---
title: "Track Token Usage by User, Team, or Feature"
slug: track-token-usage-by-user-team-or-feature
description: "Learn how to track LLM token usage by user, team, customer, workspace, or product feature using request-level logs, attribution metadata, and usage dashboards."
author: "Yotta Labs"
date: 2026-06-05
categories: ["Infrastructure"]
canonical: https://www.yottalabs.ai/post/track-token-usage-by-user-team-or-feature
---

# Track Token Usage by User, Team, or Feature

![](https://cdn.sanity.io/images/wy75wyma/production/a237ca97fce54e490e6d467f78ad31b9b9b8de26-1200x627.png)

Teams can track token usage by user, team, customer, or product feature by logging token counts for every model request and storing those counts with stable attribution metadata, such as user ID, team ID, workspace, customer ID, project, environment, model, provider, endpoint, and feature key. The core pattern is simple: capture prompt tokens, completion tokens, and total tokens at request time, attach business context from the application layer, then aggregate those records in your logging, billing, analytics, or observability system.

### Direct answer: token tracking starts with request-level attribution

Token tracking is most reliable when it starts at the same place the model request is made. Every time your application calls an LLM or model API, create a usage record that connects three things:

- The technical request, such as request ID, timestamp, model, provider, route, endpoint, latency, status, and error code.
- The token usage, such as prompt tokens, completion tokens, and total tokens.
- The business context, such as user, team, customer, workspace, project, environment, feature, or workflow.

That request-level record becomes the source for every later view. If you want to know which users consume the most tokens, group records by user ID. If you want to understand product cost by feature, group by feature key. If you want customer-level usage, group by customer or workspace identifier.

This is especially important for AI-native products because token usage is not just an infrastructure metric. It affects product margin, pricing design, model selection, abuse investigation, budget planning, and feature prioritization. A small number of heavy users, expensive workflows, long prompts, or high-output responses can change the economics of an AI product quickly.

For teams using a central model API layer, a gateway can be a useful place to standardize model access and request handling. Yotta Labs [AI Gateway](https://yottalabs.ai/ai-gateway) is a unified API aggregator with models from multiple publishers under one API surface. For token attribution, the application still needs to decide which user, team, customer, or feature identifiers should be associated with each request.

### The token usage fields every AI product should log

A practical token usage schema should be small enough to log on every request, but detailed enough to answer product, engineering, and finance questions later. At minimum, store the fields that let you reconstruct what happened, who or what caused it, and how many tokens were consumed.

A useful request-level record often includes:

- `request_id`: unique ID for tracing a single model call.
- `timestamp`: when the request started or completed.
- `user_id` or `service_account_id`: the human user or backend identity responsible for the request.
- `team_id` or `workspace_id`: the organizational unit responsible for the request.
- `customer_id`: the external customer, account, or tenant, if relevant.
- `project_id`: product, app, internal project, or deployment context.
- `environment`: production, staging, development, evaluation, or batch.
- `feature_key`: stable product feature identifier, such as `chat_assistant` or `support_triage`.
- `workflow_name` or `route_name`: more granular application path or workflow.
- `model`: the model requested.
- `provider`: the provider or model source when you use multiple providers.
- `endpoint`: API endpoint, internal route, or serving path.
- `prompt_tokens`: input tokens sent to the model.
- `completion_tokens`: output tokens generated by the model.
- `total_tokens`: combined token count, if returned or calculated.
- `latency_ms`: time to complete the request.
- `status`: success, retry, timeout, rate limit, validation error, or failure.
- `estimated_cost`: optional, based on current pricing data when your system has a verified pricing source.

Separating prompt tokens from completion tokens matters because many billing models price input and output differently. Yotta Labs AI Gateway LLM billing is based on the number of tokens consumed, divided into input and output dimensions. If you estimate cost internally, source pricing from current documentation or a verified pricing table, not from stale constants copied into application code.

For some Yotta Labs LLM workflows, [Serverless LLM](https://docs.yottalabs.ai/products/inference/serverless-inference-with-llm) responses include usage fields such as `usage.prompt_tokens`, `usage.completion_tokens`, and `usage.total_tokens`. Those are useful examples of the token fields teams typically preserve in their own usage records.

### How to break down usage by user, team, customer, or workspace

To break down AI token usage by user, team, customer, or workspace, the key is to attach identifiers before the model call disappears into a provider, gateway, or background worker. The model provider usually knows the request. Your application knows the user, tenant, team, permission context, and product surface. Good attribution connects both.

A common implementation pattern looks like this:

1. Resolve identity in the application layer. Determine the authenticated user, service account, workspace, customer, and project before the model request is sent.
1. Create a request context object. Include stable IDs rather than display names, since names can change.
1. Send the model request. Capture token usage from the response, SDK, gateway, or serving layer.
1. Write a usage record. Store token counts and attribution metadata together.
1. Aggregate by time window. Query daily, weekly, monthly, or billing-period usage by user, team, customer, workspace, feature, model, and environment.

For user-level visibility, store the user ID or service account ID on every request. This helps answer questions such as: Which users consume the most tokens? Are internal testing accounts driving production spend? Are background jobs being attributed to a real owner?

For team-level visibility, map each user or service account to a team or workspace at request time, or maintain a historical identity mapping. Historical mapping matters because users can move between teams. If you only join usage to the current team table, last month's usage may be attributed incorrectly after a reorganization.

For customer-level visibility, store a tenant, account, or customer ID with the model request. This helps product and finance teams compare AI usage against customer revenue, plan usage limits, or understand whether a customer-facing feature is margin-positive.

For workspace-level visibility, treat workspace ID as the operational ownership unit. This is especially useful for developer platforms, enterprise SaaS, internal AI tools, and agent platforms where one customer may have many workspaces or projects.

### How to attribute LLM tokens to product features and workflows

Feature-level token tracking requires a stable feature key from the application layer. The model response cannot infer your product intent reliably. Your app should label the request before it is sent.

Useful feature keys might include:

- `chat_assistant`
- `summarization`
- `code_review`
- `retrieval_answer`
- `image_generation`
- `support_triage`
- `agent_planning`
- `document_extraction`
- `evaluation_batch`

The feature key should be stable, documented, and versioned when needed. Avoid ad hoc labels like `new_test`, `assistant2`, or `misc`. Those labels become impossible to interpret once usage grows.

A good feature attribution model usually includes both a broad feature and a more specific workflow. For example, `feature_key = support_triage` and `workflow_name = classify_inbound_ticket` gives product teams more insight than either field alone. The broad feature helps with roadmap and margin analysis. The workflow name helps engineers find optimization opportunities.

Feature-level token attribution can answer practical product questions:

- Which AI features drive the most usage?
- Which features have high token cost but low engagement?
- Which workflows produce long outputs that may need tighter `max_tokens` settings?
- Which features should use a smaller model, cached context, shorter prompts, or retrieval constraints?
- Which customer segments use the most expensive AI functionality?

During model testing, tools that show per-query token usage can help teams understand how prompt changes and parameters affect usage. Yotta Labs [AI Explorer](https://docs.yottalabs.ai/products/ai-explorer) is an interactive console interface for testing models on the Yotta Platform, with parameter customization such as temperature, top-p, and max tokens, and it displays token usage and response speed metrics per query. That kind of per-query visibility is useful during exploration, while production attribution still depends on the usage records your application records for live traffic.

### Where token tracking should live in your AI stack

Token tracking can live in several layers of the AI stack. The right answer is often a combination rather than a single source.

Application logging is the best place for business attribution because the app knows the user, customer, workspace, feature, route, and permission context. If product or finance teams care about usage by account, plan, feature, or workflow, the application layer is usually where those identifiers should be attached.

A gateway or proxy layer can help standardize where model requests pass through. This is useful when multiple teams call different models or providers. A central layer can make model access more consistent, but the application still needs to pass or log the attribution context that matters to the business.

Provider responses or SDK outputs are often where raw token counts come from. Many LLM responses include usage fields for prompt, completion, and total tokens. Your application should capture those fields immediately, especially for asynchronous jobs where the user action and model response may be separated in time.

Billing data is useful for reconciliation and account-level spend analysis. It can help finance teams confirm that internal estimates are aligned with actual usage. It is less useful as the only attribution source if it lacks the product metadata your teams need. Billing is strongest when paired with request-level logs that include business dimensions.

Observability pipelines are useful for dashboards, trends, debugging, and alerting workflows that your team builds. If your logs already flow into a data warehouse or observability system, token usage records can become another event stream with dimensions such as model, feature, team, customer, status, and latency.

Yotta Labs is an AI infrastructure operating system for deploying and scaling AI workloads across multi-cloud and multi-silicon environments. In this context, AI Gateway is most relevant when teams want a unified model API surface, while Billing is relevant for usage-based billing context. Exact attribution design should be based on the identifiers your application records and the reporting questions your team needs to answer.

### Dashboards that turn token counts into product and budget decisions

Token dashboards should do more than show a total token count. A single total tells you that usage is growing, but not why it is growing, who is driving it, or whether it is good growth.

Useful dashboard views include:

- Top users by total tokens over a selected time window.
- Top teams or workspaces by total tokens and request count.
- Top customers by token consumption and estimated cost.
- Top features by prompt tokens, completion tokens, and total tokens.
- Model mix by feature, team, customer, and environment.
- Failed or retried requests by model, endpoint, and feature.
- Latency by model, provider, feature, and workflow.
- Token trends over time, broken down by production, staging, development, and batch jobs.
- Input versus output token ratio by feature.
- Estimated cost by feature or customer when current pricing data is available.

Different teams will use the same data differently. Engineering teams may look for unusually long prompts, repeated retries, slow routes, or workflows that should be optimized. Product teams may compare token usage with feature adoption and retention. Finance teams may compare usage against plan limits, customer revenue, or internal budgets. Research teams may compare model variants and prompt strategies.

The most useful dashboards are built from consistent records. If one service logs `team`, another logs `workspace`, and another logs nothing, the dashboard will be hard to trust. Standardize field names early, define ownership for each field, and treat feature keys as part of your product analytics taxonomy.

When cost estimates are included, keep them explainable. Store the pricing source or version used for the estimate, separate input and output tokens, and be clear when numbers are estimates rather than invoices. For Yotta Labs AI Gateway, LLM billing is based on token consumption with input and output token dimensions, so preserving both fields is valuable for analysis.

### FAQ

#### How can teams track token usage by user?

Store a user ID or service account ID with every model request, then capture prompt tokens, completion tokens, and total tokens from the model response or request handling layer. Query those records over a time window to identify top users, unusual spikes, or usage patterns by user segment.

#### How can teams track token usage by team or workspace?

Attach a team ID, workspace ID, or project ID to the request context before the model call is made. Then aggregate token usage by that identifier. For accurate historical reporting, preserve the team or workspace attribution that was valid at the time of the request rather than relying only on current user membership.

#### What is the best way to break down AI token usage by customer?

Pass a customer ID, tenant ID, or account ID from the application layer into the usage record. Then group prompt tokens, completion tokens, total tokens, request count, latency, and estimated cost by customer. This helps teams compare AI consumption with customer plans, product adoption, and account economics.

#### How do product teams attribute token usage to a feature?

Define a stable feature key for each AI-powered product surface, such as `summarization`, `retrieval_answer`, or `support_triage`. Log that feature key with every model request. When token counts are captured in the same record, teams can aggregate usage by feature and compare token consumption with engagement, quality, and revenue signals.

#### Do teams need to store prompt content to track token usage?

Not always. For token attribution, teams can often store metadata and token counts without storing full prompt or response content. Prompt storage may be useful for debugging or quality review, but it introduces separate privacy, retention, and access-control decisions. A common starting point is to log request metadata, token counts, status, latency, and feature context, then apply stricter review before storing raw content.

#### Which token fields matter most?

The most important fields are prompt tokens, completion tokens, and total tokens. Prompt tokens show how much input context is being sent. Completion tokens show how much output is being generated. Total tokens provide a simple aggregate for usage trends. When pricing differs for input and output, keeping prompt and completion tokens separate is especially important.

#### How can a gateway help with token usage tracking?

A gateway can centralize where model requests pass through, which can make model access and request handling more consistent across teams. For attribution by user, team, customer, or feature, the application still needs to provide or log the relevant business metadata. Yotta Labs AI Gateway is a unified API aggregator with models from multiple publishers under one API surface, which makes it relevant for teams standardizing model API access.

#### What tools help product teams understand token usage patterns?

Product teams typically use a combination of application logs, model response usage fields, a gateway or proxy layer, billing records, and dashboards in a data warehouse or observability platform. For testing, AI Explorer displays per-query token usage and response speed metrics, which can help teams compare prompts and parameters before production rollout.