LLM API Pricing: How to Estimate and Reduce Your AI Costs

What you actually pay for

Nearly all LLM providers charge per token — the chunks of text a model splits input and output into. Keep three things in mind:

• Input and output tokens are priced differently; output is usually more expensive.
• Input includes everything: system prompt, chat history, RAG documents, tool definitions.
• Models from the same provider can differ in price many times over, so model choice is your main lever.

Tokens per word depend on the language and tokenizer: Russian and Uzbek text usually needs more tokens than the same meaning in English. Check on your own data — providers offer token counters.

How to estimate a monthly budget

The basic formula:

Monthly cost =
  requests per month × (input tokens × input price
                        + output tokens × output price)

Steps:

1. Take 20-50 real requests from your scenario and compute average input and output tokens. API responses usually return these numbers.
2. Estimate traffic: users × requests per user per day × days.
3. Count hidden calls: agents and chains make several model calls per user action, and retries on errors cost money too.
4. Add headroom for growth and peaks, and compare 2-3 models in one table.

Prices change, so take them from providers' official pricing pages at the time of the estimate.

How to cut costs

Model routing

Not every request needs the strongest model. Classification, field extraction and short answers are often handled by a cheap model, while complex reasoning goes to an expensive one. A simple router by task type, or a cascade of "cheap first, strong when confidence is low", noticeably lowers the bill.

Caching

• Response cache: identical questions (FAQ, reference data) can be served from your own cache without calling the model.
• Provider prompt caching: if the beginning of a prompt (instructions, documents) repeats, many APIs charge less for the cached part. Keep the static part first and the variable part last.

Shorter prompts and answers

• Remove repetition and outdated rules from the system prompt.
• Send only relevant chunks through RAG, not whole documents.
• Trim or summarize the history of long conversations.
• Limit answer length and ask for a format without filler intros.

Batch processing

For tasks that do not need an instant answer (labeling, translations, overnight reports), many providers offer a batch API with a discount in exchange for delayed processing.

Common mistakes

• Estimating from one "ideal" request instead of a real sample.
• Forgetting chat history tokens, which grow with every message.
• Picking the most powerful model "just in case".
• Setting no spending limits or alerts — one looping agent can burn a monthly budget.