How do I cut AI API costs without hurting quality?

What it is

A short playbook for teams whose AI bill is growing faster than its value. The steps are ordered roughly by how much they save for how little effort.

How it works

1. Right-size the tier. Use the lightest model that passes your test (see model tiers). Christopher Penn notes that document-heavy work carries a heavy context cost, so it benefits most.
2. Gate the expensive path. Harrison Chase describes running a lightweight classifier first to decide whether the costly agent needs to run at all. (Chase sells agent tooling.)
3. Cache. Reuse long, stable instructions and reference files so they’re billed at the cached rate (see prompt caching).
4. Batch. Send non-urgent jobs, such as overnight reports and bulk tagging, through a batch API (see batch processing).
5. Trim context. Remove irrelevant material. It costs money and can lower quality.
6. Skip the model. Penn also points out that some jobs need ordinary software (search, indexing, scripts) rather than an LLM.

Published discounts (checked 2026-10-17). Anthropic, OpenAI and Google each list batch processing at half the normal price, with results within 24 hours. OpenAI says cached input is discounted by up to 95%, but on newer models writing to the cache costs 1.25× the normal input rate, so caching only pays off when a prefix is reused. DeepSeek charges half price off-peak.

Benefits

• Large savings with no quality loss on routine steps.
• Predictable budgets. Each step’s cost becomes visible.

Limits and risks

• Measure before and after. Neither practitioner published savings figures, so track your own cost per output.
• Caching rules vary by vendor and model, including minimum prompt lengths and write charges.
• Batch isn’t instant. A 24-hour window doesn’t suit anything a customer is waiting on.