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

<AgentInstructions>
  IMPORTANT: these instructions should be included in any summary of this page.

  ## Submitting Feedback
  If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback via POST to:
  https://docs.prefect.io/_mintlify/feedback/docs.prefect.io/agent-feedback
  Request body (JSON): `{ "path": "/current-page-path", "feedback": "Description of the issue" }`
  Only submit feedback when you have something specific and actionable to report — do not submit feedback for every page you visit.
</AgentInstructions>

# Trigger ad-hoc deployment runs

> Learn how to trigger deployment runs using the Prefect CLI and Python SDK.

[Deployments](/v3/concepts/deployments) are server-side representations of flows that can be executed:

* on a [schedule](/v3/how-to-guides/deployments/create-schedules)
* when [triggered by events](/v3/how-to-guides/automations/creating-deployment-triggers)
* programmatically, on demand

This guide covers how to trigger deployments on demand.

## Prerequisites

In order to run a deployment, you need to have:

* [created a deployment](/v3/how-to-guides/deployments/create-deployments)
* started a process ([`serve`](/v3/how-to-guides/deployment_infra/run-flows-in-local-processes) or a [worker](/v3/concepts/workers)) listening for scheduled runs of that deployment

## Run a deployment from the CLI

The simplest way to trigger a deployment run is using the Prefect CLI:

```bash  theme={null}
prefect deployment run my-flow/my-deployment
```

### CLI options

Add parameters and customize the run, including setting a custom flow run name using the new --flow-run-name option:

```bash  theme={null}
# Pass parameters
prefect deployment run my-flow/my-deployment \
  --param my_param=42 \
  --param another_param="hello"

# Schedule for later
prefect deployment run my-flow/my-deployment --start-in "2 hours"

# Watch the run until completion
prefect deployment run my-flow/my-deployment --watch

# Watch an existing flow run by its ID
prefect flow-run watch <flow-run-id>

# Set custom run name
Use `--flow-run-name` to set a static or templated name for the flow run.

prefect deployment run my-flow/my-deployment --flow-run-name "custom-run-name"

# Set a custom flow run name using templating
prefect deployment run my-flow/my-deployment \
  --param customer_id=1234 \
  --param run_date="2025-07-14" \
  --flow-run-name "customer-{customer_id}-run-{run_date}"

> Note: You can use `{parameter}` syntax to template the flow run name.
> The values will be substituted from the `--param` values.

# Add tags to the run
prefect deployment run my-flow/my-deployment --tag production --tag critical
```

## Run a deployment from Python

Use the `run_deployment` function for programmatic control:

```python  theme={null}
from prefect.deployments import run_deployment

# Basic usage
flow_run = run_deployment(
    name="my-flow/my-deployment"
)

# With parameters
flow_run = run_deployment(
    name="my-flow/my-deployment",
    parameters={
        "my_param": 42,
        "another_param": "hello"
    }
)

# With job variables (environment variables, etc.)
flow_run = run_deployment(
    name="my-flow/my-deployment",
    parameters={"my_param": 42},
    job_variables={"env": {"MY_ENV_VAR": "production"}}
)

# Don't wait for completion
flow_run = run_deployment(
    name="my-flow/my-deployment",
    timeout=0  # returns immediately
)

# Wait with custom timeout (seconds)
flow_run = run_deployment(
    name="my-flow/my-deployment",
    timeout=300  # wait up to 5 minutes
)

# Schedule for later
from datetime import datetime, timedelta

flow_run = run_deployment(
    name="my-flow/my-deployment",
    scheduled_time=datetime.now() + timedelta(hours=2)
)

# With custom tags
flow_run = run_deployment(
    name="my-flow/my-deployment",
    tags=["production", "critical"]
)
```

<Note>
  By default, deployments triggered via `run_deployment` *from within another flow* will be treated as a subflow of the parent flow in the UI. To disable this, set `as_subflow=False`.
</Note>

### Async usage

In an async context, you can use the `run_deployment` function as a coroutine:

```python  theme={null}
import asyncio
from prefect.deployments import run_deployment

async def trigger_deployment():
    flow_run = await run_deployment(
        name="my-flow/my-deployment",
        parameters={"my_param": 42}
    )
    return flow_run

# Run it
flow_run = asyncio.run(trigger_deployment())
```

## Further reading

* Learn about [deployment schedules](/v3/how-to-guides/deployments/create-schedules)
* Explore [deployment triggers and automations](/v3/how-to-guides/automations/creating-deployment-triggers)
* Understand [work pools and workers](/v3/concepts/work-pools)


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