Deployments

Create, call, pause, and resume a model deployment in Pictograph. Per-deployment bearer-token auth, one /predict endpoint, billed by uptime. SDK and CLI.

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A deployment turns a trained model into an always-on inference endpoint. You create it, get a pk_deploy_ token and a /predict URL, and call that URL from REST, the SDK, or the CLI. It is billed by uptime, so you pause it when idle to stop the meter.

from pictograph import Client

client = Client()

deployment = client.deployments.create(
    model_id="mdl_road_signs",
    gpu="t4",            # t4 / l4 / a10g / a100
    min_containers=1,    # keep one warm, or 0 to scale to zero
)
print(deployment.endpoint_url)
print(deployment.token)   # pk_deploy_... shown once, store it now

How do I call the endpoint?

Each deployment exposes one /predict URL secured by its own bearer token. The token is shown once at create time and only its hash is stored.

curl https://your-deployment.pictograph.io/predict \
  -H "Authorization: Bearer pk_deploy_..." \
  -F "image=@frame.jpg"

from pictograph import DeploymentClient

client = DeploymentClient(
    endpoint="https://your-deployment.pictograph.io",
    token="pk_deploy_...",
)
print(client.predict(image="./frame.jpg"))

pictograph deployments predict dep_abc123 --image ./frame.jpg

How does billing work?

Deployments are metered by uptime in USD compute credits. The chosen GPU runs while the deployment is active, and you control cost three ways:

Pause and resume. Pause to stop the meter, resume in seconds.
Scale to zero. Set min_containers=0 to bill only for active seconds during requests.
Auto-pause. If your balance reaches zero, the deployment pauses automatically.

Every metering window is recorded for a full audit trail, and you see a transparent USD quote at create time.

How is a deployment different from a workflow?

A deployment is an always-on endpoint you call yourself, ideal for low-latency, on-demand inference. A workflow loads model weights directly per run, so batch and video jobs need no deployment. Use a deployment when you want a stable URL to integrate; use a workflow for pipelines over images, video, or datasets.

Next steps

Last updated June 2026.