Szacowanie kosztów API LLM i budżetów tokenów

LLM APIs bill by the token. A token is a chunk of text that averages roughly four characters, or about three quarters of a word, in English. Other languages and code often need more tokens for the same meaning, and each provider's tokenizer counts a little differently. Prices are quoted per million tokens, with separate rates for input (everything you send) and output (everything the model generates). Output is usually priced several times higher than input. A reliable estimate follows five steps: 1. Define each task the feature performs. 2. Measure tokens per request using real sample prompts and the provider's tokenizer or token-counting endpoint. Count the system prompt, retrieved documents, conversation history, user input and expected output. 3. Estimate volume: active users times requests per user per day. 4. Multiply tokens by price and by volume. 5. Add overhead for retries, failed calls, evaluation runs and growth. Here is a worked example using hypothetical prices of $3 per million input tokens and $15 per million output tokens. A request with 2,000 input tokens costs $0.006 for input. Its 400 output tokens cost another $0.006, so the request costs $0.012. At 10,000 requests a day, that is $120 a day, or about $3,600 over a 30-day month, before overhead. The biggest estimating errors come from what people forget: - Most chat applications resend the conversation on every turn. Input grows with each message, so one long conversation costs far more than several short ones. - Retrieval-augmented features can add thousands of tokens of context to each call. - Reasoning models generate internal thinking tokens that are billed as output even when they are not shown. - Agents make many calls for each user action. Discounts change the math. Prompt caching lowers the price of repeated prefixes, and batch APIs discount work that can wait. Always check the provider's current pricing page, because rates change often.