How to Track LLM Costs Using Vertex AI Request Labels (2026 Guide)

The Problem Vertex AI Request Labels Solves

When you operate enterprise-level LLM services, you often face a significant visibility challenge regarding cloud expenditure. If you host multiple business lines or client-facing bots within a single Google Cloud Platform (GCP) project, the billing dashboard aggregates all Gemini API usage into one lump sum. Finance and operations teams are frequently left unable to answer the question: "Which specific user or department is driving these costs?"

This issue affects cloud architects and DevOps engineers who are constrained by architectural requirements to keep resources within a single project. Because GCP billing reports traditionally only group costs by Project, Service, or SKU, individual API keys or internal identifiers are invisible to the billing engine. This lack of granularity makes chargeback models or budget monitoring for specific internal teams nearly impossible.

Vertex AI Request Labels fixes this by allowing you to pass custom key-value pairs directly in the request payload of your API calls. By tagging requests at the source, you can group your billing reports by these custom labels, effectively creating a multi-tenant cost attribution system without the overhead of managing dozens of separate GCP projects. In this tutorial, you will learn exactly how to use Vertex AI Request Labels — step by step.

How to Get Started with Vertex AI Request Labels in 5 Minutes

1. Ensure your application is using the Vertex AI SDK rather than the Google AI Studio API keys, as labels are not supported by the latter.
2. Identify the specific business units, clients, or services you need to track for your cost allocation reports.
3. Update your Python environment to use the latest version of the google-genai SDK to ensure compatibility with the GenerateContentConfig object.
4. Refactor your API call code to include a labels dictionary within the GenerateContentConfig parameter.
5. Deploy your changes and wait for the next billing cycle to filter your GCP Billing reports by your custom label keys.

How to Use Vertex AI Request Labels: Complete Tutorial

Step 1: Auditing Your Existing API Architecture

Before you begin coding, you must identify every location in your codebase where Gemini is invoked. It is critical to distinguish between calls made via Vertex AI and those made via Google AI Studio API keys. The labels parameter is exclusive to the Vertex AI platform; attempting to pass it to an AI Studio API key will result in an SDK error.

Scan your repository for generate_content calls and verify the initialization of your client. If your code uses vertexai=False or relies on external API keys, you will need to migrate those specific modules to the Vertex AI SDK before you can implement labels. Use a simple search tool or IDE reference check to map out every file that requires modification.

Step 2: Implementing Labels with GenerateContentConfig

Once you have identified your target files, you need to modify the generate_content method. If your existing code already utilizes a GenerateContentConfig object, you simply need to add the labels argument. The labels should be defined as a dictionary of string key-value pairs, such as {"client_id": "marketing_team"}.

If your current implementation does not use a config object, you must instantiate one. This involves importing the types module from the SDK and passing the config object into the generate_content call. This structure ensures that your metadata is correctly serialized and passed to the GCP billing backend.

Step 3: Verifying and Reporting in GCP Billing

After deploying your updated code, allow time for the API calls to process. Once the usage data has been ingested by the billing system, navigate to the Google Cloud Console and open the "Billing" section. Access your "Reports" and look for the "Group by" filter.

You should now see your custom label keys (e.g., client_id) available as grouping dimensions. Selecting these will allow you to break down your Gemini API costs by the specific values you assigned in your code. This provides the granular visibility required for accurate internal chargebacks or budget tracking.

Vertex AI Request Labels: Pros & Cons

Vertex AI Request Labels Pricing: Free vs Paid

There is no separate pricing for Vertex AI Request Labels. The feature is a native capability of the Vertex AI platform, meaning you do not pay extra to include these metadata tags in your requests. You continue to pay the standard usage-based rates for the Gemini models you consume.

Because there is no "free" or "paid" tier for this specific feature, it is available to all users of the Vertex AI platform. The cost is entirely tied to your model consumption, not the metadata tagging itself. Always verify the latest pricing and terms on the official website to ensure you are aware of any changes to model-specific costs.

Who is Vertex AI Request Labels Best For?

For FinOps teams: This tool provides the necessary data to enforce accountability across departments, allowing you to generate accurate chargeback reports without needing to manage complex multi-project infrastructure.

For Cloud Architects: It allows you to maintain a clean, centralized GCP project structure while still providing the logical separation required for multi-tenant applications or diverse internal business lines.

For DevOps Engineers: It offers a programmatic way to inject metadata into your CI/CD pipelines, ensuring that every API call is tagged consistently at the source, which simplifies monitoring and auditing tasks.

Who Should Not Use Vertex AI Request Labels?

If your application is small, has a single owner, or does not require cost attribution, implementing Request Labels is unnecessary overhead. The refactoring process requires time and testing; if you do not have a business requirement to split costs, the added complexity in your codebase provides no tangible benefit.

Additionally, if you are heavily reliant on Google AI Studio API keys for rapid prototyping or simple scripts, this tool is not for you. You would need to migrate your entire authentication and call structure to the Vertex AI SDK first. If you are not prepared to perform this migration, you should stick to your current setup and accept the lack of granular billing data.

Alternatives to Vertex AI Request Labels

One common alternative is creating separate GCP projects for each business unit, which provides perfect billing isolation but increases management complexity. Another approach is using custom logging and middleware to track usage locally, though this requires building and maintaining your own data pipeline. Vertex AI Request Labels is generally the preferred choice for those already within the Google Cloud ecosystem because it leverages native billing tools rather than requiring external, custom-built solutions.

How We Evaluated Vertex AI Request Labels

This tutorial was developed by analyzing official Google Cloud documentation, product launch announcements, and technical implementation guides. We focused on the architectural requirements and the specific SDK methods necessary to enable the feature. This content is intended to provide an objective, expert-level overview based on the current capabilities of the Vertex AI platform as of July 2026.

Final Verdict: Is Vertex AI Request Labels Worth It?

For any organization struggling with cost visibility in a shared GCP project, this is an essential implementation. While it requires a one-time refactoring effort, the long-term benefit of accurate, automated billing reports is well worth the investment.