Guides / llm cost spike after launch
Why your LLM cost spikes after a launch
A launch spike is rarely proportional to traffic. New users explore more per session than retained ones, they arrive on your cheapest tier, and any unbounded loop meets its worst input at once. Culpa, a local-first LLM cost, margin, and forecast ledger, forecasts a launch from your own usage curve and prices it before you ship.
Why this happens
Three effects stack on launch day. A curious first-time user sends more messages per session than a habitual one, so cost per user runs above your steady-state figure. Launch traffic skews heavily toward free and trial accounts, so the expensive cohort is also the one paying least. And volume finds the edge cases your loops never hit at low traffic, which is when an unbounded retry or agent run becomes a real number.
What this usually looks like
- Spend rises faster than signups, so cost per new user exceeds your modelled figure.
- The spike doesn't decay for several days, because exploration continues past day one.
- Your worst single conversation of the month happens during launch week.
- Free-tier accounts account for most of the increase while contributing no revenue.
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It shows your most expensive conversation before you install anything.
Mistakes that cost the most
| Mistake | Why it hurts | Do instead |
|---|---|---|
| Forecasting a launch by multiplying steady-state cost per user by expected signups. | New users cost more than retained ones, so the forecast is low exactly when being low matters. | Use first-week cost per user from a previous cohort, not the blended figure across all users. |
| Shipping without a per-account spend cap. | Launch traffic is where a single account can generate an unbounded bill before anyone notices. | Set a per-account daily ceiling before launch, and alert on approach rather than on breach. |
| Publishing a single forecast number for launch month. | Launch variance is the widest of any month, so a point estimate is read as a commitment and then missed. | Publish a range and widen it deliberately for launch week, stating the assumption behind each bound. |
Run this check tonight
- Pull cost per user for their first seven days from a past cohort, and compare it to your blended figure.
- Check whether a per-account spend ceiling exists in code today. If not, your launch worst case is unbounded.
- Identify which model your onboarding flow uses. It's often the most expensive one, on the least valuable traffic.
- Price your single most expensive conversation ever, then multiply by a plausible number of curious new users.
- Confirm your alerting fires on spend rate, not only on daily total. A daily total tells you the morning after.
Launch week priced from a first-week cohort, not the blended average
Illustrative example
2,000 signups in launch week. Steady-state users cost $0.22 a month on GPT-5.4 at real rates, $0.0025 per 1k input and $0.015 per 1k output. First-week users are modelled at 3.2 times the steady-state token usage.
The naive forecast under-reads by nearly four times, entirely because it priced new users as if they behaved like retained ones. The fix is a cohort number, not a bigger multiplier.
Every number, with its confidence and source
| Figure | What it means | Confidence | Source |
|---|---|---|---|
| $440 to $1,730 | modelled launch-month spend, naive forecast versus cohort-based forecast | estimated | Both endpoints from the teardown arithmetic at real GPT-5.4 rates from the price book, effective 2026-07-02. A range because the first-week usage multiple is modelled, not measured. |
| $0.22 | modelled steady-state model cost per active user per month | calculated | (40 x $0.0025) + (8 x $0.015) using GPT-5.4 rates per 1k tokens from the price book. Per-user token volumes are modelled. |
What a generic answer can’t know
A provider has no idea you launched. It can't separate a first-week user from a habitual one, because it has never seen your signup dates or your plan tiers. That join lives in your own data. Culpa reads it on your infrastructure, keeps your prompts and responses there, and counts the calls to run your plan.
Questions founders ask next
How much more do new users cost than retained ones?
It varies by product and you should measure your own rather than borrow a figure. The direction is consistent though: first-week usage runs above steady state because exploration is front-loaded, so a blended average always under-forecasts a launch.
Should I cap usage during a launch?
Cap per account rather than globally. A global cap degrades the launch for everyone when one account misbehaves, and the whole point of a launch is that the experience holds up.
How long does a launch spike last?
Longer than most teams budget for, because exploration continues past day one and the cohort decays gradually rather than dropping. Model it as a decay over the first weeks, not a single-day peak.
On your infrastructure
Culpa runs on your infrastructure. Your prompts and responses never leave it. Culpa counts calls to run your plan, and it fails open, so if it ever breaks your app keeps running.
Free, no card, no account
Run the free Cost Leak Scan
It shows your most expensive conversation before you install anything.
Keep reading
Sources: OpenAI API pricing. Last reviewed 2026-08-01. Plain text version.