Paying frontier prices for routine work
Expensive models doing work a cheaper sibling completes to the same standard — including the migration you finished everywhere except one app.
- A frontier model doing work a cheaper model in the same family completes to the same standard.
- Short prompts running on a large-context model, where a smaller model in the same family serves the same requests.
- The same workload spread across several model families, where one of them serves it all at lower cost.
- A migration you already completed elsewhere, with one application left behind on the old model.executable steps
Found in the public demo — real engine output
Finish the gpt-4-turbo → claude-sonnet migration: Meeting Summaries missed the rollout
€40,940/yr · 2d payback · 72% confidence — from a fictional firm's pinned dataset. See the full workspace.
Paying for the same answer twice
Repeated prompts and prefixes billed at full price because nothing in front of the model remembers the last answer.
- Repeated near-identical prompts that a semantic cache in front of the model would answer without a call.
- Repeated prompt prefixes paying full input price because prompt caching is not switched on.executable steps
- Prompts whose length is dominated by a static preamble that could be shortened or cached.
- Cache writes that are never read back — paying the write premium for nothing.
Paying rush rates for patient work
Latency-tolerant jobs billed at synchronous rates, and calls billed at context tiers their prompts never reach.
- Latency-tolerant work billed at synchronous rates when a batch endpoint would serve it cheaper.
- Calls billed at a long-context tier whose prompts never approach that tier's threshold.
Paying above the market you already use
The same model, cheaper from a provider or region already in your stack.
- The same model available from a provider you already use at a lower per-token price.executable steps
- Traffic served from a region that costs more than an equivalent one you already use.
Paying for nothing at all
Spend on failed calls, and output far longer than the task ever needed.
- Spend on calls that failed — money paid for output nobody received.
- Output token counts far above what the task needs, usually a missing length instruction.
Measured on a real customer
Everything above is the engine running on a fictional firm’s pinned dataset. For what it did to a real production workload — cost per call 70% lower, and the prediction we got wrong — read the Finautor case study.
3 of these screens ship with steps reviewed to the standard an agent can execute — the rest are advisory, each saying why. The full registry, with every precondition, is on the rules page.
See what Bayeto finds in your stack
One usage export, parsed in your browser — or explore the demo first, no account needed.