Mahdi Shamlou | Durable Workflow Engines Comparison — Temporal, DBOS Transact, Prefect & Custom Python Engines Mahdi SHamlou

#programming #designsystem #ai #python

Hey everyone! Mahdi Shamlou here again 🚀

After diving into classic algorithm challenges like LeetCode #8 String to Integer (atoi), today I want to explore something a bit different — durable workflow engines in Python.

I noticed most blog posts out there are old, incomplete, or heavily biased toward one tool. So I decided to create a fresh, practical comparison and give my take on which workflow engine you should pick in 2026.

Let’s dive in.

What is a Durable Workflow Engine?

A durable workflow engine helps you run code reliably and safely, even if your app crashes, your machine restarts, or network hiccups occur. They’re particularly useful for:

Long-running processes (hours, days, or even months)
Background jobs and automation
Retry, compensation, and saga patterns
Observability & external signaling

I’ll compare four approaches:

Temporal — the battle-tested distributed platform
DBOS Transact — lightweight embedded durability in Postgres
Prefect 3.x — Pythonic, open-source workflow orchestration
Custom Python engine — pure Python + your own persistence

1. Temporal (temporalio/sdk-python)

Temporal is a production-proven, distributed workflow engine originally built at Uber (as Cadence). It’s used by DoorDash, Snap, Stripe, and others.

How it works:

Workflows as code (functions/classes)
Activities separated from orchestration
External Temporal server handles task durability

Pros:

Maximum durability — even months-long workflows survive anything
Signals, queries, workflow versioning, child workflows
Multi-language support (Python, Go, Java, TS, .NET)
Great observability (Temporal Web UI / Cloud)

Cons:

Requires server/cluster → more ops complexity
Higher learning curve and more code

2. DBOS Transact (dbos-transact-py)

DBOS Transact is an embedded durable engine for Python backed by Postgres. Think of it as “durable Python without leaving your app.”

How it works:

Annotate normal Python functions with @workflow and @step decorators
Each step automatically checkpoints its state in Postgres
On crash/restart, workflow resumes from last checkpoint
External events or signals can be sent with recv() / send() methods
Minimal changes to your existing code — the library handles durability

Pros:

Minimal code changes → easy to adopt
Lightweight, fast, low ops overhead
Great performance for embedded durability

Cons:

Tied to Postgres
Fewer advanced features than Temporal

3. Prefect (Prefect 3.x)

Prefect is a Python-first workflow orchestration framework — great for data pipelines, ML workflows, and automation tasks.

How it works:

Define workflows with @flow and tasks with @task decorators
Each task execution is automatically tracked and persisted (checkpointing)
Flows can be suspended/resumed manually or via events
Result persistence allows retries, caching, and skipping already-completed tasks
Optional Prefect server/agent UI provides real-time observability and control

Pros:

Extremely Pythonic → minimal code friction
Excellent UI and monitoring
Durable execution for long-running flows
Open-source, hybrid execution

Cons:

Durability is checkpoint-based → not full deterministic replay like Temporal
Python-only (no multi-language support)
Some advanced saga patterns require manual handling

4. Pure Python Custom Engine

You could roll your own workflow engine using just Python + a DB or file store.

How it works:

Store workflow instances in a table with workflow ID, current step, status, and data
Poll a queue (e.g., Redis, RQ, Celery) and execute steps based on stored state
Implement retry, timeout, and compensation manually
Signals/events handled with a custom event table or pub/sub system

Pros:

Full control, zero dependencies
Simple for very small workflows

Cons:

Very easy to get wrong — lost state, duplicates, race conditions
No UI or observability out-of-the-box
Maintenance grows with complexity

My Take: Which One Should You Use?

Start with Prefect or DBOS Transact :

Prefect: Best for Python workflows, great UI, caching, event-driven automations. Durable execution with minimal overhead.
DBOS Transact: Ultra-lightweight, embedded durability in Postgres. Great if you want minimal footprint and existing Postgres usage.