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Forecast an LLM inference bill by measuring representative requests, applying the selected model’s current input, output, and feature-specific rates, then scaling that cost across expected traffic. A token estimate is a planning figure, not an invoice: validate it against usage returned by real provider calls, and recheck the rate card before launch.
What an inference estimate includes
This workflow estimates API charges for model requests. It does not automatically include hosting, databases, observability, staffing, taxes, or other deployment costs. Treat those as separate budget lines if you need a broader cost-of-operation forecast.
For a simple token-priced request, estimate input and output independently:
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request_cost = (input_tokens / 1_000_000 * input_rate) + (output_tokens / 1_000_000 * output_rate)
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Use rates in the same currency and per-million-token units as the provider’s rate card. Add separate charges for cached input, cache writes, tools, modalities, or other billable features when they apply. A provider may also price different context-length tiers, regions, or service tiers differently, so a single blended rate can mislead.
For a workload, multiply each request class by its expected volume and sum the results:
estimated_period_cost = sum(requests_in_class * estimated_cost_per_request_for_class)
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Build a representative request
Start with an actual application action, such as answering a support question or summarizing a document. Count everything the API request sends, not just the visible user text. Include:
- System and developer instructions, user content, and retrieved context.
- Tool definitions and the expected number of tool calls.
- Output limits or a realistic target length.
- Images, audio, files, or other non-text inputs the product will use.
Token counts depend on the model’s tokenizer and the request’s structure. Roles, boundaries, schemas, tools, and multimodal content can affect usage. OpenAI cautions that “A plain-text token count does not necessarily include all tokens in an API request.” Its token guidance also gives rough English-language estimates of about four characters or three-quarters of a word per token; these are approximations, not conversion rules for every model or request.
Count tokens with the selected provider in Python
Use the provider’s supported counter for the model and request format you plan to ship. One provider’s tokenizer is not a universal cross-provider meter.
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The Gemini API guide documents a Python token-counting call using the google-genai client. Adapt the model name and contents to your actual request:
from google import genai
client = genai.Client()
response = client.models.count_tokens(
model="YOUR_GEMINI_MODEL",
contents="Your representative prompt goes here",
)
print(response.total_tokens)For structured or multimodal content, pass the corresponding request contents rather than reducing them to a plain-text approximation. See Google’s token guide for the current Python interface and supported usage fields.
OpenAI
For plain-text tokenization, OpenAI’s guide points to tiktoken and selecting an encoding for the target model. That is useful for a text estimate, but it should not be treated as a complete count for every API request. For full Responses inputs, OpenAI describes an input-token counting API that accounts for message roles and boundaries, images, files, tools, and conversations. Follow the current token-counting guide for the supported method and request shape.
Estimate output separately from visible text
Use representative completions or a realistic output cap to estimate generated tokens. A maximum output setting is a ceiling, not a prediction that the model will use all of it. Conversely, visible answer length alone may understate billable output: relevant models can include reasoning tokens, and usage can include structural tokens that do not appear as ordinary returned text. Google exposes thinking-token usage separately in its response usage details; OpenAI discusses reasoning and other usage in its token guidance.
Keep separate fields for estimated input and output per request class, and leave room for output variability. Do not assume that a concise displayed answer necessarily means low output usage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for caching and other price categories
Do not price all input tokens at the ordinary input rate if the provider bills eligible cached tokens differently. Count only prompt tokens that the provider’s caching feature actually treats as cached. OpenAI describes prompt caching as reuse of a matching rendered prefix; repeated meaning alone does not guarantee a cache hit. Track cached-token and cache-write usage alongside latency and realized cost using its prompt-caching guide. Anthropic’s pricing documentation distinguishes cache writes from cache hits.
For planning, make the cache assumption explicit: no cache, an expected hit fraction based on observed requests, or a low-to-high sensitivity range. Apply the matching cache rates only to eligible tokens. Also check whether the request incurs separate tool, image, audio, or other feature charges, and whether its context length or service region changes the applicable rate.
Scale the estimate to expected traffic
Forecast each meaningful request class separately, then total the period. Include relevant user journeys, retries, and background jobs rather than multiplying one idealized prompt by all traffic.
- Estimate input, output, and eligible cached tokens for each representative request class.
- Apply the current rate categories and any feature charges to get a per-request estimate.
- Multiply by expected requests for that class in the period.
- Sum the classes to produce the period forecast.
- Build low, base, and high scenarios by varying traffic, input size, output length, and cache-hit assumptions.
These scenarios are your planning assumptions, not provider-published forecasts. The cost of a task also depends on how a model tokenizes the same content and how much output or reasoning it produces. As OpenAI’s documentation puts it: “A lower price per million tokens does not necessarily produce a lower total cost: models can tokenize the same text differently and generate different amounts of output or reasoning.” Compare candidate models on the same representative tasks and request mix, using projected cost per task and period—not only the listed input price.
Validate against real usage before launch
Run a representative set of requests and compare the forecast with the usage fields returned by the provider. The returned usage is the basis for reconciling an estimate with actual API consumption.
- For Google, inspect the documented input, output, thinking, cached-content, tool-use, and total usage fields where available.
- For OpenAI, compare forecast counts with the usage returned for representative calls, using the applicable API documentation.
- After launch, aggregate actual usage and cost by model, endpoint, feature, user journey, and time period.
Recalculate when the model, prompt, retrieval strategy, tools, traffic mix, cache behavior, or provider rate card changes. For model comparisons, hold the task and request mix constant and account for input and output usage, reasoning behavior, cache categories, context tiers, modalities, tools, geography or service tier, and whether you use list or negotiated pricing.
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