# Input token creep, the increase no single release explains

> Input token creep is a small monthly rise across your whole workload. No single change looks big, and twelve of them compound to 60%. Math inside.

URL: https://getculpa.com/input-token-creep
Last reviewed: 2026-08-01

## Answer
Input token creep is a small monthly rise in average input tokens per call across your whole workload, with no single change large enough to notice. Four percent a month compounds to about 60% in a year. Culpa, a local-first LLM cost, margin, and forecast ledger, tracks tokens per call as a series, so drift reads as a trend rather than as a surprise.

## Why this happens
This differs from creep in a single prompt, which one release causes and one diff explains. Portfolio creep is the sum of many features each growing a little for good reasons: a new field here, a longer history window there, one more retrieved chunk somewhere else. No individual change survives scrutiny as a problem, and no monthly review flags four percent. The compounding does the damage, and compounding is invisible to anyone comparing this month against last month.

## What this usually looks like
- Model spend grew steadily over a year with flat traffic and no incident to point at.
- Every individual feature owner can justify their own increase, and all of them are right.
- Budget forecasts built from a recent month keep coming in low.
- Nobody holds a tokens-per-call series longer than the retention window of the observability tool.

## Common mistakes
- Reviewing cost month against month. Why it hurts: A 4% move sits inside normal noise, so twelve consecutive real increases each read as nothing. Do instead: Review against the same month a year earlier, and plot the whole series rather than the last two points.
- Holding token metrics only as long as your observability retention. Why it hurts: Creep is measured in quarters, so a 30-day window structurally can't show it. Do instead: Keep an aggregated tokens-per-call series indefinitely. It's small, and it's the only record that shows drift.
- Forecasting next year from last month's unit cost. Why it hurts: The unit cost itself is drifting, so a volume-only forecast under-reads by the compounding rate. Do instead: Forecast tokens per call and volume separately, and put a drift rate on the first.

## Self-check
- Find your average input tokens per call this month and the same figure twelve months ago.
- Compute the implied monthly growth rate. Anything above 2% compounds to a real number.
- Check how long your token metrics are retained. If it's 30 days, you can't answer this question yet.
- List the three features with the largest input growth and confirm each change was deliberate.
- Rebuild your annual forecast with a drift rate on tokens per call, then compare it against the flat one.

## Four percent a month, twelve times over (illustrative)
A workload on Claude Haiku 4.5 at a real rate of $0.001 per 1k input, holding 2 million calls a month steady throughout. Average input starts at 5,000 tokens per call. The 4% monthly drift is modelled.
  Month 0: 2,000,000 calls x 5.0k tokens = 10,000,000k, so 10,000,000 x $0.001 = $10,000
  Month 6: 5,000 x 1.04^6 = 6,327 tokens, so 12,654,000k x $0.001 = $12,654
  Month 12: 5,000 x 1.04^12 = 8,005 tokens, so 16,010,000k x $0.001 = $16,010
  Input spend rose 60% with call volume completely flat
  The largest single month-on-month increase in that year was 4%
No review would have escalated a 4% month and no month was worse than that. The year still cost 60% more, entirely from changes each of which was reasonable on its own.

## Cost figures
Every figure carries its confidence and its source. No figure on this site is provider-reported.
- $10,000 to $16,010 — modelled monthly input spend at the start and end of a year of 4% drift, at flat call volume [estimated] Source: Both endpoints at the real Claude Haiku 4.5 input rate of $0.001 per 1k from the price book, effective 2026-07-02. A range because the drift rate and volumes are modelled.
- 60% — annual input cost increase produced by 4% monthly drift with no traffic growth [calculated] Source: 1.04 to the power of 12 equals 1.601, from the teardown arithmetic.

## FAQ
Q: How is input token creep different from context creep?
A: Context creep is one prompt growing across releases, which a diff can explain. Input token creep is the whole workload drifting upward from many small changes at once, so no single diff explains it and only a long series shows it.

Q: What drift rate should worry me?
A: Anything sustained above about 2% a month, because it compounds to more than a quarter of your input spend within a year. The rate matters more than any single month, which is exactly why month-on-month review misses it.

Q: How far back do I need to keep token metrics?
A: At least two years, in aggregate form. The series is tiny compared with the logs around it, and it's the only record that can distinguish drift from a step change when somebody finally asks where the money went.

## Sources
- Anthropic pricing: https://platform.claude.com/docs/en/docs/about-claude/pricing

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