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Checklist · AI & Machine LearningFree

LLM Cost Optimization Checklist

A checklist for cutting what you spend on LLM APIs without cutting quality. It covers measuring cost per request and per feature, routing work to smaller models, prompt caching, batch APIs, trimming context, limiting output, caching whole responses, retries and rate limits, and budgets with alerts, each with a severity and a concrete way to verify it.

Topics
Chapters

8

Sefism · Oct 2026

Checks

31

Sefism · Oct 2026

Sources cited

8

Sefism · Oct 2026

Topic

AI

Sefism · Oct 2026

Format

Checklist

Topic

AI & Machine Learning

Provider

Sefism

Time needed

16 min read

Level

Intermediate

Access

Free

What this checklist is

A checklist for cutting what you spend on LLM APIs without cutting quality. It covers measuring cost per request and per feature, routing work to smaller models, prompt caching, batch APIs, trimming context, limiting output, caching whole responses, retries and rate limits, and budgets with alerts, each with a severity and a concrete way to verify it.

It has 8 chapters and 31 checks, each with how to verify it and how much it matters. For developers paying for LLM APIs in production.

Chapter by chapter

1. Measuring cost (4 checks)

You cannot cut what you cannot see. These checks give you cost per request, per feature and per user, from the usage data every API already returns.

2. Model choice and routing (5 checks)

The biggest single lever on cost per token is which model you call. These checks make sure each task uses the smallest model that passes your evals.

3. Prompt caching (4 checks)

Prompt caching bills a repeated prompt prefix at a reduced rate. It only works if the prefix is really identical, so most of these checks are about keeping it stable.

4. Batch APIs (3 checks)

Work that does not need an answer in seconds can usually run through a batch API at a lower price. These checks find that work and run it safely.

5. Trimming context (4 checks)

Input tokens are usually the bulk of the bill. These checks remove what the model does not need to see on each call.

6. Output limits (3 checks)

Output tokens are often priced higher than input tokens on your provider's price list, and long answers are often worse answers. These checks keep output as short as the task allows.

7. Caching responses and avoiding calls (3 checks)

The cheapest call is the one you do not make. These checks reuse answers safely and keep deterministic work out of the model.

8. Retries, rate limits and budgets (5 checks)

Runaway loops and abuse cause the worst cost surprises. These checks cap spend per request, per user and per account, and tell you before the invoice does.

How it works on Sefism

The checklist is one page: each check has a severity (must, should or nice to have) and a concrete way to verify it, and members can tick checks off as they go. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.

Every claim is checked against the sources listed on the page (8 cited).

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