I'll be honest: I loved Celery. For years, it was my go-to for background tasks in Python. Queue up some jobs, let Redis handle the broker, spin up workers, and call it a day. Simple, right?
Then came the project that broke everything.
We were building an AI-powered customer support system for a healthcare tech client. Think intelligent ticket routing, sentiment analysis on support conversations, and an LLM-powered chatbot using LangChain that could handle complex medical billing questions. The kind of system where a task might take 30 seconds or 30 minutes depending on the conversation depth.
That's when Celery showed its cracks.
The Pain Points: What Actually Goes Wrong With Celery
Five critical problems that forced us to migrate
1. Zero Visibility Into Running Tasks
Picture this: Your client calls at 2 AM because "the AI responses stopped working." You check Celery. Tasks are queued. Workers are running. But... which tasks are actually executing? Where are they stuck? No idea.
What I faced:
- A worker silently failed halfway through processing a chain of LLM calls
- The task showed as "running" in Flower (Celery's monitoring tool)
- No way to see it was stuck on the 4th API call to OpenAI
- Had to restart the entire worker pool and lose in-flight work
The real problem: Celery gives you task IDs and states (pending, started, success, failure). That's it. If you want to know what's happening inside a long-running task? You're on your own.
2. Long-Running Tasks Kill Workers
Our LangChain chatbot would sometimes need to:
- Fetch conversation history (2-3 seconds)
- Call GPT-4 for context analysis (5-10 seconds)
- Query our vector database for relevant billing codes (3-5 seconds)
- Call GPT-4 again for the final response (5-10 seconds)
- Update conversation state and log everything (2 seconds)
Total time: 17-30 seconds on average, but sometimes 2-3 minutes for complex queries.
What happened:
- Workers would appear "busy" for minutes at a time
- New high-priority tasks (like urgent ticket routing) would sit in the queue
- We had to over-provision workers just to handle spikes
- Worker memory would bloat over time and require restarts
Enter Temporal: The Game Changer
Why workflows that survive failures matter
After weeks of Celery chaos, a colleague mentioned Temporal. "It's like Celery, but for workflows you actually care about staying alive."
I was skeptical. Another task queue? Really?
Then I read this line in their docs: "Workflows in Temporal are resumable, recoverable, and reactive. They survive process failures, network partitions, and even data center outages."
I built a proof-of-concept in a weekend. By Monday, I was rewriting our production system.
What Makes Temporal Different:
1. Full Visibility Into Every Running Workflow
With Temporal's Web UI, I could see:
- Every workflow currently executing
- Exactly which step each workflow was on
- Complete history of every activity (API call, DB query, etc.)
- How long each step took
- Full execution history even after completion
Real example from our system:
Workflow: process_customer_chat_abc123
Status: Running
Current Step: generate_llm_response (Activity 4 of 6)
Duration: 8.3 seconds
Previous Steps:
extract_intent - 1.2s
fetch_conversation_history - 2.1s
query_vector_database - 3.7s
→ generate_llm_response - 8.3s (in progress)
Compare that to Celery's: Task abc123: STARTED
Long-Running Workflows Are First-Class Citizens
How Temporal handles complex AI workflows naturally
In Temporal, workflows can run for seconds, hours, or even days. Workers can restart, machines can reboot, and your workflow just... continues.
How it works:
- Temporal persists every step's result
- When a worker picks up a workflow, it replays the history
- Activities (individual steps) are idempotent
- If a worker dies mid-activity, another worker picks it up
Our chatbot workflow in Temporal handled:
- Step 1: Extract intent (10 second timeout)
- Step 2: Fetch context (30 second timeout)
- Step 3: Query vector DB (30 second timeout)
- Step 4: Generate LLM response (2 minute timeout)
- Step 5: Update database (10 second timeout)
What's different:
- Each activity is isolated and has its own timeout
- If generate_llm_response takes 90 seconds, that's fine
- If it fails, only that activity retries (not the whole workflow)
- Previous steps' results are already saved
Retries That Actually Make Sense:
Temporal has built-in retry policies per activity. When our OpenAI API call fails, the retry logic:
- First retry: after 1 second
- Second retry: after 2 seconds
- Third retry: after 4 seconds
- Fourth retry: after 8 seconds
- Fifth retry: after 16 seconds
The key difference: Previous activities (extract intent, fetch context) don't re-run. Temporal already has their results saved.
Real-World Comparison: Same Feature, Different Stack
Processing customer chat with human approval workflow
Feature: Process a customer chat message with AI, including human approval for billing disputes
Celery Implementation (What We Had):
Problems we faced:
- Database becomes the source of truth (extra complexity)
- Polling wastes worker time
- No visibility into where workflow is stuck
- If wait_for_approval task fails, we lose the 5 minutes of waiting
- Passing data between tasks is messy
Temporal Implementation (What We Built):
Our workflow now handles:
- Step 1: Extract intent
- Step 2: Check if approval needed
- If billing dispute: Send notification to admin
- Wait for signal (up to 24 hours)
- Step 3: Fetch context
- Step 4: Generate response
Benefits:
- No database state management needed
- No polling (workflows sleep until signaled)
- Full visibility in Temporal UI
- If worker crashes, another picks up exactly where it left off
- Data flows naturally through the workflow
- Easy to test (just Python functions)
Human-in-the-Loop Is Built In:
For sensitive queries requiring approval, our workflow:
- Waits up to 24 hours for human approval
- Resumes automatically when approval signal received
- Handles timeout scenarios gracefully
- No polling, no database flags, no cron jobs
To approve from external system, we simply signal the workflow and it resumes exactly where it left off.
When Temporal Clicks (And When It Doesn't)
Choosing the right tool for your use case
Use Temporal When:
Long-running workflows (minutes to days)
- Our AI chatbot workflows (20 seconds to 2 minutes)
- Data processing pipelines
- Multi-step approval workflows
Reliability is critical
- Financial transactions
- Healthcare data processing
- Customer-facing automation
Complex workflow logic
- Conditional branches
- Human-in-the-loop
- Parallel processing with dependencies
You need visibility
- Debugging production issues
- Understanding why tasks are slow
- Compliance/audit requirements
Stick With Celery When:
Simple, fire-and-forget tasks
- Sending emails
- Generating thumbnails
- Simple cache updates
Your team is already Celery experts
- Migration cost might not be worth it
- Unless you're hitting the pain points we faced
Tasks are independent and fast (<10 seconds)
- Celery's simplicity shines here
- Temporal is overkill
The Migration: How We Actually Did It
A practical approach to switching task systems
We didn't rewrite everything overnight. Here's our approach:
Phase 1: New Features First
- Built new AI chatbot workflows in Temporal
- Ran Temporal alongside existing Celery setup
- Learned the patterns in production
Phase 2: Critical Paths
- Migrated workflows where visibility mattered most
- Payment processing
- Data synchronization with external systems
Phase 3: Everything Else
- Gradually migrated remaining Celery tasks
- Kept simple email/notification tasks in Celery initially
- Eventually moved everything to Temporal for consistency
Timeline: 3 months from first Temporal workflow to full migration.
Getting Started With Temporal:
If you're sold on trying Temporal, here's how to get started:
1. Setup using Docker
2. Install Python SDK: pip install temporalio
3. Create your first workflow
4. Run a worker
5. Start a workflow
That's it. You now have:
- A running Temporal server
- A workflow that executes an activity
- Full visibility in the UI
Before Temporal (with Celery): - 2-3 hours/week debugging "stuck" tasks - Manual database queries to figure out workflow state - Scared to touch production workflows - Worker restarts required every few days
After Temporal: - Open the UI, see exactly what's happening - Workflows survive worker restarts automatically - Confidence to build complex multi-step processes - Sleep better at night
Cost: - Learning curve: ~2 weeks to feel comfortable - Infrastructure: Minimal (Temporal server + same number of workers) - Migration effort: ~200 hours of engineering time
Value: - Reduced debugging time: ~120 hours/year saved - Prevented production incidents: Hard to quantify, but significant - Enabled complex features we couldn't build before - Team velocity increased (easier to reason about workflows)
ROI: Paid for itself in 3 months.
Celery isn't bad. It's great for simple use cases. But if you're building multi-step AI/ML workflows, long-running business processes, or anything where "what's happening right now?" is a critical question, give Temporal a weekend. Build a proof-of-concept. I think you'll have the same "wait, why wasn't I using this before?" moment I had.