How to Estimate LLM API Costs Before Launch

Posted on Oct 22 • Originally published at letinix.com

To estimate LLM API costs before launch, start with the price per million input and output tokens, estimate how many tokens each request uses, and multiply by expected request volume. Treat the result as a planning estimate, not a guaranteed invoice: real usage varies with prompt length, output length, caching, retries, model routing and separately billed tools.

The LLM API Cost Calculator applies those assumptions to estimate cost per request, day, month and year. The hard part is not the multiplication. It is choosing assumptions that resemble the workload you intend to ship.

Start with a specific product action, not a vague label such as “one AI request.” A support reply, a document summary and an agent that searches a knowledge base can have very different token and tool-use patterns.

For a first estimate, write down four inputs:

Use observed traces from a prototype when you have them. Before that, define a low, expected and high scenario rather than treating a single guess as a forecast. Keep each assumption visible so it can be replaced with measured usage later.

If you need to estimate the token count of a particular prompt or context first, use a tokenizer appropriate to the model. Token counts are not reliably interchangeable across model families, and text length alone is only a rough proxy.

When a provider quotes text input and output prices per million tokens, calculate the two components separately:

Use these formulas (prices are in USD per million tokens):

Here, each T is the token count for one request, and each P is the price in USD per million tokens. Multiply the per-request estimate by the number of model requests in the period to estimate total inference charges for that period.

The unit matters. A price entered as dollars per million tokens must be divided by one million; a price quoted per thousand tokens, per image, per audio unit or per request needs a different calculation. Confirm the unit on the provider’s pricing page before copying a rate into the calculator.

Suppose a hypothetical application makes 5,000 model requests each day. Assume each request uses 1,200 input tokens and generates 350 output tokens. For illustration only, enter an input price of $2.00 per million tokens and an output price of $8.00 per million tokens. These are example inputs, not current prices for any provider or model.

At 5,000 requests per day, that is $26.00 per day. With 30 days in the planning month, the estimate is $780 per month, or $9,360 for twelve months if the same usage and prices persisted. The monthly token volume is 232.5 million tokens across input and output.

This example deliberately holds request volume, token counts and prices constant. It does not account for growth, day-to-day variation, caching, failed requests, tool fees or discounts. The annual figure is a straight multiplication of the monthly scenario, not a claim about future spend.

The output is a scenario estimate, not a provider invoice.

If a provider charges a different rate for cached input tokens, the cost of those tokens depends on how much input is eligible for cache reads and how often the cache is hit. A simplified estimate is:

C_input = T_input × [(1 − h) × P_input + h × P_cached] / 1,000,000

Here, h is the cache-hit fraction from 0 to 1, and P_cached is the price for cached input tokens. A 30% cache-hit rate is entered as 30 in the calculator’s percentage field.

This is a simplified model, not a universal cache-billing formula. Provider rules can distinguish cache writes from reads, impose minimum cacheable prompt lengths or durations, and charge for cache storage. The calculator’s single cached-input price and hit-rate field does not model all those details. If writes or storage are material, estimate them separately and add them to the scenario.

Do not assume that all repeated text is automatically billed at the cached rate. Check the provider’s caching documentation and the API behavior for the model you use.

Use official pricing documentation to obtain rates for the exact model and service tier. The three major providers document different pricing details, so avoid copying one provider’s cache or tool assumptions into another provider’s estimate.

These pages are references for current provider rules, not fixed values embedded in this article. Prices and model availability change. Before budgeting, check the page for your chosen model, API, modality, service tier, region or other applicable pricing modifier. Record the date and assumptions alongside any estimate you intend to compare later.

A single scenario is a starting point. A budget becomes more useful when it makes uncertainty visible.

Vary the inputs that actually drive your workload. For example, you can model fewer or more requests, shorter or longer prompts, and lower or higher output lengths. Do not label these cases “typical” unless you have measurements supporting that claim.

If request volume is uncertain, forecast it separately from cost per request. If token usage is uncertain, keep input and output estimates separate. This makes it easier to see whether the budget is sensitive to traffic, long context or generated output.

Capture token usage returned by the API, along with the model, request type and relevant cache or tool metadata. Use a representative sample of real product tasks. Compare the observed distribution—not only the average—with your planning assumptions. Long-tail prompts and unusually large outputs can matter even when most requests are small.

Update the estimate when you change the system prompt, retrieval strategy, model, output limit, routing policy or agent loop. These changes can alter token use without changing the number of user-visible actions.

For a simple chat turn, one user action may correspond to one model call. In a workflow, the same action may lead to a planning call, a tool-selection call, one or more follow-up calls and a final response. Estimate each call class separately when you know the workflow. Do not multiply every user action by an assumed fixed number of calls without checking traces or explicitly labelling the assumption.

The calculator models an average request and a request count. It does not model a workflow graph, retries or tool fees as separate line items. For a multi-step agent, calculate each step or call class independently, then sum the results and add separately billed components.

A token-based estimate is not the same as the full cost of operating an AI feature. Depending on the provider and architecture, a first-pass estimate may exclude:

The calculator does not automatically fetch provider rates. It uses the prices and workload assumptions entered by the user, runs in the browser, and does not send those inputs elsewhere. Its results are an estimate of the token charges represented by those fields—not a complete invoice, total product cost or guarantee of future spend.

Before using an estimate to set a budget, verify these items:

The practical goal is not to predict an exact future invoice. It is to build a transparent estimate, understand which assumptions dominate it, and replace guesses with observed usage as the application matures.

This article was originally published on Letinix.