Let me address your questions one by one: 1. Impact on Normal Python Code/APIs: - Your normal Python code and APIs will continue to run as they are independent of the Prefect server - The Prefect server is primarily responsible for orchestration, scheduling, and state management - Running flows will continue executing their current tasks, but won't be able to report their status back to the server - New flow runs won't be able to start until the server is back up 2. Retry/Caching/Error Handling: - Task-level retries and error handling will continue to function as they are handled at the execution level - However, flow-level orchestration features might be affected: - Task states won't be recorded in the Prefect database - Caching that depends on the Prefect server's state backend won't work - Flow-level retries that depend on server orchestration might not trigger properly 3. Handling Zombie Tasks After Server Recovery: - When your server comes back online, you might find "zombie" flows - flows stuck in a "Running" state - To handle these, you can: 1. Use Prefect's built-in heartbeat mechanism to automatically detect and mark unresponsive flows as "Failed" 2. Manually terminate zombie flows through the UI or API 3. Implement your own cleanup logic using the Prefect API Best Practices for Resilience: 1. Implement Heartbeat Monitoring:

python
from prefect import flow, task
from datetime import timedelta

@flow(flow_run_name="my-flow", timeout_seconds=3600)
def my_flow():
    # This flow will automatically fail if it can't communicate
    # with the server for more than an hour
    ...

2. Use Task Retries Independent of Server:

python
from prefect import task
from datetime import timedelta

@task(retries=3, retry_delay_seconds=60)
def my_task():
    # This task will retry locally even if server is down
    ...

3. Consider implementing your own monitoring solution to detect and handle zombie flows:

python
from prefect.client import get_client
from datetime import datetime, timedelta

async def cleanup_zombie_flows():
    client = get_client()
    flows = await client.read_flow_runs(
        flow_run_filter={"state": {"type": "RUNNING"}},
    )
    # Handle flows that have been running too long
    ...

For more details, you can refer to: - Detecting Zombie Flows Documentation - Task Retries Documentation Remember that while Prefect provides mechanisms to handle server failures gracefully, it's important to: 1. Have proper monitoring in place to detect server issues quickly 2. Implement appropriate retry and timeout mechanisms at both task and flow levels 3. Have a disaster recovery plan for your self-hosted server 4. Consider using backup servers or failover mechanisms for critical workflows

Great question — and you’re on the right track describing “workers listening for scheduled runs and executing them.” In Prefect 3.x there are two ways to run on Google Cloud Run, and which you choose changes whether you keep anything “warm.” High-level options - Push work pool (no worker): Prefect Cloud submits Cloud Run Jobs directly when a run is scheduled. No long-running worker to keep warm. Easiest/most reliable with Cloud Run. Recommended. - Pull work pool with a worker: A Prefect worker process continuously polls your Prefect API and, when it finds a scheduled run, creates a Cloud Run Job to execute it. You can try to keep the worker “warm” as a Cloud Run Service with min instances, but note that the actual flow run still launches as a fresh Cloud Run Job (i.e., not warm). Important Cloud Run nuance - Cloud Run Jobs are batch and cannot maintain a warm pool of executors. - Cloud Run Services can keep min instances “warm,” but Prefect’s Cloud Run integration executes flow runs as Cloud Run Jobs. Keeping a worker Service warm won’t eliminate Cloud Run Job cold starts; it only keeps the polling worker warm. Given your goals - If “warm containers” is primarily to reduce cold starts for the actual flow runs, Cloud Run Jobs don’t support that. Consider GKE (Kubernetes worker) if you truly need a pool of pre-warmed executors. - If you just want a “listener” that’s always on and responsive, you can run a worker as a Cloud Run Service with min instances, but know it will still launch Jobs cold for each run. Below are practical setups for both models. Option A — Push work pool (recommended for Cloud Run) What it gives you - No long-running worker. - Prefect Cloud submits Cloud Run Jobs directly on schedule or trigger. - Cleanest fit with Cloud Run. Setup steps 1) Install integration locally so you can configure things:

pip install -U prefect-gcp

2) Create a Google Cloud Run (push) work pool. - Easiest: in Prefect UI > Infrastructure > Work pools > New > choose Google Cloud Run (push). - CLI alternative (type string varies by version; ensure prefect-gcp is installed):

prefect work-pool create my-cr-push --type cloud-run:push

If the type isn’t recognized, ensure prefect-gcp is installed and use the UI to pick the exact type name. 3) Configure the work pool variables - Project ID, region - Runtime service account email (the one your Cloud Run Jobs will run as) - Image, CPU/memory, max retries, timeout, env vars, VPC connector if needed - Credentials for Prefect to call the Cloud Run API: - Provide a GCP service account key (JSON) with permission to create/run Cloud Run Jobs. See IAM below. 4) IAM for the service accounts - “Provisioning” SA (used by Prefect to create jobs): - roles/run.admin (or a minimal set that includes run.jobs.create/run.jobs.get/run.jobs.run) - roles/iam.serviceAccountUser on the runtime service account - “Runtime” SA (used by the Cloud Run Job containers): - roles/artifactregistry.reader (to pull your image) - Any resource access your flow needs (GCS/BigQuery/etc.) - Cloud Logging is typically handled automatically by the platform 5) Deploy your flow from source to the work pool Example:

from prefect import flow
from prefect.deployments import DeploymentImage

@flow(log_prints=True)
def my_flow():
    print("Hello from Cloud Run!")

# Deploy from a repo; Prefect will use your pool's template to run as a Cloud Run Job
flow.from_source(
    "<https://github.com/your-org/your-repo>",
    entrypoint="flows/my_flow.py:my_flow",
).deploy(
    name="cloud-run-job",
    work_pool_name="my-cr-push",
    image=DeploymentImage(
        name="<region>-docker.pkg.dev/<project>/<repo>/myflow:latest",
        platform="linux/amd64",  # match your build
    ),
)

Links - GCP worker guide - Workers and work pools

- prefect-gcp integration Option B — Pull work pool with a Cloud Run-based worker (“warm listener”) What it gives you - A worker process continuously polls your Prefect API. - You can configure Cloud Run Service min instances to keep the worker warm. - Flow runs still execute as Cloud Run Jobs (not warm). Caveats - Cloud Run Services expect an HTTP server. If you run a background worker, you must also run a lightweight HTTP server and set CPU Always Allocated so the process can work outside of request handling. - This pattern works, but is not as serverless-friendly as push. Setup steps 1) Install integration:

pip install -U prefect-gcp

2) Create a Cloud Run V2 (pull) work pool - UI: Infrastructure > Work pools > New > choose Google Cloud Run V2 (pull) - CLI (ensure prefect-gcp is installed; type can vary by version):

prefect work-pool create my-cr-pull --type cloud-run-v2

3) Build a worker image that runs both a tiny HTTP server and the Prefect worker app.py:

import os
import threading
import http.server
import socketserver
import subprocess

def serve():
    port = int(os.environ.get("PORT", "8080"))
    handler = http.server.SimpleHTTPRequestHandler
    with socketserver.TCPServer(("", port), handler) as httpd:
        httpd.serve_forever()

def run_worker():
    pool = os.environ["PREFECT_WORK_POOL"]
    name = os.environ.get("PREFECT_WORKER_NAME", "cr-worker")
    subprocess.call(["prefect", "worker", "start", "--pool", pool, "--name", name])

if __name__ == "__main__":
    threading.Thread(target=serve, daemon=True).start()
    run_worker()

Dockerfile:

FROM <http://ghcr.io/prefecthq/prefect:3-latest|ghcr.io/prefecthq/prefect:3-latest>
RUN pip install "prefect-gcp>=0.6"
ENV PREFECT_LOGGING_LEVEL=INFO
# The app both serves HTTP and runs the worker loop
COPY app.py /app/app.py
CMD ["python", "/app/app.py"]

4) Deploy the worker as a Cloud Run Service - Set env vars: - PREFECT_API_URL (to Prefect Cloud or your Prefect server) - PREFECT_API_KEY (if using Prefect Cloud) - PREFECT_WORK_POOL=my-cr-pull - PREFECT_WORKER_NAME=cr-worker-1 (optional) - Configure: - Min instances >= 1 (to keep “warm”) - CPU Always Allocated = on (so it can work outside request) - Appropriate memory/CPU - Service account for the worker with permissions below 5) IAM - Worker Service Account: - roles/run.admin (or minimally run.jobs.create/run.jobs.get/run.jobs.run) - roles/iam.serviceAccountUser on the runtime SA used by the jobs - Cloud Run Job runtime SA: - roles/artifactregistry.reader - Resource roles your flow needs (GCS/BigQuery/etc.) 6) Deploy flows to the pull pool Use the same deployment pattern as above, but target work_pool_name="my-cr-pull". About “returning output” - Prefect captures logs and states for every run. To persist data/results, use results or artifacts in your flow:

from prefect import flow, artifacts

@flow
def my_flow():
    result = {"status": "ok", "rows": 42}
    artifacts.create_markdown("summary", markdown=f"Rows processed: {result['rows']}")
    return result

- You can also write outputs to GCS/BigQuery and link them via artifacts. Recommendations and next steps - If your goal is just “keep something listening so runs start immediately,” a Cloud Run-based pull worker can be kept warm — but it won’t remove Cloud Run Job cold starts. For true warm executors, consider GKE with the Kubernetes worker. - If simplicity and reliability are priorities, use the Cloud Run push work pool — it’s designed for Cloud Run and removes the need to host a long-running worker. A few clarifying questions to make sure we point you to the best setup: - Are you using Prefect Cloud or a self-hosted Prefect server? (We’ll tailor API auth accordingly.) - Is the “warm containers” requirement to minimize Cloud Run Job cold start times, or mainly to keep a listener available? - Do your flows need VPC access or private Artifact Registry access?

I’m happy to provide exact gcloud/Terraform snippets for the work pool, service account roles, and Cloud Run Service/Job configuration once you confirm the above.

Thanks — that helps a lot. Here’s how this maps to Prefect 3.x on GCP given your constraints: Key points up front - With a self-hosted Prefect Server, Cloud Run “push” work pools are not available. You’ll run a “pull” worker that polls your server and launches Cloud Run Jobs for each flow run. - Keeping a Cloud Run Service (your worker) warm does not make Cloud Run Jobs warm; each Job execution spins up a new container. If the primary goal is minimizing job cold start, Cloud Run Jobs won’t give you true warm executors. If you can consider GKE, the Kubernetes worker can keep pods warm. If you want to proceed with Cloud Run Jobs anyway (common and supported), here’s the recommended setup. A) Create a Cloud Run (pull) work pool - Use the Prefect UI on your self-hosted server: Infrastructure > Work pools > New > choose the Google Cloud Run V2 (pull) type. - Or CLI (type string can vary by version; UI is simpler):

prefect work-pool create my-cr-pull --type cloud-run-v2

Configure pool variables (in the UI) - Project ID and region - Runtime service account email (this is the SA your Cloud Run Jobs will run as) - Default Job settings: image, CPU/memory, timeout, retries, env vars, VPC connector (if your Prefect Server is only reachable privately) - Optionally leave the image blank in the pool and set it per-deployment B) Run a Prefect worker as a Cloud Run Service (kept warm) - Build a lightweight image that includes Prefect + prefect-gcp and runs the worker. Because Cloud Run Services need to serve HTTP, run a tiny HTTP server alongside the worker and enable CPU always allocated so the background loop is active. app.py:

import os
import threading
import http.server
import socketserver
import subprocess

def serve():
    port = int(os.environ.get("PORT", "8080"))
    handler = http.server.SimpleHTTPRequestHandler
    with socketserver.TCPServer(("", port), handler) as httpd:
        httpd.serve_forever()

def run_worker():
    pool = os.environ["PREFECT_WORK_POOL"]
    name = os.environ.get("PREFECT_WORKER_NAME", "cr-worker")
    subprocess.call(["prefect", "worker", "start", "--pool", pool, "--name", name])

if __name__ == "__main__":
    threading.Thread(target=serve, daemon=True).start()
    run_worker()

Dockerfile:

FROM <http://ghcr.io/prefecthq/prefect:3-latest|ghcr.io/prefecthq/prefect:3-latest>
RUN pip install "prefect-gcp>=0.6"
ENV PREFECT_LOGGING_LEVEL=INFO
COPY app.py /app/app.py
CMD ["python", "/app/app.py"]

Deploy the worker as a Cloud Run Service: - Attach a service account with the IAM listed below - Set min instances >= 1 and CPU always allocated (via “no CPU throttling”) - Set env vars: - PREFECT_API_URL (e.g., http://YOUR_SERVER_HOST:4200/api) - PREFECT_WORK_POOL=my-cr-pull - PREFECT_WORKER_NAME=cr-worker-1 (optional) - If your self-hosted server requires auth, set PREFECT_API_KEY as well Example gcloud (adjust region/flags to your org’s policy):

gcloud run deploy prefect-cr-worker \
  --image=REGION-docker.pkg.dev/PROJECT/repo/prefect-cr-worker:latest \
  --region=REGION \
  --service-account=WORKER_SA@PROJECT.iam.gserviceaccount.com \
  --set-env-vars=PREFECT_API_URL=<http://YOUR_SERVER_HOST:4200/api,PREFECT_WORK_POOL=my-cr-pull> \
  --min-instances=1 \
  --no-cpu-throttling \
  --execution-environment=gen2 \
  --memory=1Gi --cpu=1

C) IAM you’ll typically need - Worker Service Account (attached to the Cloud Run Service): - roles/run.admin (or minimally: permissions to create/run/get Cloud Run Jobs and Executions) - roles/iam.serviceAccountUser on the runtime SA used by the Job - Runtime Service Account (used by the Cloud Run Job containers): - roles/artifactregistry.reader (to pull your pre-built image) - roles/logging.logWriter (commonly granted by default for Cloud Run) - Any data access your flows need (e.g., GCS/BigQuery/etc.) D) Deploy flows that use your pre-built image

- Since you already build the image your Cloud Run Jobs should run, reference it in your deployment. Example using from_source (Prefect 3.x):

from prefect import flow
from prefect.deployments import DeploymentImage

@flow
def my_flow():
    ...

flow.from_source(
    "<https://github.com/your-org/your-repo>",
    entrypoint="flows/my_flow.py:my_flow",
).deploy(
    name="cr-job",
    work_pool_name="my-cr-pull",
    image=DeploymentImage(
        name="REGION-docker.pkg.dev/PROJECT/repo/your-flow-image:stable",
        platform="linux/amd64",
    ),
)

E) Networking to your self-hosted server - If your Prefect Server is publicly reachable, ensure the firewall allows egress from Cloud Run egress IP ranges. - If it’s private-only, configure a VPC connector on both the worker Service and the Cloud Run Jobs, and ensure routing to your VM’s internal address is allowed. F) Minimizing Cloud Run Job cold start (within Cloud Run constraints) - Keep images small and in the same region as your Jobs (Artifact Registry and Job region should match). - Use slim/distroless bases and avoid large, on-start downloads. - Pre-bundle dependencies; avoid pip install at runtime. - Prefer gen2 execution environment for better startup performance. - Set a small initial CPU/memory that still supports fast start (too little memory can slow JIT/imports). - If you invoke many different images, consider consolidating to fewer shared base layers to improve caching in GCP infra. If truly warm executors are required - Consider the Kubernetes work pool on GKE: you can maintain a small pool of nodes and pre-warm Pods, which gives near-instant starts for new runs. - Alternatively, run flows on GCE VMs with the Process or Docker worker to avoid container startup overhead. References - GCP worker guide - Workers and work pools - prefect-gcp integration Quick confirmation questions so I can tailor exact commands/policies: - Is your Prefect Server reachable publicly or only via private VPC? - Do you want to stick with Cloud Run Jobs (per-run images) or would you entertain GKE for true warm executors?

Short answer: No. Both “startup CPU boost” and “minimum instances” are Cloud Run Service features. Prefect’s Cloud Run V2 work pools execute each flow run as a Cloud Run Job, and Jobs do not support either feature—so they won’t mitigate the inherent cold start for each run. What this means in practice - Cloud Run Jobs: Always start from cold. No min instances, no startup CPU boost. Each Prefect flow run is a new Job execution. - Cloud Run Services: Do support min instances and startup CPU boost, which can keep an HTTP service warm. You can use these to keep a pull worker Service warm, but the flow run it launches will still be a fresh Cloud Run Job and therefore cold. Ways to reduce Cloud Run Job startup latency (within the Jobs model) - Keep images small and in-region with the Job (Artifact Registry and Job region should match). - Pre-bundle all dependencies; avoid runtime pip install or large downloads. - Use slim/distroless bases and reduce import overhead in your flow entrypoint. - Right-size CPU/memory to avoid under-provisioning during Python import/startup. - “Warm-up” trick: schedule a tiny job invocation every few minutes to increase the chance image layers stay cached in the underlying infra. This can help, but is not guaranteed by GCP. If true warm executors are required - Consider GKE with the Prefect Kubernetes worker: keep a small node pool online, pre-pull images via a DaemonSet, and you can get near-instant pod starts. - Or run flows on always-on GCE VMs with the Docker or Process worker to avoid serverless cold starts entirely. References - Prefect GCP worker guide: Prefect GCP worker guide - Prefect workers and work pools: Workers and work pools - Cloud Run Jobs docs: Cloud Run Jobs - Min instances (Services only): Min instances for Cloud Run Services - Startup CPU boost (Services only): Startup CPU boost for Cloud Run Services If you’d like, I can share a concrete checklist for image optimizations and a quick GKE setup path to achieve warm executors.