Your LLM bill shows total spend, but where is that money actually going? Without cost attribution, you are flying blind — unable to identify inefficiencies, allocate costs fairly, or make informed decisions about optimization.

Why Cost Attribution Matters

Cost attribution answers critical questions:

For SaaS companies: How much does each customer cost to serve? Are you profitable per customer?
For product teams: Which features consume the most AI resources? Where should you optimize?
For finance: How do you allocate AI costs across departments? How do you forecast spend?

Attribution Dimensions

There are several ways to slice your LLM costs. Choose the dimensions that matter most for your business:

By Customer

Track costs per customer or account. Essential for SaaS businesses to understand per-customer profitability and identify heavy users.

Example: Customer A costs $50/month, Customer B costs $500/month

By Feature

Attribute costs to specific product features. Helps prioritize optimization efforts and make build vs. buy decisions.

Example: Chat feature: 40%, Document analysis: 35%, Search: 25%

By Team/Department

Allocate costs to internal teams for budgeting and chargeback. Common in enterprises with shared AI infrastructure.

Example: Engineering: 60%, Marketing: 25%, Support: 15%

By Environment

Separate production costs from development and testing. Ensures dev/test does not unexpectedly inflate your bill.

Example: Production: 80%, Staging: 15%, Development: 5%

Implementing Cost Attribution

Step 1: Define Your Tagging Strategy

Before writing any code, decide what you need to track. Common tags include:

• customer_id — The paying customer
• user_id — The individual user (within a customer)
• feature — Which product feature made the request
• environment — prod, staging, dev
• team — Internal team ownership

Step 2: Add Tags to Every Request

Include attribution metadata with each LLM API call. This can be done through:

• Request headers — Add custom headers with attribution data
• Request metadata — Use provider-specific metadata fields
• Proxy/gateway — Route through a service that adds attribution

Tip: Start Simple

You do not need to track everything from day one. Start with customer_id and feature, then add more dimensions as your needs evolve.

Step 3: Aggregate and Analyze

Collect tagged request data and aggregate by your chosen dimensions. Key metrics to track:

• Total cost per dimension (customer, feature, etc.)
• Request count per dimension
• Average cost per request by dimension
• Trends over time — Is cost per customer increasing?

Common Pitfalls

Incomplete tagging

If some requests are not tagged, your attribution data will be incomplete. Set up alerts for untagged requests.

Over-complicated tagging

Too many dimensions make analysis difficult. Focus on 3-5 key dimensions that directly inform business decisions.

Ignoring shared costs

Some requests benefit multiple customers (e.g., a cached response). Decide how to allocate these shared costs fairly.

Key Takeaways

You cannot optimize what you cannot measure — Attribution is the foundation of cost management
Choose dimensions that matter — Customer, feature, team, environment
Tag every request — Incomplete data leads to bad decisions
Start simple, iterate — Begin with 2-3 dimensions and expand

LLM Cost Attribution: How to Track and Allocate AI Spending