Claude Platform Docs

Get Token Usage Over Time

GET/v1/organizations/analytics/usage_report

Get token usage over time across a date range.

Returns token usage bucketed by minute, hour, or day, optionally broken down by product, model, context window, inference region, or speed. Available to organizations on a Claude Enterprise plan. Requires an API key with the read:analytics scope.

Query parameters
starting_at: string

Start of range, inclusive. RFC 3339 tz-aware. Must be within the last 365 days and no earlier than 2026-01-01T00:00:00Z.

formatdate-time
bucket_width: optional "1d" or "1h" or "1m"

Time bucket granularity.

default1d
One of the following:
"1d"
"1h"
"1m"
context_windows: optional array of "0-200k" or "200k-1M"

Filter to specific context-window pricing tiers. Use group_by[]=context_window to break out per-tier values.

maxItems100
One of the following:
"0-200k"
"200k-1M"
ending_at: optional string

End of range, exclusive. When omitted, defaults to the earlier of now and starting_at + 31 days. The range may span at most 31 days.

formatdate-time
group_by: optional array of "context_window" or "inference_geo" or "model" or 4 more

Dimensions to break each time bucket out by. Defaults to no grouping (one total per bucket). Each bucket reports at most its top 100 groups; a group beyond that cap has no row in that bucket (there is no remainder row), so grouped buckets are not exhaustive when a dimension has more than 100 distinct values.

maxItems100
One of the following:
"context_window"
"inference_geo"
"model"
"product"
"rbac_group_id"
"slack_channel_id"
"speed"
inference_geos: optional array of "global" or "not_available" or "us"

Filter to specific inference regions. not_available matches rows where the region is unset. Use group_by[]=inference_geo to break out per-region values.

maxItems100
One of the following:
"global"
"not_available"
"us"
limit: optional number

Maximum number of time buckets per page. Defaults and caps vary by bucket_width (1d: default 7, max 31; 1h: default 24, max 168; 1m: default 60, max 256).

minimum1
models: optional array of string

Models to include. Defaults to all models. Use group_by[]=model to break out per-model values.

maxItems100
page: optional string

Opaque cursor from a previous response's next_page field.

products: optional array of string

Product surfaces to include. Defaults to all products. Use group_by[]=product to break out per-product values. Values include "chat", "claude_code", "cowork", "office_agent", "claude_in_chrome", "claude_design", and "claude-in-slack". "claude-in-slack" (with hyphens) is Claude Tag, the Claude product in Slack. A similarly spelled legacy value (underscores instead of hyphens) identifies the retiring v1 Slack chat bot and appears only for organizations that used it.

maxItems100
rbac_group_ids: optional array of string

Filter to usage attributed to specific RBAC groups. Accepts tagged RBAC group IDs (rbac_group_...) or bare group UUIDs. A row matches when the user belonged to any of the listed groups on the (UTC) day the usage occurred; usage with no group attribution never matches.

maxItems100
slack_channel_ids: optional array of string

Filter to usage originating from specific Slack channels. Use group_by[]=slack_channel_id to break out per-channel values.

maxItems100
speeds: optional array of "fast" or "standard"

Filter to fast or standard inference mode. Use group_by[]=speed to break out per-mode values.

maxItems100
One of the following:
"fast"
"standard"
user_ids: optional array of string

Filter to specific users by tagged user ID.

maxItems100
Returns
UsageBucket object{ data, data_refreshed_at, has_more, 2 more }

Get Token Usage Over Time

cURL
curl https://api.anthropic.com/v1/organizations/analytics/usage_report \
    -H 'anthropic-version: 2023-06-01' \
    -H "X-Api-Key: $ANTHROPIC_ADMIN_API_KEY"
{
  "data": [
    {
      "ending_at": "2019-12-27T18:11:19.117Z",
      "results": [
        {
          "cache_creation": {
            "ephemeral_1h_input_tokens": 1000,
            "ephemeral_5m_input_tokens": 500
          },
          "cache_read_input_tokens": 0,
          "context_window": "0-200k",
          "inference_geo": "global",
          "model": "claude-opus-4-6",
          "output_tokens": 0,
          "product": "chat",
          "rbac_group_id": "rbac_group_012rppKaSVsmTo6NqRDXQXNF",
          "requests": 0,
          "server_tool_use": {
            "web_search_requests": 10
          },
          "slack_channel_id": "C0123ABCDEF",
          "speed": "fast",
          "uncached_input_tokens": 0
        }
      ],
      "starting_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "data_refreshed_at": "2019-12-27T18:11:19.117Z",
  "has_more": true,
  "next_page": "next_page",
  "organization_id": "org_013FP9SaFPBg7Kw7fetjn6cF"
}
Returns Examples
{
  "data": [
    {
      "ending_at": "2019-12-27T18:11:19.117Z",
      "results": [
        {
          "cache_creation": {
            "ephemeral_1h_input_tokens": 1000,
            "ephemeral_5m_input_tokens": 500
          },
          "cache_read_input_tokens": 0,
          "context_window": "0-200k",
          "inference_geo": "global",
          "model": "claude-opus-4-6",
          "output_tokens": 0,
          "product": "chat",
          "rbac_group_id": "rbac_group_012rppKaSVsmTo6NqRDXQXNF",
          "requests": 0,
          "server_tool_use": {
            "web_search_requests": 10
          },
          "slack_channel_id": "C0123ABCDEF",
          "speed": "fast",
          "uncached_input_tokens": 0
        }
      ],
      "starting_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "data_refreshed_at": "2019-12-27T18:11:19.117Z",
  "has_more": true,
  "next_page": "next_page",
  "organization_id": "org_013FP9SaFPBg7Kw7fetjn6cF"
}