Keep the API key as a server-side application credential, but attribute each request through a separate, server-generated usage identity tied to the authenticated user, tenant, workspace, project, or cost center. An API key identifies the calling application or provider account; by itself, it does not identify which end user generated a request. The application therefore needs its own trusted attribution layer and usage ledger.
The Short Answer: Separate Application Authentication from Usage Attribution
A shared API key and an internal usage identity serve different purposes:
- The API key authenticates the application to the model service. It should remain in a protected server-side environment rather than being embedded in a browser, mobile app, or other end-user client.
- The usage identity attributes activity within the application. It should be derived from the application's authenticated and authorized context for each request.
This separation allows one application credential to serve many users without treating everyone behind that credential as a single cost or control domain. It also avoids making a separate provider key for every end user the default architecture, which can create unnecessary credential-management overhead and may not align with the provider's account model.
The application should establish who is making the request before model inference begins. It can then associate that request with the appropriate tenant, workspace, project, or cost center. If the provider accepts suitable metadata, selected identifiers may accompany the upstream request. If it does not, the application can retain the attribution entirely within its internal ledger.
Token Forge Cloud offers Managed Model APIs as an API-first route to model access and usage data for teams validating demand. The specific attribution layer behind a shared application credential should still be designed around the application's own identity, authorization, and reporting requirements.
Create a Server-Generated Usage Identity for Every Request
Do not rely on a tenant ID, cost center, or billing label supplied directly by an untrusted client. A user could alter such a value and assign activity to another organization or budget. Instead, the server should resolve attribution only after authenticating the caller and checking authorization.
A practical process is to:
- Authenticate the user, service account, or workload through the application.
- Determine the tenant and other organizational dimensions that identity is authorized to use.
- Resolve the relevant workspace, project, cost center, and environment from server-controlled records.
- Generate an internal request ID and attach the resolved usage identity to the request lifecycle.
Use a stable pseudonymous subject identifier where possible. An internal opaque ID is generally more appropriate for metering than an email address, personal name, or other unnecessary personal information. Pseudonymization does not by itself address every privacy or security obligation, but it can reduce the amount of directly identifying data included in operational records.
The usage identity can be hierarchical. For example, a request might belong to one tenant, one workspace within that tenant, a particular project, and an authorized user or service account. This structure supports reporting at different levels without allowing the shared provider key to become the source of identity.
What to Record in an Internal Usage Ledger
The internal ledger should preserve enough context to explain who initiated work, what was requested, how it was processed, and which measurement source produced each usage value. Useful fields can include:
- A pseudonymous user or workload ID
- Tenant, workspace, project, and cost center
- Deployment environment, such as development or production
- Internal logical request ID and individual attempt ID
- Provider request ID, when returned
- Model or internal model alias
- Request and completion timestamps
- Input and output units, when available
- Provider-reported usage and internally estimated usage as separate values
- Request status, error category, and latency
- Cache or batch allocation markers when relevant
- Measurement source and record finalization state
Do not treat request count, token count, input and output units, GPU time, latency, and monetary cost as interchangeable. They answer different questions. A product team may care about requests per active user, an engineering team may investigate latency, and a finance team may allocate billed cost across business units.
It is also useful to distinguish a logical request from an execution attempt. One user action can produce several attempts because of retries, fallbacks, or asynchronous processing. Keeping both identifiers makes it possible to analyze reliability while preventing duplicate chargeback entries.
Avoid overwriting estimates when final usage becomes available. Store the source and state of each measurement so reports can distinguish estimated consumption, provider-reported units, invoiced amounts, and internally allocated cost.
Example Flow from an End-User Request to a Usage Record
A vendor-neutral request-to-ledger flow can work as follows:
- Authenticate the caller. The application verifies the user, service account, or workload identity.
- Resolve authorized dimensions. The server identifies the permitted tenant, workspace, project, and cost center rather than trusting arbitrary client values.
- Create internal identifiers. The application creates a logical request ID and an attempt ID before contacting the model service.
- Call the model service. The backend sends the request using the server-held API credential. Attribution remains linked to the request in server-side context.
- Capture the result. The application records status, timing, reported usage, and a provider request ID when those values are available.
- Normalize the event. Provider-specific measurements are converted into a stable internal representation while retaining the original source values.
- Write an idempotent ledger entry. A unique event or attempt key prevents the same completion, callback, or retry result from being counted twice.
For asynchronous jobs, the initial submission may create a pending record. A later completion event can finalize it using the same logical request ID. For streaming responses, the record may remain provisional until the stream closes, fails, or reaches the application's finalization rule.
This is an architectural example, not a provider-specific request schema. Metadata support, response identifiers, token reporting, and usage-finalization behavior differ across model services.
How to Handle Retries, Streaming, Failures, Caching, and Batching
Usage attribution becomes more complex when one logical user action does not map cleanly to one provider request. Define lifecycle and allocation rules before using the data for budgets or chargeback.
Retries and timeouts: Give every attempt its own identifier while retaining the parent logical request ID. Record whether an attempt completed, failed, timed out, or was superseded. Provider billing treatment can vary, so a failed application request should not automatically be assumed to have generated no billable work.
Streaming: Accumulate provisional usage during the stream when possible, but finalize the ledger according to the provider's final usage response or a documented internal estimation rule. Preserve partial-output status if the connection ends early.
Asynchronous work: Separate submission, execution, and completion events. Use idempotency controls so repeated polling, callbacks, or event delivery cannot create duplicate usage records.
Caching: Decide whether a cache hit is allocated according to actual billed usage, the logical consumption avoided, or an internal service rate. These are different views and should be labeled clearly rather than combined into one ambiguous token or cost figure.
Batching: A single execution may combine work from several users or tenants. Allocation might use each item's input and output units, an equal share, measured execution consumption, or another documented rule. No single method is universally correct; the policy should match the purpose of the report.
Maintain raw events alongside normalized and allocated records where operationally appropriate. That separation makes allocation rules easier to revise without losing the original execution history.
Reconcile Provider Billing Without Conflating It with Internal Allocation
Provider accounting answers what the application account was billed or reported. Internal attribution answers which user, tenant, project, or cost center should receive operational or financial responsibility. The totals are related, but they are not necessarily identical at every point in time.
Reconcile detailed internal records with provider invoices or aggregate usage reports on a regular close cycle. Differences can arise from reporting delays, rounding, retry treatment, cached work, batching, tokenization, delayed asynchronous jobs, and differences between estimated and billable usage.
A useful reconciliation model preserves distinct values for:
- Internal estimated consumption
- Provider-reported usage
- Actual invoiced cost when available
- Cost allocated under the organization's chosen policy
Define tolerances and escalation rules appropriate to the reporting purpose. A small timing difference may be acceptable for an operational dashboard but inappropriate for a finalized finance report. A discrepancy also does not automatically mean that either record is wrong; the systems may be measuring different events or applying different finalization schedules.
Observability, security auditing, showback, chargeback, and provider billing should therefore remain separate reporting views. Each can require different dimensions, retention periods, precision, and allocation logic.
Apply Attribution to Quotas, Showback, Chargeback, and Serving-Layer Decisions
Once attribution is trusted, enforce quotas, rate limits, budgets, and alerts against the authenticated internal user or tenant—not only against the shared upstream API key. A key-level limit protects the provider account as a whole, but it cannot prevent one tenant from consuming the capacity intended for others.
The same ledger can support several controlled views:
- Operational observability for request volume, failures, and latency
- Security auditing for tracing activity to an authenticated subject and authorized tenant
- Showback for explaining consumption without transferring cost
- Chargeback for assigning cost under a documented allocation policy
- Capacity planning for understanding demand by workload and environment
Attribution can also inform serving-layer analysis, but identity alone should not dictate technical policy. Latency-sensitive chat, batch enrichment, and agentic workflows present different serving-policy problems. Their routing, caching, batching, quantization, and GPU scheduling decisions may depend on workload behavior, service objectives, and deployment constraints in addition to tenant or user identity.
Token Forge Cloud offers Private LLM Inference for private deployment and serving-layer optimization for enterprise AI workloads. Token Forge Cloud offers Managed Model APIs as an API-first option for model access and demand validation before potential private deployment. In either deployment path, teams should define how their application identities, normalized usage records, allocation policies, and provider accounting will fit together before using the data for financial or operational decisions.
Contact Token Forge Cloud to discuss API access, private deployment, and LLM inference cost control.