

Usage in AI products is heavily skewed. The same product, sliced by user segment:

| Segment     | Generations/month | LLM cost/month | % of $29 ARPU |
| ----------- | ----------------- | -------------- | ------------- |
| Median user | 25                | $1.13          | 3.9%          |
| Mean user   | 40                | $1.82          | 6.3%          |
| p95 user    | 400               | $18.15         | 63%           |
| p99 user    | 1,200             | $54.45         | 188%          |

The mean user costs 6% of revenue; the p95 user costs 63%; the p99 user costs almost twice what they pay. With 10,000 users, the top 1% generates around $5,400 of monthly COGS against $2,900 of revenue from that same segment. Every "unlimited AI" plan quietly bets that this tail stays thin, and engaged products grow their own tails: your best users become your most expensive ones.

## Margin bands worth aiming at

Working guidance from these unit economics:

- **Under 10% of ARPU at the mean**: comfortable; AI is a feature cost.
- **10 to 25%**: a warning band; one model upgrade or engagement spike pushes you out of software margins.
- **Over 25%**: reprice, cap, or re-architect; the AI is eating the gross margin that sales efficiency math assumes.

The cheapest lever is model assignment per path. Moving draft calls to GPT-5 Mini ($0.25/$2 per million) drops the per-call cost to about $0.003 and the mean user to roughly $0.36 a month: 1.3% of ARPU, with quality holding wherever your evals say it holds. Which tier suffices per task type is the subject of [the cheapest OpenAI model that still does the job](/blog/cheapest-openai-model-for-production).

One more multiplier hides in roadmaps: agent features. The baseline assumes 3 calls per action; an agentic "do this for me" feature runs 15 to 50 calls per action, which moves the whole table by 5 to 15x. Price those features with [the agent cost formula](/blog/ai-agent-cost-calculation) before they ship, not after the first invoice.

## When flat capacity de-risks the pricing

Per-token COGS means your liability scales with engagement, which is the one thing you are trying to maximize. Flat-cost capacity puts a ceiling on it. A subscription-backed lane through [Codex Hosted](/) bills bulk workloads against a flat ChatGPT plan: Pro 5x is $100 and our fee is a flat $129, and that absorbs an estimated $3,500 of API-equivalent work (an estimate from observed usage windows, not a guarantee). At this product's mean usage, that is roughly 1,900 users' worth of generation volume for a fixed $100 plus $129.

The tail math changes character: a p99 user on the flat lane consumes window capacity, not marginal dollars, so their worst case is throttling into the API fallback lane rather than a COGS spike. Caps per sub-key still belong in the design, and bursty launch days still want the API lane as overflow. The honest survey of every fixed-cost path, including self-hosting and provider commitments, is in [fixed-cost LLM inference](/blog/fixed-cost-llm-inference).

## The model to build this week

Pull 30 days of logs and compute four numbers: mean and median cost per user, p95, p99, and LLM COGS as a percent of MRR. Then price the tail: caps, tiers, or a flat lane. If your mean user costs under 10% of ARPU and your p99 user is capped, AI margins stop being a board-meeting surprise.

Your own volumes drop into the [calculator](/calculator) in under a minute; it shows the metered bill next to the flat-capacity setup that would cover it.