Claude Platform Docs

Obtenir l'utilisation de jetons au fil du temps

GET/v1/organizations/analytics/usage_report

Obtenir l'utilisation de jetons au fil du temps sur une plage de dates.

Renvoie l'utilisation de jetons regroupée par minute, heure ou jour, éventuellement ventilée par produit, modèle, fenêtre de contexte, région d'inférence ou vitesse. Disponible pour les organisations disposant d'un forfait Claude Enterprise. Nécessite une clé API avec la portée read:analytics.

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"
claude_tag_categories: optional array of "dm" or "engaged" or "monitoring" or 2 more

Filter to Claude Tag (Claude in Slack) usage in specific spend categories. Usage with no category never matches. dm usage is reported under the user's product rather than claude-tag, so combining this filter with products[]=claude-tag excludes it. Use group_by[]=claude_tag_category to break out per-category values.

maxItems100
One of the following:
"dm"
"engaged"
"monitoring"
"proactive"
"scheduled"
claude_tag_user_ids: optional array of string

Filter to Claude Tag (Claude in Slack) usage attributed to specific Slack users, by Slack user ID (for example U0123ABCDEF), not claude.ai user ID. Usage that is not Claude Tag, and Claude Tag usage not attributed to a single user, never matches. Use group_by[]=claude_tag_user_id to break out per-user values.

maxItems100
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 "claude_tag_category" or "claude_tag_user_id" or "context_window" or 6 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:
"claude_tag_category"
"claude_tag_user_id"
"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 "chat" or "claude-tag" or "claude_code" or 4 more

Product surfaces to include. Defaults to all products. Use group_by[]=product to break out per-product values.

maxItems100
One of the following:
"chat"
"claude-tag"
"claude_code"
"claude_design"
"claude_in_chrome"
"cowork"
"office_agent"
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 }
Obtenir l'utilisation de jetons au fil du temps
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"
Returns Examples
Response 200
{
  "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,
          "claude_tag_category": "dm",
          "claude_tag_user_id": "U0123ABCDEF",
          "context_window": "0-200k",
          "inference_geo": "global",
          "model": "claude-opus-5",
          "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"
}