Capacity

Check budget allocations and remaining GPU hours

Get Capacity

Retrieve capacity budget and allocation information for your organization.

GET /v1/capacity

Query Parameters

Parameter	Type	Description
initiative_id	string	Filter to specific team
pool_id	string	Filter to specific capacity pool

Example Request

curl -X GET "https://api.usechamber.io/v1/capacity" \
  -H "Authorization: Bearer <token>" \
  -H "X-Organization-Id: <org-id>"

Example Response

{
  "data": {
    "organization_id": "org_abc123",
    "summary": {
      "total_allocated_gpu_hours": 10000,
      "total_used_gpu_hours": 6500,
      "total_remaining_gpu_hours": 3500,
      "current_active_gpus": 24
    },
    "pools": [
      {
        "pool_id": "pool_prod",
        "name": "Production Pool",
        "allocated_gpu_hours": 6000,
        "used_gpu_hours": 4200,
        "remaining_gpu_hours": 1800,
        "instance_types": ["p4d.24xlarge", "p5.48xlarge"]
      },
      {
        "pool_id": "pool_dev",
        "name": "Development Pool",
        "allocated_gpu_hours": 4000,
        "used_gpu_hours": 2300,
        "remaining_gpu_hours": 1700,
        "instance_types": ["g5.xlarge", "g5.2xlarge"]
      }
    ],
    "initiatives": [
      {
        "initiative_id": "init_ml_team",
        "name": "ML Platform Team",
        "allocated_gpu_hours": 5000,
        "used_gpu_hours": 3200,
        "remaining_gpu_hours": 1800
      },
      {
        "initiative_id": "init_research",
        "name": "Research Team",
        "allocated_gpu_hours": 5000,
        "used_gpu_hours": 3300,
        "remaining_gpu_hours": 1700
      }
    ]
  },
  "request_id": "req_abc123"
}

Response Fields

Field	Description
total_allocated_gpu_hours	Total GPU hours allocated to the organization
total_used_gpu_hours	GPU hours consumed across all workloads
total_remaining_gpu_hours	Available GPU hours remaining
current_active_gpus	Number of GPUs currently in use
pools	Breakdown by capacity pool
initiatives	Breakdown by team