Token Counting and Cost Control
Count tokens, predict what a feature will cost and cut spending without hurting answer quality.
A taste of a lesson
My chatbot's cost per message keeps rising during long conversations. Why?
Because the API is stateless, your app resends the whole conversation with each new message. Turn 1 sends the system prompt plus one message; turn 20 sends the system prompt plus nineteen earlier exchanges plus the new one. Input tokens grow every turn, so cost per message grows too. Fixes: keep only the most recent turns within a token budget, summarise older turns, and cap reply length. Check your logs: how many input tokens did turn 1 and turn 20 use?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain what tokens are and why counts vary by language and content
- Count tokens before a call and read real usage after it
- Estimate cost per request, per user and per month
- Apply the main cost levers in order of impact while protecting quality
- Set up usage tracking, alerts and spending caps
Lesson plan
- 1 What a token is Understand tokens and why the same text has different counts in different settings. Start
- 2 Counting before and after Measure tokens with a tokenizer before calling and from usage after. Start
- 3 From tokens to money Calculate cost per request, per user and per month with example prices. Start
- 4 Where the tokens go Break a real request down to find the biggest token consumers. Start
- 5 Cutting cost without cutting quality Apply cost levers and confirm quality holds with a small test set. Start
- 6 Budgets, alerts and caps Put guardrails in place so costs stay inside your plan. Start
Try asking
About this tutor
For beginners building with model APIs who want to know what their app will cost before the bill arrives. You learn what tokens are, why counts differ across languages and content types, how to count them before a call and read actual usage after it, and how to turn counts into cost per request, per user and per month. Then you work through the main levers in order of impact: shorter prompts, trimmed history, output limits, smaller models for easy tasks, caching and batch processing. Every lesson uses made up example prices so the method stays valid when real prices change.
Reviews
4.3
3 ratingsSample
- Anika R.Sample
The breakdown of where tokens go showed our examples block was bigger than everything else combined. Trimmed it and quality held on our test set.
- Tobias E.Sample
Clear explanation of why our German and Polish users cost more. Practical and not preachy.
- Gustavo L.Sample
Built a cost table for my feature in the second lesson. Using made up prices and then plugging in real ones made the method stick.
About the teacher
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observability
9 tutors 347 lessons taught Sample
Most of my working life has been on platform and reliability teams, and these days I spend it on LLM features: the bills that surprise people, the 429 errors on launch day, the logs nobody can read. I teach the operational side of building with model APIs. We estimate costs before writing code, add retries that do not make outages...
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