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AI agent burning through API credits — why it happens and the permanent fix

My AI agent is burning through API credits
Quick answer

Agents spend 10–100x more than chat use because every loop step re-sends the growing conversation: tool results re-enter context, so the cost per step climbs as the session runs. The tactical fixes are context hygiene and routing routine steps to cheaper models; the structural fix is a flat monthly plan, so a heavy day costs the same as a quiet one.

What causes it

How to fix it

  1. Compact aggressively: summarize or reset context per task instead of one endless session.
  2. Split model usage — route routine steps to a budget model and save the frontier model for hard reasoning. Some routers do this automatically.
  3. Cap retries and parallel tool calls; failed loops that auto-retry are pure credit burn.
  4. Set hard budget alerts at your provider so a runaway loop can't surprise you at invoice time.
  5. Move the workload to a flat-rate plan: if your agent runs daily, a fixed monthly price converts an unpredictable bill into a constant.
Running an agent?

If you're not sure where the credits go, log tokens per step for one session: the last steps of a long session typically cost several times the first steps purely from accumulated context.

The permanent fix

Stop hitting this entirely

This is the exact problem Standard Compute is built around: a flat monthly price (from $39/mo) instead of a token meter, with smart routing that automatically sends trivial agent steps to budget models and hard ones to frontier models — through one OpenAI-compatible base URL. The bill is the same on your agent's busiest day as on its quietest.

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FAQ

How much does it cost to run a coding agent heavily?

Per-token, heavy daily agent use commonly lands between $50 and several hundred dollars per month depending on model and context size — and it varies week to week. Flat-rate plans put a fixed number on it instead.

Do cheaper models actually work for agents?

For routine steps, yes — file operations, mechanical edits and summaries don't need frontier reasoning. The quality-sensitive steps are a minority; routing only those to a frontier model keeps quality while cutting most of the burn.

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