Custom metadata labels

You can add custom metadata to generateContent and streamGenerateContent API calls by using labels. This page explains what labels are, and shows you how to use them to break down your billed charges.

What are labels?

A label is a key-value pair that you can assign to generateContent and streamGenerateContent API calls. They help you organize these calls and manage your costs at scale, with the granularity you need. You can attach a label to each call, then filter the calls based on their labels. Information about labels is forwarded to the billing system that lets you break down your billed charges by label. With built-in billing reports, you can filter and group costs by labels. You can also use labels to query billing data exports. For information on how to use labels after creation, see an example from the labels overview.

Requirements for labels

The labels applied to an API call must meet the following requirements:

  • Each API call can have up to 64 labels.
  • Each label must be a key-value pair.
  • Keys have a minimum length of 1 character and a maximum length of 63 characters, and cannot be empty. Values can be empty, and have a maximum length of 63 characters.
  • Keys and values can contain only lowercase letters, numeric characters, underscores, and dashes. All characters must use UTF-8 encoding, and international characters are allowed. Keys must start with a lowercase letter or international character.
  • The key portion of a label must be unique within a single API call. However, you can use the same key with multiple calls.

These limits apply to the key and value for each label, and to the individual API call that have labels. There is no limit on how many labels you can apply across all API calls within a project.

Common uses of labels

Here are some common use cases for labels:

  • Team or cost center labels: Add labels based on team or cost center to distinguish API calls owned by different teams (for example, team:research and team:analytics). You can use this type of label for cost accounting or budgeting.

  • Component labels: For example, component:redis, component:frontend, component:ingest, and component:dashboard.

  • Environment or stage labels: For example, environment:production and environment:test.

  • Ownership labels: Used to identify the teams that are responsible for operations, for example: team:shopping-cart.

We don't recommend creating large numbers of unique labels, such as for timestamps or individual values for every API call. The problem with this approach is that when the values change frequently or with keys that clutter the catalog, this makes it difficult to effectively filter and report on API calls.

Add a label to an API call

To add a label to a generateContent or streamGenerateContent API call, do the following:

REST

Before using any of the request data, make the following replacements:

  • GENERATE_RESPONSE_METHOD: The type of response that you want the model to generate. Choose a method that generates how you want the model's response to be returned:
    • streamGenerateContent: The response is streamed as it's being generated to reduce the perception of latency to a human audience.
    • generateContent: The response is returned after it's fully generated.
  • LOCATION: The region to process the request. Available options include the following:

    Click to expand a partial list of available regions

    • us-central1
    • us-west4
    • northamerica-northeast1
    • us-east4
    • us-west1
    • asia-northeast3
    • asia-southeast1
    • asia-northeast1
  • PROJECT_ID: Your project ID.
  • MODEL_ID: The model ID of the model that you want to use.
  • ROLE: The role in a conversation associated with the content. Specifying a role is required even in singleturn use cases. Acceptable values include the following:
    • USER: Specifies content that's sent by you.
    • MODEL: Specifies the model's response.
  • PROMPT_TEXT
     The text instructions to include in the prompt. JSON
  • LABEL_KEY: The label metadata that you want to associate with this API call.
  • LABEL_VALUE: The value of the label.

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

cat > request.json << 'EOF'
{
  "contents": {
    "role": "ROLE",
    "parts": { "text": "PROMPT_TEXT" }
  },
  "labels": {
    "LABEL_KEY": "LABEL_VALUE"
  },
}
EOF

Then execute the following command to send your REST request: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

@'
{
  "contents": {
    "role": "ROLE",
    "parts": { "text": "PROMPT_TEXT" }
  },
  "labels": {
    "LABEL_KEY": "LABEL_VALUE"
  },
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATE_RESPONSE_METHOD" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import vertexai

from vertexai.generative_models import GenerativeModel

# TODO(developer): Update and un-comment below line
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

model = GenerativeModel("gemini-2.0-flash-001")

prompt = "What is Generative AI?"
response = model.generate_content(
    prompt,
    # Example Labels
    labels={
        "team": "research",
        "component": "frontend",
        "environment": "production",
    },
)

print(response.text)
# Example response:
# Generative AI is a type of Artificial Intelligence focused on **creating new content** based on existing data.

Google Cloud products report usage and cost data to Cloud Billing processes at varying intervals. As a result, you might see a delay between your use of Google Cloud services, and the usage and costs being available to view in Cloud Billing. Typically, your costs are available within a day, but can sometimes take more than 24 hours.