How To Use LLMs Without Breaking Your Budget
An AI demo is easy to build. You get an API key, write a prompt, and it works.
But shipping it to real users is different. Traffic arrives and your costs spike. Your latency grows. Your finance team asks questions.
The gap between a demo and a real product is engineering. You must manage cost and speed.
Control your output to save money
Most APIs charge per token. They charge for what you send and what they send back. Output tokens cost more than input tokens.
Do not just trim your prompts. Focus on the answer. • Ask for JSON. • Ask for one sentence. • Set a max token limit. • Tell the model to be brief.
Short answers are cheaper and faster.
Reduce the number of calls
The cheapest call is the one you never make.
- Use caching. Many users ask the same questions. A cache turns a slow API call into a fast lookup.
- Use a router. You do not need a massive model for every task. Use a small, cheap model for easy work. Use the expensive model only for hard tasks.
Improve the user experience
Sometimes you cannot make the model faster. You can make it feel faster.
- Stream responses. Show text as it generates. Users start reading immediately. This makes the wait feel shorter.
- Show progress. If the work takes steps, tell the user. Use messages like "Searching documents..." instead of a blank loading spinner.
Manage slow requests
A few very slow requests can ruin your product. Do not let them hang.
- Set strict timeouts. Decide what happens if a request takes too long.
- Use retries with limits. Do not retry forever.
- Use circuit breakers. If the provider is down, stop sending requests and show a fallback.
Track your data
You cannot fix what you do not measure. Log these three things for every request: • Input tokens • Output tokens • Total latency
Track these by feature. You will likely find one specific feature that causes most of your costs.
Stop treating the model as magic. Treat it as a slow, expensive dependency that you must manage.