LLM APIs on a Solo Developer Budget
Build and launch a side project on model APIs without a surprise bill, abuse or an overbuilt setup.
A taste of a lesson
I am launching a free AI writing tool next week. What should I do first to avoid a big bill?
Three things before launch. Set a hard spending limit and an alert with your provider, at an amount you can afford to lose. Put the model call behind your own backend with sign in, or at least rate limits per IP, plus a daily request quota per user. Cap input length and output tokens so a single request cannot be huge. Then watch daily spend closely for the first two weeks. Will users need to sign in to use it?
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
- Set provider spending limits and in app quotas before launch
- Protect public endpoints from bots and free riding
- Iterate cheaply with cached responses and small models
- Keep the architecture simple enough to run alone
- Estimate cost per user and choose a free tier, bring your own key or pricing
Lesson plan
- 1 Caps before code Put spending limits and alerts in place before building anything public. Start
- 2 Protecting public endpoints Stop bots and free riders from draining your budget. Start
- 3 Building cheaply Iterate on your app without paying for the same responses repeatedly. Start
- 4 A setup you can run alone Choose an architecture proportional to a solo project. Start
- 5 Cost per user and pricing choices Decide how to cover costs once real people use the app. Start
Try asking
About this tutor
For indie developers, students and freelancers building a side project or small product on model APIs with a tight budget. You learn to cap spending at the provider and in your app, develop cheaply by caching responses while you iterate, choose small models first, protect public endpoints from abuse, keep the architecture simple, and estimate cost per user so you know whether a free tier or price makes sense. The tutor is realistic: free credits and tiers change, abuse of open endpoints is common, and a simple setup that you understand beats a clever one you cannot debug alone.
Reviews
Students can review a tutor after a paid lesson. Nobody has yet.
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...
See Farid's profile and tutorsMore like this
Other tutors on the same or nearby topics.