Great question — there are a few levers that materially affect “time to first answer” after an automation triggers a deployment. In Prefect 3.x, the biggest wins come from keeping the runtime warm, avoiding per-run infrastructure spin-up, and avoiding code fetch at run time. Quick wins (in order of impact) - Keep a hot process with serve (lowest latency) - Run your flow as a long-lived service so executions happen in an already-running process:

prefect flow serve ./path/to/flows.py:respond_to_event --name chatbot

or in code:

from prefect import flow

    @flow
    def respond_to_event(payload: dict):
        ...

    if __name__ == "__main__":
        respond_to_event.serve(name="chatbot")

- Point your automation’s “Run deployment” action at this served deployment. Because the process is already up with your code imported and dependencies loaded, cold start latency is minimal. - Prefer a Process work pool with an always-on worker - If you don’t need per-run isolation, use a process pool to avoid container scheduling delays:

# Create a process pool
    prefect work-pool create process-lowlatency --type process

    # Run a worker on a VM with your deps pre-installed
    prefect worker start -p process-lowlatency

- Keep the worker always on. This eliminates container/image pull time and cuts startup to just queue → execute. - If you must use Docker/Kubernetes/ECS, prebuild and prewarm - Prebuild a minimal image with all dependencies (no pip installs at runtime). - Bake your flow code into the image so there’s no git clone on each run. Pin a tag and avoid “latest”. - Pre-pull images on nodes (DaemonSet in K8s) and set imagePullPolicy to IfNotPresent. - Keep the cluster warm (min nodes > 0; avoid scale-to-zero on node groups). - Place workers and execution in the same region as your event source to minimize network and scheduling latency. - If you deploy from source (Git), reduce code fetch costs - Best: bake code into the image and skip git fetch at run time. - If you’re using a Process pool, you can point storage to a Git repo with caching so the worker maintains a local checkout between runs (e.g.,

GitRepository(..., pull_interval=60)

). This helps only when the same worker executes subsequent runs. - Trim Python import/init time - Move heavy imports/initializations (e.g., model loads) to module import or a startup hook when using serve so they happen once. - If you need models/files at runtime, pre-bundle them in the image or mount a fast local volume to avoid remote fetch on first token. Operator checklist - Use serve for “instant” runs, or a Process work pool with an always-on worker for near-instant starts. - If containerized, ensure images are small, prebuilt, pre-pulled; nodes are warm; and code is baked-in. - Keep your automation posture reactive and target a single queue with a dedicated worker to avoid queue contention. How to tell where your latency is - Check the flow run timeline and logs for gaps: - “Created infrastructure” → long gap = container scheduling/image pull - Early logs show git clone steps = code fetch latency - Flow begins quickly but first task waits on imports = import/model load - That will point you to the right fix from the list above. References and deeper dives - Work pools and workers: secure Python deploys - Prefect + Docker orchestration patterns - Case study: reducing deployment times A couple of clarifiers to tailor this: - Which work pool type are you using today (process, docker, kubernetes, ecs)? - Are you deploying from source (git clone at run time) or baking code into an image?

- Roughly how much of the delay is infra startup vs code fetch vs import/model load?

Great question. A couple key points: What pull_interval does - pull_interval is a setting on Prefect’s GitRepository storage that controls how often an already-running process checks for and pulls updates from the remote repo. - It only applies when the same runtime process keeps the repo checkout around (e.g., a long-lived Process worker or a served deployment). It does not change the behavior of one-shot “clone once and exit” runs. Why you can’t set it in prefect.yaml pull steps - The prefect.yaml pull steps (e.g., git_clone) run at deploy time on your machine to prepare the project. They aren’t used by workers at runtime. - The pull step’s API does not have a pull_interval parameter. Its parameters are repo, branch, commit_sha, include_submodules, credentials, directories, etc. So there’s no way to set pull_interval in prefect.yaml’s pull step. How to use pull_interval - Define your deployment from code using Flow.from_source with a GitRepository storage object and set pull_interval there. Example:

from prefect import Flow
from prefect.storage import GitRepository, GitCredentials

repo = GitRepository(
    url="<https://github.com/owner/repo.git>",
    branch="main",
    pull_interval=60,  # check for updates every 60s while the process runs
    credentials=GitCredentials(access_token="${{ PREFECT_GH_TOKEN }}"),
)

flow = Flow.from_source(repo, entrypoint="flows/chatbot.py:respond_to_event")

flow.deploy(
    name="chatbot",
    work_pool_name="process-lowlatency",
)

Notes and tips - For low-latency, a Process work pool with an always-on worker benefits most from pull_interval since the worker keeps a local checkout and periodically refreshes it. - If you use ephemeral containers (Docker/K8s/ECS) per run, pull_interval won’t help much; consider baking your code into the image to avoid git clone entirely. Docs - Prefect YAML and deployment steps: prefect.yaml guide - Deployment versioning and source: deployment versioning If you’d like, share how you’re creating deployments today (prefect deploy vs code with Flow.from_source) and your work pool type, and I can suggest the best wiring for your setup.