> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fastino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy a training checkpoint

> Deploy a saved Fastino training checkpoint to hosted inference.

`POST /v1/training-jobs/{job_id}/checkpoints/{checkpoint_id}/deploy`

Deploys one deployable checkpoint to the hosted multi-model inference endpoint.

## Request

<ParamField header="X-API-Key" type="string" required>
  Your Fastino API key.
</ParamField>

<ParamField path="job_id" type="string" required>
  Training-job UUID.
</ParamField>

<ParamField path="checkpoint_id" type="string" required>
  Checkpoint ID returned by the checkpoint-list endpoint.
</ParamField>

<RequestExample>
  ```bash cURL theme={null}
  curl -X POST \
    https://api.fastino.ai/v1/training-jobs/YOUR_TRAINING_JOB_ID/checkpoints/CHECKPOINT_ID/deploy \
    -H "X-API-Key: $FASTINO_API_KEY"
  ```
</RequestExample>

## Response

<ResponseExample>
  ```json Response theme={null}
  {
    "success": true,
    "message": "Checkpoint deployed",
    "job_id": "YOUR_TRAINING_JOB_ID",
    "checkpoint_id": "CHECKPOINT_ID",
    "mme_path": "PROVIDER_MODEL_PATH"
  }
  ```
</ResponseExample>

An unknown job or checkpoint returns `404`. A checkpoint that is not deployable returns a request-specific `4xx` error.