The “should I use n8n or just write Python?” question comes up constantly for AI automation. Both approaches work. The right choice depends on factors that have nothing to do with technical capability.

The Core Trade-off

n8n: Visual workflow builder with prebuilt integrations. Faster to build, easier to maintain, limited flexibility.

Custom Python: Complete control, unlimited flexibility, more development and maintenance overhead.

Neither is universally better. The decision is contextual.

When the Choice is Obvious

Use n8n When

You’re connecting existing services:

• Trigger on Stripe payment → Enrich with LLM → Update CRM
• New Google Form → Classify with AI → Route to Slack channel
• Email received → Summarize → Create Notion page

These “glue” automations are n8n’s sweet spot. Prebuilt connections + AI nodes = done in hours.

Use Python When

You’re building core product features:

• RAG system that’s central to your product
• Custom LLM pipeline with specific requirements
• Anything that needs extensive unit testing

Product features deserve proper software engineering. That means Python (or your language of choice).

Comparison Table

Factor	n8n	Custom Python
Time to first version	Hours	Days
Maintenance burden	Low (visual)	Medium-High
Customization	Limited	Unlimited
Testing	Manual mostly	Full test suite
Version control	JSON exports	Native Git
Debugging	Visual logs	Full debugging
Team skills needed	Low technical	Developer
Cost at scale	Infrastructure	Infrastructure + dev time

Development Speed Comparison

Building a Content Pipeline

Requirements: Take RSS feed, summarize with LLM, post to social media.

n8n approach:

1. Add RSS trigger (2 minutes)
2. Add OpenAI node (5 minutes)
3. Add Twitter/LinkedIn nodes (10 minutes)
4. Configure and test (30 minutes)

Total: ~1 hour

Python approach:

1. Set up project structure (10 minutes)
2. Write RSS parsing (20 minutes)
3. Implement OpenAI integration (30 minutes)
4. Implement social media posting (1 hour)
5. Add scheduling (30 minutes)
6. Handle errors, retries (1 hour)
7. Deploy (30 minutes)

Total: ~4 hours minimum

For this use case, n8n wins 4x on development time.

See the n8n for AI automation tutorial for more examples.

Building a Custom RAG Pipeline

Requirements: Document ingestion, embedding, retrieval with custom reranking, streaming response.

n8n approach:

1. Use AI nodes for embedding (works)
2. Vector store integration (limited options)
3. Custom reranking (need code node)
4. Streaming response (not really supported)

Result: Hitting walls constantly. Code nodes everywhere. Might as well write Python.

Python approach:

1. Design clean architecture (1 day)
2. Implement document processing (1 day)
3. Implement retrieval pipeline (1 day)
4. Add custom reranking (4 hours)
5. Streaming response (4 hours)
6. Testing suite (1 day)

Total: ~4-5 days, production-ready

For complex AI systems, Python enables quality that n8n can’t match.

The building production RAG systems guide covers proper implementation.

Maintenance Considerations

n8n Maintenance

What you manage:

• n8n instance (if self-hosted)
• Workflow versions
• Credential updates

What n8n handles:

• Integration updates (API changes)
• Visual debugging
• Execution history

Maintenance time: Low. Check occasionally, update when integrations break.

Python Maintenance

What you manage:

• All code
• Dependencies
• API client updates
• Infrastructure
• Monitoring

What you get:

• Full control
• Test coverage
• Clear ownership

Maintenance time: Higher. Regular dependency updates, API change handling, monitoring review.

Testing and Quality

n8n Testing

Options:

• Manual execution
• Test runs with sample data
• Error workflow monitoring

Limitations:

• No unit tests
• No mocking
• No CI/CD integration (mostly)

Python Testing

Options:

• Unit tests with pytest
• Integration tests
• Mocking for external services
• Full CI/CD pipeline

Example test structure:

tests/
  unit/
    test_prompt_builder.py
    test_response_parser.py
  integration/
    test_rag_pipeline.py
  e2e/
    test_full_workflow.py

For AI systems where output quality matters, testable Python code is essential.

Team Considerations

n8n Suits

• Solo founders handling operations
• Non-developer team members
• Rapid prototyping phases
• Business operations automations

Python Suits

• Engineering teams
• Products where AI is core
• High-quality requirements
• Systems needing extensive testing

Hybrid Approach

Often the best answer is both:

Use n8n for:

• Triggers and scheduling
• External service integrations
• Simple data transformations
• Monitoring and alerts

Use Python for:

• Core AI logic
• Complex prompt engineering
• Custom models and pipelines
• Business-critical processing

Connect them via:

• n8n HTTP Request to Python API
• Webhooks from Python to n8n triggers

This pattern gives you n8n’s convenience for glue code and Python’s power for the hard parts.

The n8n vs Make comparison covers automation platform choice.

Cost Analysis

n8n Costs

Self-hosted:

• VPS: $20-50/month
• Unlimited executions
• Your time for maintenance

Cloud:

• Based on execution count
• Higher at scale

Python Costs

Infrastructure:

• Similar to n8n self-hosted
• Maybe more for API serving

Development:

• Initial build: Higher
• Maintenance: Ongoing

Hidden costs:

• Developer time is expensive
• Technical debt accumulates

Break-even Analysis

n8n pays off when:

• Workflows are simple enough
• Volume doesn’t justify dev investment
• Team lacks Python capacity

Python pays off when:

• Workflows are complex
• Quality requirements are high
• Team has engineering capacity
• You’d fight n8n’s limitations anyway

Migration Paths

n8n to Python

When to migrate:

• Hitting n8n’s limits constantly
• Need extensive testing
• Workflow complexity exceeds visual management

How:

1. Document current workflow logic
2. Build Python equivalent with tests
3. Run parallel for verification
4. Switch over

Python to n8n

When to migrate:

• Workflow is simpler than expected
• Non-engineers need to maintain
• Developer time needed elsewhere

How:

1. Ensure workflow fits n8n paradigm
2. Build in n8n
3. Verify behavior matches
4. Deprecate Python version

Decision Framework

Start with n8n if:

• Prototyping or MVP
• Connecting existing services
• Non-engineers involved
• Speed to value is priority

Start with Python if:

• Core product feature
• Complex AI logic
• Testing is critical
• Long-term maintenance expected

Plan to migrate when:

• Initial choice doesn’t fit needs
• Team composition changes
• Requirements shift significantly

My Recommendation

Default to n8n for operational automations. The time savings are real, and most operational workflows stay simple enough.

Default to Python for product features. AI systems that users interact with deserve proper engineering.

Don’t mix contexts. n8n for ops, Python for product. Crossing these creates awkward systems that are hard to maintain.

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Building AI automations?

I cover both approaches on the AI Engineering YouTube channel.

Discuss architecture decisions with other engineers in the AI Engineer community on Skool.