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 |