TokenAtlasPer-seat AI cost math
If a feature uses 50K tokens/user/month on GPT-4o, that's $0.50/user. Pricing the feature at $5/seat keeps AI <10% of ARPU — the standard SaaS rule of thumb.
Gross margin protection
Hard-cap per-user token spend. Route low-value users to cheaper models. Cache aggressively on system prompts. Each of these moves margin 5-15 points.
Common failure mode
Shipping AI features without volume forecasts and watching cost outpace revenue. TokenAtlas models the curve before you launch.
Choosing a model per feature tier
Blended at 70/30, GPT-4o models to $4.75/1M tokens and GPT-4o mini to $0.285/1M — a roughly 17x gap. For a feature where quality differences are marginal to the end user (short summaries, tagging, classification), modelling the mini-tier cost against the same volume shows whether the cheaper model keeps the feature under the target share of ARPU without a quality trade-off worth paying for.
What moves the per-seat estimate most
Calls per seat per month is the input most likely to be wrong in a first estimate, since usage habits shift after launch. Model a base case from expected usage and a high-usage case at 2-3x calls per seat; if the high-usage case pushes modelled cost past the per-seat price you charge, that is the scenario worth planning for before it happens in production.
A cheaper model can be a false economy here
Downgrading a customer-facing feature to a cheaper model to protect margin only works if output quality holds. If it doesn't, and the feature needs a follow-up call or a support escalation to fix a bad response, the modelled per-call saving is offset by cost that shows up elsewhere — model the retry case, not just the single-call rate, before committing to the swap.
A second scenario at larger seat count
The same 50K-tokens/user/month feature on GPT-4o at 100,000 seats models to $50,000/month rather than the $0.50/user unit figure changing — the per-seat cost is constant, but the absolute number that finance reviews scales linearly with seats. Model both the per-seat rate and the absolute monthly total, since a board conversation cares about the latter even when the former looks fine.
Assumptions checklist for a per-seat estimate
— Tokens per user per month, and whether this is measured or assumed from a comparable feature — The model assigned to the feature and whether tiering by user segment is planned — Target AI cost as a percentage of ARPU — Whether caching or prompt-length reduction is already factored into the tokens-per-user figure
What this page does not do
This page models AI cost against a seat price using token volumes and rates you supply; it does not ingest usage logs, sync with a billing system, or track actual per-user consumption in production. Use it to set a pricing target and sanity-check a feature before launch, then re-run it with observed usage once real data exists.
Frequently asked questions
- What % of ARPU should AI cost be?
- Industry benchmark: ≤10%. Above 20% the feature is structurally unprofitable.
- Should I pass cost through to users?
- Either a usage cap or a usage-based pricing tier. Hidden cost growth is the killer.
- How do I monitor in production?
- Track token volumes in your own logging, then re-run the scenario with the observed prompt, completion and call-count figures. TokenAtlas models the cost from numbers you supply; it does not read provider usage.