Token usage logging is the practice of recording how much model processing an application request consumed, together with enough context to interpret that measurement. It is not the same as recording authentication tokens. Access tokens are credentials and should not be copied into a usage log. Keeping that distinction explicit prevents a surprisingly dangerous ambiguity in the phrase “token log API.”
A useful usage record can support capacity planning, release comparisons, and estimated cost allocation. It cannot become an accurate invoice simply because a dashboard multiplies two numbers. This guide explains how to keep estimated usage, reported usage, and pricing assumptions separate so the resulting analysis remains understandable.
Record the request identity and the attempt
One user operation may generate several model requests. A retrieval workflow could summarize passages, draft an answer, and retry a failed generation. Record a workflow identifier for the business operation and an attempt identifier for each provider call. Keep the provider's request reference when it is available and appropriate for your access policy.
This structure prevents two common mistakes. Counting every attempt as a separate user task overstates completed work, while keeping only the final successful attempt can omit processing consumed by earlier attempts. Usage aggregation should state its unit: provider attempts, successful model calls, or complete application workflows. Give each view a clear label rather than expecting one total to answer all three questions.
Separate estimates from reported measurements
A preflight token count can help assess whether an input fits a chosen request budget. It is not automatically the final quantity associated with execution. The Claude token counting documentation describes counting a message before sending it and notes that the returned count is an estimate that may differ from the input tokens used by a subsequent message request.
Use separate fields such as estimated_input_tokens and reported_input_tokens. Add a provenance field describing where a value came from. Never overwrite an estimate with a reported value without retaining the distinction needed by downstream readers. A missing reported quantity is unknown, not zero. Your aggregation should expose how many requests have incomplete usage rather than quietly treating them as free processing.
Preserve the provider's categories
Providers and request modes may expose different usage categories. Your own schema might distinguish ordinary input, output, cache-related quantities, or other provider-specific measurements. Do not force every response into a simplistic two-number model if doing so changes the meaning. Retain a documented normalized representation and, where appropriate, a limited source category map.
Be careful about double counting. A subcategory may already be included in a broader total. Before adding fields together, verify their definitions for the actual provider and API version. Record those mapping decisions in code and fixtures. The token log API overview uses intentionally simple sample data, with input and output values clearly labeled as example measurements rather than a universal billing model.
Capture completion state alongside usage
A response can end because it completed, reached a configured limit, was cancelled, or failed. Usage information may arrive differently across response modes. Your event should capture what the application actually observed and whether the usage record is complete. Avoid inventing a final value when the connection ended before that value was received.
For streaming workflows, define the difference between a partial usage update and a terminal usage record. Give updates a stable request reference and a sequence or update policy. If the same final record arrives twice, ingestion should not automatically double the amount attributed to that request. Deduplication and reconciliation are explicit parts of the storage design. They are not guaranteed merely because the source record contains a request identifier.
Calculate estimates with versioned assumptions
A simple illustrative estimate might multiply input tokens by an assumed input rate and output tokens by an assumed output rate. If rates are quoted per million tokens, divide each token quantity by one million before multiplying. Store the currency, rate unit, model or deployment reference, pricing version, and the time at which the rate applies.
For a purely hypothetical example, 2,000 input tokens at an assumed rate of 2 currency units per million contribute 0.004 units. Five hundred output tokens at an assumed rate of 8 units per million contribute another 0.004 units. The combined estimate is 0.008 units. These are invented teaching rates, not current provider prices. Actual reconciliation may involve other categories, discounts, adjustments, and charges outside token processing.
Allocate work without exposing people
Usage attribution is often most useful at the level of a project, feature, environment, or internal cost center. Start there rather than logging names, email addresses, or raw conversation text. A controlled tenant reference may be useful in a multi-tenant application, but it still needs an access policy and a clear purpose.
Do not encode personal information into identifiers merely because they look technical. A request reference derived from an email address can remain identifying. Keep usage events focused on resource consumption and application context. Where a business needs person-level allocation, define who can access that mapping and whether aggregated reporting would serve the same purpose. The logging pipeline should not become an accidental directory of customers or employees.
Compare releases with a stable denominator
A release that uses fewer tokens per request may still consume more overall because it serves more requests or performs more retries. Conversely, a larger prompt might improve completion rates enough that usage per completed workflow changes differently from usage per attempt. Choose denominators that match the decision you are trying to make.
For a release comparison, inspect input and output distributions, completion outcomes, retry counts, and the mix of tasks. Do not interpret a change in average usage as an efficiency improvement without considering workload composition. Use comparable synthetic test sets for controlled experiments and separate them from production aggregates. The AI logging guide explains how configuration versions help attribute changes to the right part of the workflow.
Reconcile rather than conceal mismatches
Build a recurring reconciliation process between request-level usage records and the provider's available account-level reporting. State which period, timezone, and account scope each side covers. Delivery delays, missing final events, different aggregation definitions, and pricing adjustments can all create differences that need investigation.
Keep a reconciliation status rather than silently editing historical records until totals match. Record corrections as traceable adjustments with a reason and an owner. Protect the original measurements from casual mutation. A cost dashboard that openly identifies incomplete requests is more useful than a perfectly tidy total built on unexplained assumptions. Precision in presentation should not exceed the confidence of the underlying data.
Test failure paths and duplicate delivery
Create fixtures for zero reported output, missing usage, a cancelled stream, repeated final events, and a workflow with two attempts. Add a pricing version change at a known boundary. Verify whether the aggregation applies the intended rate to each record and whether your arithmetic avoids unnecessary rounding at intermediate steps.
Check the edge cases
Ask reviewers to reconstruct a small synthetic bill estimate from the raw events. They should be able to explain the quantities, rate assumptions, unknowns, and adjustments without private knowledge of the dashboard. A reproducible calculation is a stronger foundation than a visually impressive chart. Test both individual records and period totals because errors can disappear when positive and negative mistakes happen to cancel out.
Keep usage reporting honest
Begin with request identity, provider-reported quantities, completion state, and provenance. Add cost estimates only after the usage categories and pricing assumptions are explicit. Preserve missing values and show reconciliation coverage in every aggregate where it matters.
Token logging becomes valuable when it helps a team decide which workflows deserve optimization and which measurements need further attention. It should not blur estimates into invoices or hide incomplete records behind a decimal-heavy total. A clear measurement contract, careful attribution, and versioned arithmetic make the same event useful to developers, operations teams, and the people planning resource budgets.
