The Messages API accepts an optional metadata object on each request. Its documented purpose is attribution: the user_id field carries an identifier for the end user on whose behalf your application is calling, which helps the platform associate traffic with distinct users (useful, for example, in abuse investigations) and helps you tie a request back to a person in your own systems. The exact set of accepted keys is defined in the official API reference — check it before you design a tagging scheme around fields the API may not accept.
The golden rule: opaque identifiers only
Whatever you put in user_id, make it an identifier that means something only inside your organization — an internal UUID, not an email address or a name. This is standard privacy hygiene: you get full traceability in your own logs (where the UUID joins to your user table) without shipping personal data in every API call. The same discipline applies to any tagging surface discussed below.
Metadata is one leg of a three-legged stool
On its own, a tag inside the request body doesn't give you dashboards. Production attribution on Claude typically combines three mechanisms, all of which exist in the platform today:
| Mechanism | What it gives you |
|---|---|
metadata.user_id on requests | Per-end-user attribution, joined against your own logs |
| Workspaces and per-workspace API keys | Hard partitioning by team or environment — batches, files, and spend are scoped to a workspace, and workspaces can carry their own spend and rate limits |
Your own request logging, keyed on the request-id header | Every API response carries a request-id (e.g. req_018EeWyXxfu5pfWkrYcMdjWG); log it alongside your internal trace ID and you can correlate any incident with Anthropic support |
Related surfaces have their own attribution hooks worth knowing. The Message Batches API requires a unique custom_id (1–64 characters) on every request in a batch — that is your join key for mapping batch results back to source records, and it doubles as attribution if you encode a feature or tenant prefix into it. Managed Agents sessions accept a metadata field at creation, so long-running agent work can carry the same tags as your synchronous traffic.
Attribution on the third-party platforms
On Amazon Bedrock, Google Vertex AI, and Microsoft Foundry, the cloud provider's own attribution machinery is usually the stronger tool: cost allocation tags on AWS, labels on Google Cloud, and resource tags on Azure flow directly into each cloud's billing and cost-management tooling, with no join required. The pattern most enterprises land on is layered: cloud-native tags answer "which department pays?", while request-level identifiers in your gateway logs answer "which user, session, and feature?". If you route all LLM traffic through an internal gateway, that gateway is the natural place to stamp both — see the internal AI gateway pattern.
Audit questions this setup answers
With opaque user IDs on requests, workspace separation by team, and request IDs captured in your logs, the common audit and finance questions become queries instead of archaeology: Which users drove usage of feature X last quarter? Was this specific output produced for this specific customer? Which workspace is approaching its spend limit, and who owns it? What exactly did we send Anthropic in request req_… that support asked about? None of these require new infrastructure — only the discipline of tagging from day one.
Where to go next
Cost attribution continues in building a chargeback model and the cost tagging strategy; the logging side is covered in audit logging Claude usage.