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An AI agent’s real cost is the cost of the whole run—not just the model call that produced the answer you see. Measure every model request, retry, delegated-agent step, and relevant tool or service charge; then reconcile your estimate against provider usage and billing records. Treat missing usage as unknown, not zero.
Why the final answer is not the full cost
One user-visible answer can involve several model requests, tool calls, retries, or work delegated to another agent. Each step can consume tokens or incur a separate charge. OpenAI’s Agents API documentation on observability and usage advises accounting for root-agent and subagent work, including retries, as well as applicable tool, sandbox-compute, and third-party service charges.
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A total for the whole run helps with budgeting, but it does not show which request or retry drove the cost. Keep request-level records as well as a run total so you can both allocate spend to a task and investigate its expensive steps.
Choose what you are measuring
Start with the business outcome you want to cost: a completed task, user request, workflow, or customer. Assign it a stable run or task identifier and propagate that identifier through model requests, tools, retries, and delegated work. This makes individual events attributable to the same outcome rather than leaving them as disconnected usage records.
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Capture each model request and its usage
Persist one record per model request. Include the provider and model, timestamp, request or run identifier, status, attempt or retry information, and token usage. Retain input and output counts separately, plus cached-input and reasoning-token details where available; these categories may have different rates.
The OpenAI Agents SDK exposes both total usage and a list of per-request usage entries. Its usage can include calls that produce tool calls or handoffs, so an aggregate is useful for the run total while the individual entries help explain where tokens were used. See the Agents SDK usage guide.
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Do not assume every provider adapter supplies usage automatically. The SDK guide notes that some backends may require usage inclusion to be enabled. Retaining the raw provider usage payload also helps you distinguish a reported zero from a field that was not returned.
Trace the steps that produced the run
Keep an ordered trace of model and tool steps, including their duration and status and their association with the root or delegated agent. OpenAI’s tracing documentation describes recording inputs and outputs, tool arguments and results when available, timestamps, durations, and statuses.
Traces let you inspect repeated, failed, or delegated work and connect it to the run. They are an explanation of what happened, not proof of the final amount billed: usage can be missing or updated later, and provider billing is a separate view.
Calculate an estimate without hiding assumptions
For each request, apply the rate that matches its model and token category. Store the price table and its effective date alongside the calculation so a later rate change does not silently rewrite historical estimates. Record costs outside model tokens—such as tool, retrieval, sandbox, or third-party service charges—separately when they apply.
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LangSmith documents automatic token-based cost calculation when token counts, model or provider, and prices are available, along with manual cost entries for other run types such as tools and retrieval. Its cost-tracking guide describes that approach. Any estimate depends on the usage and pricing configuration supplied to it.
Do not choose a model based only on its advertised per-token price or the length of its visible answer. OpenAI’s token guide notes that models can tokenize the same text differently and generate different amounts of output or reasoning. A lower token rate therefore does not guarantee a lower cost for your representative task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reconcile estimates with provider usage
Compare your instrumented totals with provider usage over matching time windows and scopes. For OpenAI, the Usage Dashboard displays data in UTC, supports project selection, and offers usage exports. Response usage fields also vary by endpoint, so investigate gaps where streamed or otherwise incomplete responses may not provide the data you expect. Keep organization-level items distinct from project-level estimates where applicable.
Use the dashboard and billing records as a reconciliation view, not as a substitute for tracing each workflow. Your traces can explain which task or retry generated activity; provider records help verify usage at the account or project scope they report.
What each measurement approach can and cannot tell you
| Approach | What it contributes | Limitation to account for |
|---|---|---|
| Provider response usage | Request-level token counts and endpoint-specific usage details. | A response count may not capture the full agent workflow or costs outside model tokens. |
| SDK run accounting | Aggregated run totals and, in the Agents SDK, per-request entries. | Some adapters may omit usage unless configured; aggregates alone do not identify cost drivers. |
| Tracing | Step sequence, model and tool activity, status, duration, and recorded data for investigation. | Usage may be delayed or unknown, and a trace is not necessarily a final bill. |
| Third-party cost tracking | Can calculate model costs from configured usage and prices, and record manual costs for other run types. | Coverage and estimates depend on the product, supplied usage, and pricing configuration. |
When comparing tools or designing your own instrumentation, check whether they cover retries and delegated work, provide request-level detail and token categories, accept non-model costs, aggregate by task or customer, support provider reconciliation, and meet your retention and privacy needs. Also account for the instrumentation or service’s own cost; no single approach is established as best for every deployment.
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Handle missing and changing usage carefully
- Missing is not zero. Usage fields can be null, absent, delayed, or updated. Mark such values unknown and reconcile them later rather than reporting them as free usage.
- Keep the token categories separate. Cached input remains billable under the cited Agents API guide, and applicable models may have cache-write charges. Available usage fields may not expose enough detail to calculate every such charge exactly.
- Include non-token charges. Tools, sandbox compute, and third-party services can add cost beyond model tokens.
- Test representative tasks. Tokenization, output, and reasoning volume can change the total even when a model’s nominal per-token rate is lower.
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