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What goes into an AI agent’s cost?
Use this as the working model for a task:
Total cost = model inference + separately priced tools + retries and failed attempts + applicable compute or hosting + external API charges.
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The total depends on the workflow and what actually happens during a run; there is no single prompt, model, or token count that predicts every agent’s cost. OpenAI’s agent documentation advises estimating across all calls needed to complete a task. An agent may call a model to plan, invoke a tool, interpret its result, and produce an answer—and may make additional calls through a root agent or subagents.
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Build a per-task estimate
- Map the workflow. List the model and provider used at each step, including any root agent, subagents, or model changes.
- Estimate call counts. For a typical task, count every model turn, tool interaction that carries a charge, correction loop, retry, and likely failure. Create low, typical, and high scenarios rather than assuming every run follows the same path.
- Record usage by call. Track input, cached input, output, and cache-write quantities where applicable. Include instructions, tool definitions, conversation history, user input, files or images, tool results, and generated tool-call arguments when they enter the model context.
- Apply current rates. Multiply each usage quantity by the matching rate on the provider’s current price card. Keep separate tool fees distinct from model-token charges; check whether retrieved or returned content also counts as model input.
- Add non-model costs. Include retries and failed attempts, applicable sandbox or runtime charges, hosting, and third-party API services.
- Scale by workload volume. Multiply the per-task scenarios by the expected number of completed tasks, then compare the resulting forecast with measured usage and provider billing.
This is a practical forecasting method, not a provider-guaranteed formula. Make assumptions visible: task mix, call counts, token volumes, retry behavior, and forecast period all affect the result.
Count the costs that are easy to miss
Every model turn and its context
Count all model calls, not just the first request and final answer. Instructions and tool schemas may be sent repeatedly; conversation history and tool results can grow the input context as a task proceeds. Files and images may also affect usage. Generated text, structured tool arguments, and reasoning can contribute to output usage according to the model’s pricing and usage reporting.
Tool charges and returned content
Tool pricing differs by provider and tool. A tool may have a per-call fee, a content-token charge, or both. Google’s Gemini pricing documentation describes agent costs as underlying token use plus tool use and distinguishes tool-specific billing treatments. Do not assume a tool call is either free or fully covered by the model price: check its own terms and whether its results are passed back into the model.
Retries, errors, and throttling
Include repeated attempts in the estimate, even when an attempt fails. OpenAI notes that unsuccessful requests count toward per-minute limits, and eligible SDK retries may already be enabled. Adding a second retry loop without checking the SDK’s behavior can multiply attempts or make throttling worse. Honor a Retry-After value when one is provided; log each attempt so failed work does not disappear from your accounting. See OpenAI’s rate-limit guidance.
Hosting and other services
Model and tool prices are not necessarily the whole bill. Add any applicable sandbox compute, hosting, data services, and third-party API charges. Attribute these to the task where possible, or document how they are allocated when costs are shared across workloads.
Account for caching and uncertain usage
Prompt caching can lower the cost of reused input, but repeated text does not guarantee a cache hit. OpenAI’s prompt caching guide describes requirements such as a matching prompt prefix and eligibility and lifetime rules. Cache writes may have a distinct charge, and the guide cautions that usage fields may not show the exact charge when cache-write pricing applies.
For planning, calculate a conservative case using uncached input. Add a separate cached scenario only when the relevant model’s documented behavior or your own measurements support it. Task paths, result sizes, model choices, context growth, and retry frequency can all vary, so sample both successful and unsuccessful runs before treating an estimate as a budget.
Compare price cards without relying on headline rates
Provider rate cards describe unit prices, not the average cost of a representative agent task. There is no general “typical agent cost” benchmark established by the cited provider documentation. Check the current official price pages before making a budget or publishing a numeric comparison: rates and terms can change.
When comparing providers or designs, check the following categories together:
- Input, output, cached-input, and cache-write rates.
- Which model is used for each agent step, and whether it meets that step’s requirements.
- Tool-call fees and whether tool results are billed as model tokens.
- Context limits and usage tiers.
- Batch, priority, or other processing modes.
- Geography, data-residency, and marketplace billing adjustments.
- Usage measurement and export options.
- Rate limits and how retries affect traffic.
OpenAI’s API pricing page lists token categories and separate tool prices; in some cases, search content tokens are billed at model rates. Google’s pricing documentation distinguishes underlying inference from tool usage. Anthropic’s pricing documentation includes feature-specific prompt-cache terms and describes marketplace conversion and geography-related pricing. These differences make a comparison based only on headline input-token rates incomplete.
Measure actual usage and reconcile the forecast
Log usage at the request level and attach your own run or task identifier to model calls and tool activity. That lets you connect charges to completed outcomes, inspect expensive paths, and separate normal work from retries or failures.
OpenAI documents response-level usage details and a Usage Dashboard for current and past periods in its API usage dashboard guidance. Dashboard times are shown in UTC, and the dashboard can be filtered by project. Some costs, including Scale Tier subscription costs, may be attributed to the organization rather than a project, so reconcile at the billing level where those costs appear.
After collecting a representative sample, compare estimated totals with provider usage and billing. Investigate outlier tasks and retry counts, update the low, typical, and high per-task estimates, and reforecast using expected task volume. Include the sample period and assumptions alongside the forecast so readers of the budget can see what it covers.
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