Standard Compute

Cut Your AI Agent's API Bill

Agents don't spend like chatbots: they resend context every step, call in parallel, and retry when throttled. Seven tactics that actually move the bill, ordered by typical savings — each with the honest note on when it won't help.

01

Prompt caching (often the single biggest cut)

Cache the stable parts of every request: system prompt, tool definitions, large documents, conversation history. On Anthropic, cache reads bill at ~10% of the input price and don't count toward input-token rate limits at all.

Works when: Agents resend nearly identical context every step, so cache-hit rates of 70-90% are normal. That can cut input costs by more than half on its own.
Doesn't when: Short one-shot tasks with no repeated context, and models whose providers price cache reads poorly.
How cache-aware rate limits work →
02

Route by task size

Send routine steps (file triage, summaries, tool routing, subagent chatter) to a cheap model and reserve the frontier model for the steps that need judgment.

Works when: Most agent steps are mechanical. Teams that split traffic often serve 60-80% of calls from models costing 10-50x less.
Doesn't when: If routing logic misfires, cheap-model mistakes create expensive retries — start conservative, measure, then widen.
Model tiers and value scores →
03

Context hygiene: compact and reset

Compact long sessions and start fresh per task. Every appended tool result rides along on every subsequent call.

Works when: Cost per step grows with session length; regular compaction keeps a long session from costing 5-10x its early steps.
Doesn't when: Over-aggressive pruning makes the agent forget and redo work — which costs more than the tokens saved.
04

Kill retry storms

Exponential backoff with jitter, honor retry-after headers, cap attempts, and alert on failure loops. A 429 storm bills you for attempts while accomplishing nothing.

Works when: Runaway loops are behind most 'my agent burned $200 overnight' stories. A retry budget makes that class of bill impossible.
Doesn't when: It saves disaster money, not baseline money — the happy-path bill is unchanged.
Why agents hit 429s →
05

Batch APIs for non-interactive work

Move evaluation runs, backfills, and bulk processing to provider batch endpoints — typically ~50% off, and it doesn't compete with your live rate limits.

Works when: Any workload that tolerates minutes-to-hours latency.
Doesn't when: Interactive agent loops, obviously.
06

Cheap open-weight models where quality tolerates it

DeepSeek V4 Flash, MiniMax M2.1, and Kimi-class models deliver serviceable agent steps at cents per million tokens.

Works when: High-volume, low-stakes steps; subagents; anything you'd accept a junior doing.
Doesn't when: Long-horizon planning and gnarly debugging still separate frontier models from the rest — measure on your own tasks.
The cheapest-API math →
07

Flat-rate for always-on agents

Past a few hours of daily agent runtime, stop optimizing per-token spend and cap it: flat monthly pricing with unlimited tokens turns the bill from a variable into a constant.

Works when: Always-on agents, heavy daily coding use, anything where you've caught yourself rationing the agent to protect the bill.
Doesn't when: Light or bursty use — cheap per-token models win there, and we say so in the math linked below.
How flat-rate unlimited works →

Agent API costs — common questions

Why is my AI agent's API bill so high?

Agents multiply costs in three ways chat doesn't: they resend growing context on every step, they make many calls per task (planning, executing, verifying, retrying), and failures trigger retries that bill without producing anything. A single overnight loop can make hundreds of frontier-model calls.

What single change cuts agent API costs the most?

Usually prompt caching, because agents resend nearly identical context on every call — cache-hit rates above 70% are common and can more than halve input costs. Second is routing routine steps to cheaper models. For always-on agents, flat-rate pricing caps the bill entirely.

How do I stop an agent from burning money overnight?

Hard caps: a retry budget with exponential backoff, a per-session spend or step limit, and provider-side spend limits where offered. Then remove the incentive to fail: most overnight burns are 429 retry storms, which pacing-based flat-rate endpoints structurally can't produce.

Compute your own workload →The cheapest-API math →