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Managed AI Tags are a Vantage-managed tag schema that standardizes AI cost data across providers. As spend flows in from providers such as Cursor, Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and Google Cloud, Vantage maps each provider’s billing and usage fields into a consistent set of vntg:ai: tags. The same keys, like vntg:ai:model, vntg:ai:model_provider, and vntg:ai:token_type, appear regardless of which provider the spend came from, so you can build Cost Reports, Virtual Tags, Budgets, and Cost Alerts on the same dimensions across your entire AI stack. Each AI provider exposes different fields: the model name may be the service on one integration and a tag on another, and user identity may be an email, a user ID, or an API key depending on the vendor.
Managed AI Tags are metadata-only and included with Vantage at no additional cost for accounts with supported AI cost integrations. Vantage only normalizes fields your providers already report in cost and usage data; it does not collect prompt or completion content.

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How It Works

Managed AI Tags are applied automatically during each provider’s cost ingestion. Vantage reads the provider’s billing and usage fields, maps them to the normalized schema, and writes the resulting tags, prefixed with vntg:ai: by default, onto the cost rows. No configuration is required: once you connect a supported AI integration, the tags appear on that provider’s costs and become available anywhere tags are supported in Vantage. Not every provider supplies every field. Vantage only writes a tag when the source data provides a reliable value for that key, so the set of vntg:ai: tags you see depends on which providers you have connected and what each one reports.

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The Managed AI Tag Schema

The following keys make up the canonical Managed AI Tag schema. Each key is shown with the vntg:ai: prefix. Not every provider supplies every field. Vantage only writes a tag when the source data provides a reliable value for that key.
When Custom LLM Enrichment is enabled, Vantage also writes request metadata into the same schema, including vntg:ai:endpoint and vntg:ai:is_batch. These keys come from per-request telemetry rather than native provider billing. See Managed AI Tags vs. Custom LLM Enrichment.
Anthropic also emits vntg:ai:inference_speed (its service speed tier) alongside the canonical keys.

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Supported Providers

Managed AI Tags are emitted automatically for supported AI cost integrations. The keys each provider populates depend on the fields that provider reports.
For the hyperscalers, Managed AI Tags apply only to the AI portion of spend: Amazon Bedrock cost rows on AWS, Azure OpenAI and Foundry cost rows on Azure, and Vertex AI Gemini and Marketplace Claude cost rows on Google Cloud. Non-AI costs on those providers are unaffected.

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How Managed AI Tags Coexist with Provider Tags

Managed AI Tags add a cross-provider layer on top of the provider-specific tags Vantage already creates. Provider-specific native tags, such as cursor:user, openai:api_key_id, or anthropic:model, continue to exist and capture fields unique to that integration, while the managed vntg:ai: keys give you a single, consistent dimension that works across providers. Managed AI Tags are also additive to your existing configuration: they do not modify your Virtual Tags, Billing Rules, or reports. You can start using them in new reports and filters immediately alongside everything you already have.

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Renaming or Standardizing Managed AI Tags

Vantage maintains the canonical Managed AI Tag definitions with the set vntg:ai: prefix so the schema stays consistent across every account. If you want to present these tags under different key names, use Tag Key Collapsing to map one or more vntg:ai: keys into a Virtual Tag with your preferred naming.

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Backfill and Data Freshness

Managed AI Tags are applied as part of each provider’s cost ingestion, so they follow that provider’s refresh cadence. See the provider data refresh documentation for per-provider timing. Normalized vntg:ai: tags are available from August 2026 onward. Vantage does not backfill managed tags onto earlier historical data.

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Use Managed AI Tags in Cost Reports

Once a supported AI integration is connected and its costs are processed, the vntg:ai: keys appear in the Tag Key dropdown wherever tags are supported. To analyze AI spend with them:
  • To group: open the Group By menu on a Cost Report, select Tag, and choose a key such as vntg:ai:model (values like gpt-4o) or vntg:ai:model_provider.
  • To filter: open the Filters menu, add a new rule, select Tag, choose the Tag Key (for example, vntg:ai:user_email), then pick an operator and one or more values.
Because the same keys span every AI provider, you can drill into a single developer’s spend across Cursor and Anthropic in one view, or compare model mix across your whole AI stack. Managed AI Tags also work anywhere else tags are supported, including Virtual Tags, cost allocation segments, Budgets, and Cost Alerts. If you use a handful of keys often, mark them as preferred tags so they sort to the top of these menus.

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Managed AI Tags vs. Custom LLM Enrichment

Managed AI Tags and Custom LLM Enrichment are complementary features that share the vntg:ai: vocabulary but solve different problems. In short, Managed AI Tags standardize what providers already send, while Custom LLM Enrichment enriches what providers leave out. Use them together for the most complete view of AI spend.