Make It Cheap Enough To Leave Running

A tool you cannot afford to run all day is not really a tool. It is a demo you turn on for special occasions.

Cost scales with text, not tasks

When we started running AI systems continuously, the cost math surprised us. We assumed the bill would scale with how much we did: more tasks, more agents, more cost. It does not. The cost scales with how much text you push through the model. The number of tasks barely matters. What matters is the size of what you send it and how often you send the expensive tier a giant pile of context it does not need.

Cut what you feed the expensive step

That reframes the whole problem. If you want something you can leave running, you do not chase a cheaper model. You cut what you feed the expensive one. The move that saved us the most was simple: before handing a hard step up to the top tier, we hand it a short, faithful summary of what happened so far instead of the entire history. Same information the model needs to act, a fraction of the text. The expensive step reads less, so it costs less, every single time it runs.

The honest catch

A summary only works if it is faithful. If the short version drops something that mattered, you have traded cost for a wrong answer, which is a bad trade. So this only pays off when the summary is trustworthy, which means you have to check that it is, not assume it. We did not take the token savings on faith. We measured whether the summarized version held up against the full one, and kept it only where it did.

The general lesson: the thing making your AI expensive is usually not the model. It is how much unnecessary context you are shoving through it. Trim that, faithfully, and a tool you could only afford to run sometimes becomes one you can leave on. The premium library goes deeper into the memory system that does this for you.

Educational only. Not financial advice. Results not guaranteed. We are not financial advisors.