How to Put an LLM in Your Product Without Wrecking Costs or Latency

An AI demo is easy to build. You get an API key, write a prompt, and show it to your team.

Then you ship it. Traffic arrives. Your costs explode and your latency spikes.

Moving from a demo to a real product requires cost and latency engineering. Here is how you do it.

Control your output

Most APIs charge by tokens. Output tokens cost more than input tokens.

People spend time trimming prompts but let the model ramble. This is a mistake.

To save money and time, constrain the output:

  • Ask for JSON.
  • Request a single sentence.
  • Set a max_tokens limit.
  • Tell the model to be brief.

Short answers are faster and cheaper.

Stop making unnecessary calls

The best way to save is to not call the model at all.

  • Use caching: Store responses for common questions. A semantic cache can help if the questions are similar but not identical.
  • Use routing: Do not use your best model for simple tasks. Use a small, cheap model for classification. Save the expensive model for complex work.

Improve the user experience

If a response takes time, make it feel fast.

  • Stream tokens: Show words as they generate. This reduces perceived wait time.
  • Show progress: If the task has multiple steps, tell the user what is happening. Use text like "Searching documents..." instead of a silent spinner.

Manage the "tail" latency

Some requests will always be slow. Do not let them break your product.

  • Set timeouts: Decide what happens if a request hangs. Use a fallback or a smaller model.
  • Use retries: Add retries for small errors, but cap them.
  • Use circuit breakers: If a provider goes down, stop sending requests immediately to avoid long waits.

Track your data

You cannot fix what you do not measure. Log these three numbers for every request:

  • Input tokens.
  • Output tokens.
  • Total latency.

Look for the cost per successful user outcome. A feature that works is better than a cheap feature that fails.

Stop treating the LLM as magic. Treat it as a slow, expensive dependency that you must manage.

Source: https://dev.to/muhammadzainnaseer/how-to-put-an-llm-in-your-product-without-wrecking-your-costs-or-your-latency-89a

Optional learning community: https://t.me/GyaanSetuAi