Great question. At a high level: Prefect is a Python-first workflow orchestrator optimized for data and ML pipelines, while Temporal is a language-agnostic durable workflow engine designed for long‑running, highly reliable business processes and microservice orchestration. Here’s a concise comparison to help you choose: - Programming model - Prefect: Write normal Python functions for tasks/flows; no determinism constraints. Orchestration happens at task/flow boundaries. - Temporal: Workflow code must be deterministic and is “replayed” from an event history. Non-deterministic work is moved to Activities. - Links: Prefect docs, Temporal docs - Durability and long-running work - Prefect: Durable at the workflow/task state level (runs, retries, caching, persisted results), but does not resume mid-function; retries re-execute tasks. - Temporal: Fully durable workflow histories, durable timers, signals, heartbeating Activities; can run workflows for months/years and reliably resume precisely. - Links: Prefect states, Temporal workflows - Language ecosystem - Prefect: Python-first (flows and tasks are Python). Great fit for data stacks (pandas, Spark, dbt, ML). - Temporal: Polyglot. Mature SDKs for Go, Java, TypeScript; Python SDK available and under active development. - Link: Temporal - Scheduling and triggers - Prefect: Schedules on deployments (cron/interval/RRule), plus robust Automations for event-driven triggers and notifications. - Temporal: Native cron schedules on workflows; Signals/Queries for event-driven interaction with running workflows. - Links: Prefect deployments, Temporal cron, Temporal signals - Scaling model - Prefect: Scale via workers and work pools that launch jobs on your infra (Kubernetes, ECS, Docker, etc.). Horizontal scale is straightforward by adding workers. - Temporal: Scale Temporal Server (self-hosted or Temporal Cloud) and add more workers on task queues. Excellent throughput for massive numbers of workflow/Activity tasks. - Ops and deployment - Prefect: Fast time-to-value. Use Prefect Cloud (hosted API/UI) with only workers in your VPC, or self-host Prefect Server. Minimal operational overhead for most data teams. - Temporal: Temporal Cloud reduces ops burden; self-hosted Temporal Server is a multi-service system with stateful persistence and optional Elasticsearch for visibility—more ops-heavy but extremely robust. - Links: Prefect 3 GA, Temporal Cloud - Observability and UI - Prefect: Rich UI for flows/tasks, params, logs, retries, artifacts, concurrency limits, and automations. - Temporal: Web UI for workflow histories, event timelines, signals/queries, search/visibility, and advanced debugging of workflow state. - Links: Prefect docs, Temporal docs - Typical sweet spots - Prefect: Data engineering and ML pipelines, ELT/ETL, analytics orchestration, Python-centric teams, running jobs on Kubernetes/ECS/Docker with clear task boundaries and retries. - Temporal: Cross-service business processes, sagas, exactly-once workflow coordination, human approvals, long waits/timers, and workflows that must survive restarts with precise state replay. - Trade-offs in practice - Prefect: Very flexible Python code, easy onboarding for data teams, simpler ops; in exchange, you don’t get instruction-level deterministic replay—resilience is at task/activity boundaries with retries and persisted state/results.

- Temporal: Strong guarantees via deterministic workflow replays, durable timers/signals, and rich failure semantics; in exchange, you accept determinism rules and typically a higher ops/architecture bar (unless you use Temporal Cloud). If it helps, a quick rule of thumb: - Mostly Python, data/ML jobs, scheduled pipelines, containerized tasks on your infra, want a fast and friendly UI/Cloud option → Prefect. - Polyglot microservices, long-running business processes with human-in-the-loop, strict durability and state replay guarantees → Temporal. If you can share a bit about your use case (data vs microservices, languages involved, how long workflows run, infra preferences, ops constraints), I can give a more pointed recommendation and example architecture. Helpful links: - Prefect 3 overview: Prefect 3 GA - Prefect docs: Prefect docs - Temporal overview: Temporal - Temporal docs: Temporal docs

Short answer: Prefect is the right fit for this. Your pattern is a periodic, Python-centric ETL/ELT pipeline with short runs, DB as the source of truth, retries, and API rate limits. That’s Prefect’s sweet spot. Temporal would be overkill here unless you need long‑running, human-in-the-loop processes, strict deterministic replay, or a polyglot microservices orchestration layer. Why Prefect suits your use case - Simple 5‑minute schedules with two flows (collector and processor) - Easy parallel API calls with built-in retries/backoff/timeouts - Global rate limits and “no overlap” concurrency so runs don’t step on each other - Straightforward ops: run workers on your infra (Docker/K8s/ECS) and use Prefect Cloud for UI/observability Key building blocks (Prefect 3.x) - Scheduling: deployments with

Cron("*/5 * * * *")

- Prevent overlap: a global concurrency limit with

concurrency("processor-run")

- API rate limits:

rate_limit("vendor-x")

configured globally - Parallel enrichment:

ThreadPoolTaskRunner

+

.submit

- Automations: optionally trigger Processor when Collector completes, or offset schedules by a minute Minimal patterns Collector (every 5 minutes)

from prefect import flow, task, get_run_logger

@task(retries=3, retry_delay_seconds=10, timeout_seconds=60)
def fetch_siem_alerts():
    # TODO: pull from SIEM
    return [...]

@task
def write_alerts_to_db(alerts):
    # TODO: upsert alerts; ensure idempotency
    return len(alerts)

@flow
def collector():
    log = get_run_logger()
    alerts = fetch_siem_alerts()
    n = write_alerts_to_db(alerts)
    <http://log.info|log.info>(f"Inserted/updated {n} alerts")

Processor (every 5 minutes, no overlap, respect API limits)

from prefect import flow, task
from prefect.task_runners import ThreadPoolTaskRunner
from prefect.concurrency.sync import concurrency, rate_limit

@task(retries=3, retry_delay_seconds=[5,15,30], timeout_seconds=30)
def call_api_vendor_x(alert):
    with rate_limit("vendor-x"):  # create this limit in Prefect UI
        # TODO: call API X
        return {...}

@task
def load_new_alerts():
    # TODO: fetch only unprocessed/new alerts
    return [...]

@task
def save_enriched(alert):
    # TODO: write back to DB
    return True

@flow(task_runner=ThreadPoolTaskRunner(max_workers=8))
def processor():
    # ensure only one processor run at a time
    with concurrency("processor-run", occupy=1, timeout_seconds=0):
        alerts = load_new_alerts()
        futs = [call_api_vendor_x.submit(a) for a in alerts]
        results = [f.result() for f in futs]
        for r in results:
            save_enriched.submit(r)

How to run it on a schedule - Create deployments with cron schedules and point them at your work pool. Example (programmatic):

from prefect.flows import Flow
from prefect.schedules import Cron

Flow.from_source(".", "collector:collector").deploy(
    name="collector",
    schedule=Cron("*/5 * * * *"),  # every 5 minutes
    work_pool_name="my-pool",
)

Flow.from_source(".", "processor:processor").deploy(
    name="processor",
    schedule=Cron("*/5 * * * *"),  # or offset to start at :02
    work_pool_name="my-pool",
)

- In Prefect Cloud, create: - A global concurrency limit named

processor-run

with limit 1 - A rate limit named

vendor-x

matching the provider’s API policy - Optional Automation: trigger the Processor deployment when the Collector run completes, instead of two separate cron jobs Helpful references - Global rate limits: Rate limit concepts - Global concurrency limits: How to: concurrency limits - Work pools/workers overview: Work pools & workers - Automations overview: Automations & observability

- Task parallelism pattern: Parallel task patterns If you share: - your infra target (Docker, K8s, ECS), - expected alert volume, - API rate limits, I can sketch the worker/work-pool setup and suggest safe batch sizes and concurrency settings.

Short answer: No. In Prefect 3.x, workers only pick up flow runs that come from deployments. If you want a worker to execute your flow, you need a deployment targeting that worker’s work pool. Your alternatives without a deployment: - Run locally: call the flow like a normal Python function (runs in your process; no worker involved). - Serve/Runner for dev: you can “serve” flows or use a Runner to poll and run locally for development, but that’s not your worker executing it. If your goal is “use my worker infra,” create a deployment (programmatically or via CLI) and point it at your work pool. Helpful links: - Deployments overview: Prefect 3 deployments - Run flows locally (Runner): Run flows in local processes If you tell me whether you want ad-hoc local runs or worker-backed runs on your infra, I can share the exact snippet to set it up.

Yes — you can serve 20+ clients without creating 60+ separate deployments by parameterizing your deployments and using multiple schedules. Recommended pattern - Use 2 deployments total (one for Collector, one for Processor) - Add a

client_id

parameter to both flows. - Attach one cron schedule per client to each deployment; each schedule sets

parameters={"client_id": "<client>"}

. - Optionally offset minutes per client (to spread load and avoid API bursts). - Store per‑client config - Put credentials/secrets in Prefect Cloud (Secrets/Variables) and look them up by

client_id

. - Enforce per‑client isolation - Use a per‑client concurrency limit inside your flow so runs for the same client never overlap:

concurrency(f"processor-{client_id}", occupy=1)

. - Add per‑vendor API rate limits (e.g.,

rate_limit("vendor-x")

) so your 20+ clients don’t exceed external quotas. - Cut schedules in half (optional) - Keep only the Collector scheduled; use an Automation to trigger the Processor for the same

client_id

when the Collector run completes and actually ingested new alerts. Minimal code Flows with client_id parameter and isolation

from prefect import flow, task
from prefect.concurrency.sync import concurrency, rate_limit
from prefect.task_runners import ThreadPoolTaskRunner

@task
def fetch_from_siem(client_id): ...
@task
def upsert_alerts(client_id, alerts): ...

@flow
def collector(client_id: str):
    alerts = fetch_from_siem(client_id)
    return upsert_alerts(client_id, alerts)

@task
def load_new_alerts(client_id): ...
@task
def enrich_with_vendor_x(alert):
    with rate_limit("vendor-x"):
        ...
    return alert

@task
def save_enriched(client_id, alert): ...

@flow(task_runner=ThreadPoolTaskRunner(max_workers=8))
def processor(client_id: str):
    # ensure one processor per client at a time
    with concurrency(f"processor-{client_id}", occupy=1, timeout_seconds=0):
        alerts = load_new_alerts(client_id)
        futs = [enrich_with_vendor_x.submit(a) for a in alerts]
        for f in futs:
            save_enriched.submit(client_id, f.result())

Deploy once per flow with many client schedules

from prefect.flows import Flow
from prefect.schedules import Cron

clients = ["acme", "beta", "charlie"]  # build this list from your DB/config
collector_schedules = []
processor_schedules = []

# Stagger minutes: 0, 1, 2, ... to avoid bursts
for i, cid in enumerate(clients):
    minute = i % 5  # example staggering within a 5m cadence
    collector_schedules.append(Cron(f"{minute}/5 * * * *", parameters={"client_id": cid}))
    processor_schedules.append(Cron(f"{(minute+2)%5}/5 * * * *", parameters={"client_id": cid}))

Flow.from_source(".", "collector:collector").deploy(
    name="collector",
    schedules=collector_schedules,
    work_pool_name="your-pool",
)

Flow.from_source(".", "processor:processor").deploy(
    name="processor",
    schedules=processor_schedules,
    work_pool_name="your-pool",
)

Why this keeps costs in check - You avoid multiplying deployments; management overhead stays low with just 2 deployments. - Prefect Cloud costs generally scale with run volume and your own compute usage, not the count of deployments. Reducing “empty” work (fast no‑op when no new alerts) and staggering schedules helps. - One shared work pool and a small worker fleet can run everything; add horizontal workers only if throughput requires it. Optional: replace the Processor schedules with an Automation - When a Collector run completes and “new alerts > 0”, trigger the Processor deployment with the same

client_id

. This halves your scheduled runs while keeping the workflow reactive. Useful docs - Deployments: Deployments overview - Schedules (Cron/RRule/parameters): Schedules

- Global concurrency limits: Global concurrency limits - Rate limits: Rate limits - Work pools/workers: Work pools & workers Questions so I can tailor this exactly: - Do you need strict per‑client isolation (e.g., dedicated workers/work pools) or is logical isolation (concurrency, tagging, parameters) sufficient? - How many alerts per 5 minutes and what are the external API rate limits? - What infra will run workers (Docker, K8s, ECS)?