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Use a three-layer meter: estimate the exact request before dispatch when you need a routing or context-window decision, capture the provider’s actual usage response for accounting, and write one normalized event for every model call to your own ledger. Keep provider-specific fields and the raw usage payload alongside agent, subagent, run, and request identifiers. This lets you compare providers without treating estimates as bills, losing cached or reasoning tokens, or mistaking a missing streaming usage report for zero.
What should an AI token-monitoring system measure?
A useful monitor answers three different questions: how large a request is before it is sent, what the provider says the completed call used, and how that call belongs to an agent run. Those answers come from different places, so one counter is not enough. Preflight token counts support decisions; response usage supports accounting; a normalized event ledger supports attribution and cross-provider reporting.
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Agent-level totals are useful, but they conceal how work was done. A single run may include several model calls, tool calls, handoffs, retries, and subagents. Retain the individual request records and aggregate them into run, agent, user, or project views as needed.
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Why do preflight counts and provider usage differ?
A preflight count is a planning estimate for the request shape you submit to a counting endpoint; the provider’s returned usage is the accounting record for the call. Differences can arise when the counted request does not exactly match the dispatched request, when the model or tokenizer differs, or when the provider reports categories that the preflight number did not include.
Count the final structured input with the target model rather than estimating from a text excerpt. For example, a prompt-size check that counts only message text can miss tool schemas or multimodal content. Anthropic’s token-counting endpoint accepts the structured input shape used for message creation, including tools, images, and PDFs. OpenAI provides model-specific tokenization guidance and a Responses input-token endpoint for counting request input.
Even a correct input count cannot stand in for completed usage. Output has not yet been generated, and input categories such as cached tokens may be reported separately in the final response. Model families can also tokenize identical input differently: Anthropic documents that newer model families may produce different counts for the same text. Recount against the exact model when migrating or changing model versions.
How should the three-layer meter work?
Separate planning, provider capture, and normalized reporting. That division keeps a useful estimate from being misrepresented as final usage and preserves provider detail even as reporting needs or billing rules change.
1. Estimate the request before dispatch
Run a provider-specific count when you need to check a context window, choose a model or route, or decide whether a request fits a budget. Give the counter the same structured request that will be sent, and record the model used for the estimate. Store the result as an estimate with its timestamp and request version; do not add it to actual usage totals.
Preflight is an operational control, not a promise that a request will complete or that its eventual bill will equal an estimated token total. If an estimate is unavailable, fails, or is too costly for a particular call path, record that fact rather than silently substituting a word-based guess.
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2. Capture the actual response usage
After each provider call, extract and persist the usage object from the response. Keep the provider’s field names intact in the raw payload as well as mapping them to normalized fields. If a response is interrupted or lacks usage, mark the record incomplete or unknown; do not fill missing values with zero.
For OpenAI Chat Completions, the response usage fields include prompt_tokens, completion_tokens, and total_tokens. OpenAI Responses uses input_tokens, output_tokens, and total_tokens. Treat these as endpoint-specific schemas, not as interchangeable labels. For Anthropic, capture the actual usage from the Messages response. The count-token endpoint helps plan a request, but it does not replace the completed message’s usage data.
3. Write a normalized event for every call
Emit one immutable usage event per model call. An event should retain enough context to identify the provider request, attribute it to the correct part of an agent run, and reconstruct the normalized totals later.
| Field | Why retain it |
|---|---|
provider and model |
Identify the usage source and the model whose tokenization and pricing apply. |
agent_id, subagent_id, and run_id |
Attribute individual calls to the agent hierarchy and the larger run. |
request_id and attempt identifier |
Correlate your event with a dispatched request and distinguish retries. |
timestamp_utc |
Put events from different systems on a consistent timeline. |
| Input and output token counts | Preserve the main usage categories rather than only a run-level sum. |
| Cached-input tokens | Keep cached input visible because it can affect cost and provider reporting. |
| Reasoning or thought tokens | Retain separately when the provider reports these categories. |
| Total tokens | Preserve the provider-reported total and compare it with normalized category sums. |
| Estimated cost and price-table version | Make cost calculations reproducible when rates change independently of token counts. |
| Raw usage payload and completeness status | Retain provider detail for audit, future remapping, and incomplete-response handling. |
Normalize shared concepts, not away the distinctions between providers. For example, a common ledger can have normalized input and output columns while retaining the original response object and the provider’s own category names. If a provider adds or changes a usage field, the raw record gives you a way to revisit prior events without pretending your earlier mapping contained information it did not.
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Assign an internal request identifier before dispatch and carry it through the call path. Attach the current agent, subagent, and run identifiers at the point where the model request is made; do not try to infer ownership later from prompt text or timestamps alone.
- Create the run context. When an agent run begins, establish a stable
run_idand identify the agent responsible for its work. - Assign a request identifier. Before each model dispatch, create a unique internal
request_idand an attempt identifier. Record the provider and selected model against that request. - Carry attribution through handoffs. When a tool call, handoff, or subagent leads to another model call, create a new per-call event and associate it with the appropriate run and agent hierarchy.
- Capture each response independently. Store that call’s provider usage against its request and attempt, then expose totals at the run level by aggregation.
- Handle retries explicitly. Preserve attempt identity. Do not emit duplicate usage events for a retry that the provider confirms did not result in a model call; if a model call did occur, retain its usage as its own event.
- Close the run without discarding detail. Calculate agent and run totals from the per-request records, retaining the underlying rows for investigation and reconciliation.
This approach prevents a common attribution mistake: counting a run as if it were one provider request. OpenAI’s Agents SDK aggregates usage across all model calls during a run, including calls that produce tool calls or handoffs, and exposes request-level entries as well as cached and reasoning details. Use its aggregate as a convenient run view, not as a reason to discard individual events. OpenAI observability also exposes subagent_id, but the value can be best-effort or null and may change while accounting arrives. Preserve your own run and request correlation rather than assuming every observability field is complete.
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How can you capture usage when a response is streamed?
Streaming needs an explicit usage-capture path because the last usage record may arrive separately from the text chunks. For OpenAI streaming Chat Completions, set stream_options.include_usage=true and capture the additional usage chunk immediately before [DONE].
If the stream is interrupted before that final chunk arrives, the usage is unknown or incomplete. Do not infer zero from an empty field, a disconnected client, or the absence of a final chunk. Record the interruption and completeness status, then reconcile through an available provider reporting source where possible. Any later reconciliation should be linked to the original request, not written as an unrelated call.
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For other endpoints or providers, confirm their own streaming usage behavior rather than assuming that the OpenAI Chat Completions setting or chunk format applies. A shared adapter can present a common capture interface, but the implementation must account for provider-specific response and stream semantics.
How do you calculate costs without corrupting token records?
Store tokens and costs as related but separate data. Token usage is captured from the model response; cost is calculated using a versioned price table that reflects the provider, model, applicable usage categories, and effective pricing period. Keeping the two separate lets you recalculate historical cost estimates if a price changes without rewriting the underlying usage event.
Do not flatten cached input, reasoning, or thought usage into a single input/output figure when the provider reports those categories separately. They can materially affect cost, and losing them makes cross-provider comparisons less faithful. Keep the reported categories, the normalized mapping, and the version of the pricing data used for each calculation.
For reconciliation, compare like with like: align the provider, model, time interval, and category definitions before investigating a difference. Your ledger records individual requests and can explain agent attribution; provider dashboards provide another reporting view. A dashboard total may not be directly comparable to an application’s run total if the time window, model grouping, or treatment of incomplete requests differs.
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Which implementation pattern works across providers?
Put provider-specific behavior behind an adapter with three core operations: count_input(request), send(request), and extract_usage(response). The interface can be shared while each adapter handles the target provider’s counting endpoint, request format, response fields, and streaming details.
- Build the request once. Construct the structured request that the adapter will count and send. Avoid counting an earlier text-only representation if the dispatched request later gains tools or multimodal content.
- Run preflight when needed. Call the target provider’s input counter for routing, context, or budget decisions and store that result as an estimate.
- Attach internal identity and dispatch. Add the stable request and attempt identifiers, then send through the selected provider adapter.
- Extract and preserve usage. Parse the completed response or stream usage, save the raw payload, map known fields, and mark missing data as incomplete or unknown.
- Append an immutable event. Store attribution, UTC time, provider/model metadata, token categories, and the pricing-table version used for cost calculation.
- Aggregate for views. Build agent, subagent, run, model, and provider totals from event rows rather than replacing the rows with a single aggregate.
Use UTC timestamps and retain provider/model version metadata. When a provider’s response schema changes, update its extractor and preserve the original payload so that historical events can be interpreted with the mapping available at the time.
What can Google Cloud monitoring show?
When third-party model calls are routed through Google Cloud’s documented workflow, metadata logging and Observability Analytics can be used to query inference_response records. The official example groups records by model and sums prompt, candidate, cached-content, thoughts, and total token metadata over a time range.
This is useful for a cloud-level view of calls routed through that workflow and for model-level aggregation. It does not replace application-side request identifiers and agent attribution unless those identifiers are available in the logged metadata and carried through your workflow. Keep the application ledger as the source for mapping calls to agent runs, and use cloud analytics as an additional operational or reconciliation view.
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What should you check when choosing a monitoring approach?
Evaluate a monitoring design against the requirements that matter to your workload; a dashboard with many totals is not necessarily a faithful ledger. The following checks expose the trade-offs.
- Coverage: Which providers, models, modalities, tools, and agent frameworks does it actually capture?
- Fidelity: Are raw provider fields retained? Can cached, reasoning, thought, and tool-related usage remain distinguishable where the source reports them?
- Attribution: Can events be correlated to agent, subagent, user, project, request, retry attempt, and trace or run?
- Timeliness: Does it provide only preflight estimates, or also streaming updates, near-real-time events, and delayed billing reconciliation?
- Auditability: Are events immutable, timestamps normalized to UTC, exports available, and cost calculations replayable against a recorded price-table version?
- Operational controls: Can the system support budgets, alerts, rate-limit awareness, context-window checks, and routing policies?
For Anthropic’s token-counting endpoint, the 2026 documentation lists rate limits of 5,000 requests per minute for Start, 10,000 for Build, and 20,000 for Scale. These limits are tier-specific and may change, so verify the current tier and applicable limit before designing a counter that depends on frequent preflight calls.
How should you investigate a token-count mismatch?
Trace one request at a time before changing aggregate calculations. Most apparent discrepancies become easier to explain once the same request shape, model, usage categories, and reporting interval are compared.
- Match the request. Confirm that preflight counted the exact structured request actually dispatched, including tools and any supported non-text content.
- Match the model and endpoint. Check that the estimate and response came from the intended model and that endpoint-specific usage labels were mapped correctly.
- Compare categories before totals. Inspect input, output, cached input, and reasoning or thought fields separately where available; avoid assuming two providers’ category names have identical meanings.
- Check the event lifecycle. Look for retries, handoffs, subagent calls, and interrupted streams. Verify that real calls have their own events and that unconfirmed no-call retries were not counted.
- Check time and aggregation boundaries. Compare equivalent UTC intervals and the same provider/model grouping in the ledger and provider reporting view.
- Reconcile missing data honestly. Leave usage unknown or incomplete when the response did not provide it, and link any later reconciliation back to the original request record.
OpenAI’s 2026 Help Center puts the central streaming caveat plainly: “A missing final usage chunk does not establish that the request used no tokens.” The safe operational interpretation is that absence of reported usage is a data-quality state to resolve, not a numeric value to silently add to a total.
Frequently Asked Questions
Can I estimate token usage from a word count?
Only as a rough planning aid for English text: OpenAI’s 2026 Help Center gives approximations of four characters or three-quarters of a word per token. Languages, formatting, model tokenizers, and structured or multimodal inputs make that unsuitable for billing or precise context checks.
Are provider dashboards enough to track an agent’s usage?
They are useful for provider-side reporting and reconciliation, but a cross-provider agent view needs an application-level mapping from each request to its agent and run. Dashboard totals alone may not preserve your application’s subagent or retry relationships.
Should failed requests count as zero tokens?
Not automatically. Determine whether the provider actually processed a model call and returned usage; if that cannot be established, preserve the event as unknown or incomplete rather than reporting a false zero.
Do token-counting requests have their own rate limits?
Yes. Anthropic’s 2026 documentation lists tiered token-counting limits, but the limits can change; check the current limit for the account tier before relying on frequent preflight calls.
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