Spend Control for AI Research Labs
Research labs burn through frontier-model tokens at a rate that makes CFOs wince. The spend is real; so is the need to keep experiments running.
Where research labs spend
Frontier-model inference for experiments and evals.
Agent fleets running parallel research pipelines.
Compute and data purchases beyond the model bill.
The failure modes
An eval loop re-running after a prompt change multiplies inference spend.
Parallel agents compound spend during a single experiment.
Model drift to pricier tiers changes the bill silently.
Which rules to start with
Per-experiment budget.
Velocity limit on eval retries.
Category rule: inference vs compute vs data.
Spend map
| Research spend | Control |
|---|---|
| Frontier inference | Per-experiment ceiling |
| Eval loops | Velocity limit |
| Parallel fleets | Shared daily cap |
| Data purchases | Merchant allowlist |
FAQ
Does the firewall slow experiments?
No — ~5 ms per check, only on spend actions.
Can different projects have different budgets?
Yes — per-agent rules let each project run its own ceiling.
Related
Stop the next $12,400 night.
One API call (or MCP tool) in front of every agent transaction — APPROVED, BLOCKED, or FLAGGED, deterministic, ~5 ms, fully logged.
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