Overview
AI Costs in Cloud Cost Management gives FinOps and engineering teams a unified destination for analyzing AI spend across providers, including Amazon Bedrock, Anthropic, Google Gemini, OpenAI, Vertex AI, GitHub Copilot, and Cursor. View total AI spend alongside your existing cloud infrastructure costs, analyze it with normalized tags, track cost anomalies, and attribute usage to the specific users and API keys driving it.
Prerequisites
To use AI Costs, you must have at least one of the following supported providers set up for Cloud Cost Management:
AI cost summary
After connecting your AI providers, navigate to Cloud Cost > Summarize > AI to view the AI cost summary page.
The AI cost summary page provides:
- Total AI Cost: Aggregated AI cost and cost change over the selected time frame.
- Daily AI Cost: Daily cost trends across the selected providers over the selected time frame. Use the Filter to dropdown to define which providers appear in the graph.
- Top Cost Drivers: The models, projects, services, and users generating the most spend.
- Active AI Cost Anomalies: Cost anomalies surfaced proactively across all connected providers. Select an anomaly to open a side panel with more details and options for further action.
- AI Cost Dashboards: Out-of-the-box dashboard templates for each supported provider, combining cost data with usage signals such as token consumption, model distribution, and user analytics.
AI cost data from all supported providers is normalized to a consistent set of tags. Use these tags to filter, group, compare, and plan AI spend across dashboards, monitors, budgets, forecasts, and other Datadog tools. Use Cloud Cost Explorer to query and compare spend across providers without writing per-provider logic.
The following tags are available for all supported AI providers:
| Tag name | Tag description |
|---|
providername | The AI provider. |
model | The AI model identifier (for example, claude-opus-4-6, gpt-4.1). |
model_name | The human-readable model name (for example, Claude Opus 4.6). |
token_direction | Whether tokens are being consumed (input) or generated (output) within a service or application. |
token_category | The specific category of tokens consumed, such as input tokens, output tokens, or tokens related to caching and search operations (for example, cached input, cache write, standard input, output). |
project | The project, workspace, or environment the AI costs belong to. |
Attribute AI spend to sources
Out-of-the-box (OOTB) allocation rules use Datadog observability data to attribute AI costs to the users, API keys, and other sources that generated them. OOTB allocation rules require no configuration and are available for Anthropic and OpenAI.
The following tags are available through OOTB allocation rules:
api_key_idapi_key_namecontext_windowmodelmodel_idorg_idorg_nameservice_tieruser_emailuser_iduser_nameworkspace_idworkspace_name
account_idaccount_nameapi_key_idbatchendpointmodelorg_idproject_idproject_nameuser_emailuser_id
Configure Tag Pipelines to map OOTB tags (such as user_email) to teams, services, or business units for aggregate reporting:
After mapping, attributed spend appears in provider-specific dashboards and Cost Reports:
Further reading
Additional helpful documentation, links, and articles:
