Manage Dedicated Deployments

Provision and manage Dedicated Deployment GPU machines from Python.

Dedicated Deployments are managed GPU machines that run your Roboflow models with predictable latency and high throughput. The SDK manages them through the roboflow.adapters.deploymentapi adapter - the high-level Workspace class doesn't currently expose deployment methods.

Each function returns a (status_code, body) tuple so you can branch on the HTTP result:

from roboflow.adapters import deploymentapi

status, body = deploymentapi.list_deployment("YOUR_API_KEY")
if status == 200:
    for d in body.get("deployments", []):
        print(d["deployment_name"], d["status"])
else:
    print("Failed:", body)

List available machine types

from roboflow.adapters import deploymentapi

status, body = deploymentapi.list_machine_types("YOUR_API_KEY")
for m in body.get("machine_types", []):
    print(m["name"], m.get("description"))

Create a deployment

status, body = deploymentapi.add_deployment(
    api_key="YOUR_API_KEY",
    creator_email="[e-mail]",          # must be a workspace member
    machine_type="gpu-small",
    duration=8,                                # hours
    delete_on_expiration=True,
    deployment_name="my-deployment",
    inference_version=None,                    # None → latest
)

The deployment provisions asynchronously. Poll get_deployment until status == "ready".

Get deployment details

status, body = deploymentapi.get_deployment("YOUR_API_KEY", "my-deployment")
print(body["status"], body.get("public_url"))

Pause / resume / delete

deploymentapi.pause_deployment("YOUR_API_KEY", "my-deployment")
deploymentapi.resume_deployment("YOUR_API_KEY", "my-deployment")
deploymentapi.delete_deployment("YOUR_API_KEY", "my-deployment")

Logs

import datetime as dt

status, body = deploymentapi.get_deployment_log(
    api_key="YOUR_API_KEY",
    deployment_name="my-deployment",
    from_timestamp=dt.datetime.utcnow() - dt.timedelta(hours=1),
    to_timestamp=dt.datetime.utcnow(),
    max_entries=200,
)
for entry in body.get("logs", []):
    print(entry["timestamp"], entry["message"])

Usage

status, ws_usage = deploymentapi.get_workspace_usage(
    api_key="YOUR_API_KEY",
    from_timestamp=dt.datetime(2026, 4, 1),
    to_timestamp=dt.datetime(2026, 5, 1),
)

status, dep_usage = deploymentapi.get_deployment_usage(
    api_key="YOUR_API_KEY",
    deployment_name="my-deployment",
    from_timestamp=dt.datetime(2026, 4, 1),
    to_timestamp=dt.datetime(2026, 5, 1),
)

Running inference against a dedicated deployment

Once a deployment is ready, point inference SDK calls at its public_url (returned by get_deployment):

from inference_sdk import InferenceHTTPClient

client = InferenceHTTPClient(api_url=body["public_url"], api_key="YOUR_API_KEY")
result = client.infer("photo.jpg", model_id="my-detector/3")

REST and CLI equivalents

• REST: see Dedicated Deployments (REST).

• CLI: see Manage Deployments (CLI).