How to control what your AI agents spend
Short answer
Attribute every token to a feature, a customer, and an agent run, then route, cache, and cap. Most runaway bills come from a few patterns: retries and loops, oversized context, an expensive model doing trivial steps, and no budget per run.
Where the money goes
- Agent loops and retries that repeat the same expensive call.
- Full documents or long histories sent as context on every step.
- One large model used for every step, including classification and formatting.
- No caching of repeated instructions or reference material.
- Background jobs with no budget and no owner.
Controls that work
- Cost attribution: tag every call with feature, customer, and run ID, and report spend per tag.
- Budgets: hard per-run and per-customer limits that stop the agent and alert a person.
- Model routing: small, cheap models for simple steps; large models only where reasoning quality pays off.
- Caching: use your provider's prompt caching for repeated instructions, and cache results you would otherwise recompute.
- Context hygiene: retrieve only what the step needs instead of sending everything.
- Batch processing for work that does not need an answer in seconds.
- Loop detection: cap steps per run and flag repeated identical tool calls.
How DailyMind helps
We add cost attribution and budget guards to your agents, build a spend dashboard by feature and customer, and send a monthly report with the next three savings to make. Cost control sits with reliability, because loops and retries usually show up first as a bill.
Frequently asked questions
Should we self-host models to save money?
Sometimes. Compare the total cost of GPUs plus the people to run them against your API spend at real volume. For spiky or low volume, APIs usually cost less; for steady high volume, self-hosting can pay off.
Will cheaper models hurt quality?
Only if you route blindly. Route by step type and check every routing rule against an evaluation set before it goes live.
What is the first thing to do?
Attribution. Until you can see spend per feature, customer, and run, every other control is a guess.
Talk to us
Running agents or AI-built software in production? Tell us your setup and what worries you, and we will suggest where a readiness review should start.
Book a readiness review