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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe reliable way to measure LLM spend per customer or product feature is to create one usage record for every model invocation. Attach a stable internal user ID and feature key when the call starts, then store the provider, exact model ID, timestamp, all returned token categories, and any provider-reported cost. Aggregate those records by user, feature, model, provider, date, and environment.
Do not equate visible prompt and answer text with billed usage. Cached-input, reasoning, audio, tool, and other categories can be reported separately. When usage is missing—especially after an interrupted stream—mark it unknown and reconcile it later instead of counting it as zero.
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What you need to measure
A model name and token total cannot answer the questions a product or finance team usually asks: Which customer generated the spend? Which workflow is expensive? Did a retry double the cost? Are estimates different from the provider invoice? Those answers require attribution at the moment each provider call is made.
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Create a generation or usage event for every invocation, including retries, fallbacks, background jobs, and secondary calls such as retrieval, classification, or tool-selection calls when they are part of the request’s cost. A practical event contains:
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- Attribution: a stable internal
user_id, a feature key such assummarize,support_reply, ordocument_search, and an optional account, team, or tenant ID. - Request context: provider, exact model identifier, request or trace ID, session ID, timestamp, environment, and service or application version.
- Usage: input and output tokens plus every additional bucket returned by the provider, such as cached-input or reasoning tokens.
- Money: provider-reported cost when available, and a derived cost calculated from a versioned price table.
- Integrity state: complete, incomplete, or unavailable usage, with an error or interruption reason.
- Raw evidence: the original usage response and pricing-version reference so that a later reconciliation can reproduce the number.
Use an internal identifier rather than a display name. Keep prompts and completions out of feature labels and tags; those fields should describe business context without leaking sensitive content.
Instrument every model call at one boundary
Put a wrapper or middleware around the code that invokes each provider. The wrapper should create the event before sending the request, pass attribution through the request’s context, capture the response in both success and error paths, and close the event only when usage is known or explicitly marked unavailable.
A provider-neutral call flow
- Resolve context. Obtain the authenticated user’s stable ID, feature key, tenant, environment, and a request or trace ID. If a background job has no human user, use a documented service identity such as
system:nightly-indexrather than leaving the field blank. - Record the invocation start. Generate an event ID and persist the model, provider, feature, user, and start time before the network call. This preserves evidence for timeouts and retries.
- Send the request. Include the provider’s exact model ID. Do not substitute a marketing family name; prices and token behavior are model-specific.
- Capture returned usage. Store every token category the response exposes, the provider’s request ID, finish status, and any provider-reported cost.
- Handle failures explicitly. Save timeout, cancellation, rate-limit, and transport errors. If the provider may have processed the request but returned no usage, label the event
usage_unavailablerather than assigning zero tokens. - Calculate a provisional amount. Apply the price-table version that was effective for the model, token category, date, and service tier. Keep this amount separate from any invoice-reconciled value.
- Roll up later. Aggregate immutable events into daily or hourly summaries by user, feature, model, provider, environment, and status.
Keep retries visible
Retries are separate provider invocations and can incur separate charges. Give each attempt its own event and link it to a common parent request ID. A dashboard can then show both the customer-facing request and the attempts that produced its cost, instead of hiding retry spend inside one ambiguous total.
Capture the provider’s real usage fields
Token accounting differs by endpoint and model. For OpenAI API responses, the documented fields differ between Chat Completions and Responses, and some endpoints expose additional cached-input or reasoning-token detail. See the OpenAI usage and cost documentation for the current field behavior.
| API response family | Core fields to store | Additional data to preserve when returned |
|---|---|---|
| Chat Completions | usage.prompt_tokens, usage.completion_tokens, usage.total_tokens |
Cached-input and reasoning-token details, finish information, request ID, and any provider cost |
| Responses | usage.input_tokens, usage.output_tokens, usage.total_tokens |
Cached-input and reasoning-token details, tool-related usage, request ID, and any provider cost |
Store the raw usage object as JSON as well as normalized columns. Normalized columns make rollups fast; the raw object protects you when a provider adds a category that your schema did not yet anticipate.
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Streaming calls need a missing-data state
For streamed Chat Completions, request usage explicitly with stream_options: {"include_usage": true}. OpenAI documents that the usage object arrives in a final chunk before [DONE]. If the connection drops, that final chunk may never arrive. Mark the event as incomplete or unknown and queue it for reconciliation; never turn the absence of a final chunk into zero usage.
Keep partial text and transport telemetry separate from billing fields. A stream that delivered several visible tokens can still have an unknown billed total if the terminal usage record was lost.
Turn tokens into cost without false precision
Token-derived cost is an estimate unless it uses the exact model, token categories, and price rules that applied to the call. OpenAI’s API pricing documentation separates input, cached input, and output for many models and lists separate charges for some tools. Rates, tiers, and model availability can change, so store a price-table version and effective date with every calculation.
Use a versioned price table
Key each rate by at least:
- provider and exact model ID;
- token category, such as input, cached input, output, or reasoning output;
- effective start and end dates;
- context or usage tier;
- service tier or endpoint terms where applicable; and
- tool or modality charges that are billed outside token counts.
A calculation is conceptually:
derived_cost = (input_tokens × input_rate)
+ (cached_input_tokens × cached_input_rate)
+ (output_tokens × output_rate)
+ reasoning_or_other_token_charges
+ tool_or_modality_chargesThe rates in that formula must be looked up by the exact model and price version. Do not publish a universal “cost per 1,000 tokens” figure without naming the model, category, tier, endpoint terms, and date checked.
Prefer provider-reported amounts when available
If the provider returns a charge or an authoritative usage breakdown, retain it and use it for reconciliation. Keep your derived amount too: it exposes price-table mismatches and lets you report an estimate immediately when an invoice-level amount arrives later. For custom, private, or self-hosted models, maintain a custom definition or ingest the owner’s cost because a public price table may not exist.
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Reasoning models require provider usage
Visible output is not a safe proxy for billed output on reasoning models. Langfuse documents that it cannot infer the correct cost for models such as OpenAI o1 when token usage is absent because hidden reasoning tokens also contribute to billed output. Ingest the provider’s token counts for these calls; if they are unavailable, report the amount as unknown or an explicitly bounded estimate rather than presenting a precise number.
Build the event table and rollups
An append-only event table is easier to audit than a table that stores only daily totals. One possible relational shape is:
| Column group | Example columns | Purpose |
|---|---|---|
| Identity | event_id, parent_request_id, trace_id |
Connect retries and secondary calls to one product request |
| Attribution | user_id, feature_key, tenant_id, environment |
Answer product, customer, and deployment questions |
| Provider | provider, model_id, endpoint, service_tier |
Match usage and prices correctly |
| Usage | input_tokens, output_tokens, cached_input_tokens, reasoning_tokens, raw_usage_json |
Preserve all billed categories and future fields |
| Money | provider_cost, derived_cost, price_version, currency |
Separate authoritative and calculated values |
| Quality | usage_status, error_code, started_at, completed_at |
Find missing data, failures, and latency patterns |
From this table, create rollups partitioned by date and grouped by user_id, feature_key, model_id, provider, and environment. Keep counts of complete and incomplete events alongside token and cost sums. A total that excludes unknown events should be labeled as a lower bound, not as final spend.
Useful views
- User view: daily cost, token mix, request count, retry count, and top features.
- Feature view: cost per request, median and high-percentile tokens, model mix, and failure rate.
- Model view: input/output/cached/reasoning shares and estimated versus provider-reported cost.
- Data-quality view: unknown-usage rate, unknown-model count, missing user or feature attribution, and rollup lag.
Alert on spend thresholds and on data-quality failures. A sudden increase in unknown usage can hide a billing problem even when the visible cost chart looks stable.
OpenAI dashboard: useful, but not a product ledger
OpenAI’s Usage Dashboard is a quick audit tool. Authorized organization owners or people granted Usage Dashboard permission can view data in UTC, select projects, and filter Responses and Chat Completions usage by user. The dashboard does not combine organizations, and OpenAI notes that some Scale Tier bundle costs are attributed to the organization rather than to individual projects. For combined or custom reporting, OpenAI points to the Usage API. Details and current scope are in the official dashboard guidance.
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Use the dashboard to compare your application ledger with provider-side totals, investigate a date range, or check whether a project is receiving traffic. It cannot replace application instrumentation when you need a feature key, your own tenant hierarchy, cross-provider joins, or a consistent treatment of retries and background calls.
Three implementation approaches
| Approach | Best fit | Strengths | Limitations |
|---|---|---|---|
| Provider dashboard and response fields | One provider, quick audit, or an early implementation | Low setup; provider usage fields and user filters are available for supported OpenAI endpoints | Project and organization boundaries may not match product features; cross-provider and custom dimensions require your own aggregation |
| Application-owned events and rollups | Custom feature billing, cross-provider reporting, or strict retention requirements | Full control of attribution, retention, raw evidence, pricing logic, alerts, and exports | You must instrument every call, maintain prices, handle missing streams, and operate dashboards and reconciliation |
| LLM observability platform | Teams that want traces and ready-made breakdowns | Trace context, user and tag dimensions, dashboards, alerts, and aggregate APIs can reduce dashboard work | Validate integration coverage, data retention, throughput, privacy controls, pricing tiers, and whether exact provider usage is ingested for every model |
Choose based on the dimensions and accuracy you require, not on the presence of a token chart. A platform that only infers tokens from text may be unsuitable for reasoning models or any provider with hidden billing categories.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Using Langfuse for user and feature breakdowns
Langfuse’s documentation describes three required elements on a generation observation for cost breakdowns: model name, usage details, and optional explicit cost. Business dimensions such as users and features can be represented with tags set when the observation is created. See its trace best-practices documentation for the generation attributes and its token and cost tracking documentation for ingestion and aggregation behavior.
- Create a generation observation around each provider invocation.
- Set the exact model name and attach a stable user identifier and feature tag at creation time.
- Send normalized input, output, and cached-token usage where the provider returns it; preserve reasoning and other categories instead of folding them into a generic total.
- Ingest the provider’s cost when available. Otherwise, associate a model definition with the correct price rules and effective date.
- Use the Metrics API to aggregate by user or tags, then compare those aggregates with your own event ledger or provider dashboard.
Langfuse states that ingested usage and cost take priority when both are supplied. For private prices, custom models, or self-hosted endpoints, add a custom model definition or ingest the amount directly. Its documentation also says inferred cost for a reasoning model is not reliable when the provider does not supply usage because hidden reasoning tokens are not visible in the text.
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Reconcile application data with provider data
Reconciliation catches lost stream terminators, wrong model IDs, duplicate retries, and price-table errors. Run it on a defined UTC window and keep the comparison dimensions identical.
- Export provider usage for the same projects, endpoints, models, and dates.
- Aggregate your event table using provider-compatible categories and UTC boundaries.
- Compare request counts, input tokens, output tokens, cached tokens, and cost separately. A matching total can conceal a category mismatch.
- Investigate unknown events, events created without a user or feature, and model IDs that have no price-table row.
- Record an adjustment or reconciliation reference rather than rewriting the original event.
Do not expect every provider total to map to a customer. Organization-level charges, prepaid bundles, taxes, credits, and other billing categories can sit outside per-request token usage. Label those amounts separately so a product feature report is not mistaken for an invoice.
Privacy, security, and retention decisions
- Use opaque internal user IDs or tenant IDs; avoid email addresses in tags unless there is a documented need and access control.
- Keep prompt and completion content out of cost dimensions. Store content only when required for debugging, with redaction and retention rules.
- Restrict cost dashboards by tenant and role. A support agent should not automatically see another customer’s usage.
- Encrypt raw usage responses and define a deletion or retention policy that matches contractual and regulatory requirements.
- Log who changed a price-table row. A rate edit should not silently rewrite historical amounts.
Troubleshooting common monitoring failures
Every feature shows zero cost
Check that the wrapper receives the feature key before the provider call, that asynchronous jobs propagate context, and that rollups include the environment where traffic runs. A missing dimension should be reported as an attribution error, not grouped into an apparently free feature.
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Look for interrupted streams whose final usage chunk never arrived. Mark them unknown, use provider-side data for reconciliation, and monitor the incomplete-usage rate. Never backfill zero simply to make a chart balance.
Calculated cost disagrees with the provider
Verify the exact model ID, input versus cached-input category, output or reasoning category, service tier, tool charges, and effective price date. Keep the raw response and price version so the discrepancy can be reproduced.
A reasoning model appears unusually cheap
Confirm that reasoning-token usage was returned and stored. If only visible output was captured, the amount is incomplete; obtain provider usage or mark the cost unknown.
Cross-provider totals cannot be compared
Normalize event shape and currency, but do not force unlike categories into one token column. Preserve provider-specific fields and report a common subtotal only when its definition is explicit.
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A practical rollout plan
- Start with one service. Instrument its wrapper and verify that every invocation creates an event, including errors and retries.
- Add attribution tests. In automated tests, assert that known users and feature keys survive synchronous, asynchronous, and streaming paths.
- Store raw and normalized usage. Add an unknown state before enabling cost alerts so missing data cannot be mistaken for zero.
- Version pricing. Load model and category rates with effective dates, then test a known response against the expected calculation.
- Publish two dashboards. One shows product dimensions; the other shows data quality and reconciliation status.
- Expand provider coverage. Add new models only after their usage categories, tool charges, and private-price rules have a defined mapping.
Once this foundation is in place, per-user and per-feature reporting becomes a query over trustworthy events rather than a guess based on visible text or a provider-wide total.
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