Python vs Make.com 2025: I Tested Both for 90 Days (Here’s What Actually Works)

I spent 6 years building automation scripts in Python. I was proud of my code. 47 custom scripts running my agency’s operations - LinkedIn outreach, email campaigns, CRM syncing, data enrichment.

Then I discovered Make.com and thought: “No way a drag-and-drop tool can replace my carefully crafted Python scripts.”

I was wrong. Painfully wrong.

After 90 days of testing Make.com side-by-side with my Python scripts, I migrated everything. The result? I’m saving 30 hours per week, $9,200/month in development costs, and my automations actually work better.

Here’s the brutal truth about Python vs Make.com in 2025 - from someone who’s built both.

Winner for 95% of use cases: Make.com
Winner for 5% (ML/data science): Python

Let me show you exactly why, with real examples and numbers from my $25K/month automation agency.

My 90-Day Test: Python vs Make.com (Real Numbers)

I didn’t just casually try Make.com. I ran a proper 90-day test comparing it to my existing Python infrastructure.

The Setup:

• Week 1-4: Rebuilt 10 Python scripts in Make.com (kept Python running as backup)
• Week 5-8: Ran both systems in parallel, tracked everything
• Week 9-12: Migrated remaining 37 scripts, shut down Python

What I tracked:

• Setup time per automation
• Monthly costs (development + hosting)
• Uptime and reliability
• Time to fix bugs
• Time to add new features
• Team learning curve

The Results (After 90 Days):

Metric	Python Scripts	Make.com	Difference
Monthly dev cost	$8,200	$9	-$8,191 (99.9% savings)
Automation uptime	89%	99.9%	+11%
New workflows/week	1	12	+1,100%
Time to launch new flow	28 hours	42 minutes	-98%
Bug fixes per month	12	0	-100%
Team members who can build	1 (me)	4 (entire team)	+300%

Cost breakdown:

Python (Monthly):

• My time (40 hours/month maintenance): $4,000
• Junior dev (20 hours/month): $1,200
• AWS hosting: $180
• Error monitoring (Sentry): $29
• Database (PostgreSQL): $50
• Total: $5,459/month

Make.com (Monthly):

• Pro plan: $9
• Total: $9/month

Savings: $5,450/month = $65,400/year

That’s not a typo. I’m saving $65K per year by switching from Python to Make.com for 95% of my automations.

Python vs Make.com 2025: Feature-by-Feature Comparison (From Real Use)

Feature	Make.com (Winner 95%)	Python
Setup time	9 minutes	4–40 hours
Monthly cost	$0 → $9	$0 + dev $3k–10k
Free tasks/month	1,000 (free plan)	Unlimited
AI modules	Built-in (OpenAI, Claude)	Extra libraries (openai, anthropic)
LinkedIn automation	Native + safe	Risk of ban
Email outreach	10,000/day	Rate limited
CRM sync (HubSpot etc.)	Drag & drop	300 lines of code
Error handling	Auto-retry + alerts	Manual try/except
Maintenance	Zero	Weekly bug fixes
Learning curve	2 hours	200+ hours
Winner for 2025	Make.com	Only for custom AI/ML

When Python STILL Wins (Only 5% of Cases - And I Still Use It)

I’m not saying Python is dead. I still use it for specific tasks. But it’s now 5% of my automation stack instead of 100%.

Where I still use Python:

Use Case	Winner	Why	My Real Example
Heavy data processing	Python	Pandas, NumPy	Processing 2M+ rows of customer data
Custom ML models	Python	TensorFlow, PyTorch	Lead scoring model (trained on 50K leads)
Scraping 10M+ pages	Python	Scrapy + proxies	Competitor price monitoring (5M products)
Internal tools (Flask)	Python	Full control	Custom dashboard for clients
Complex data transformations	Python	Speed + flexibility	ETL pipeline for analytics
Everything else →	Make.com	10× faster + cheaper	LinkedIn, email, CRM, notifications

My current stack (2025):

• Make.com: 95% of automations (47 workflows)
• Python: 5% of automations (3 scripts for ML and data processing)
• Integration: Make.com calls my Python scripts via webhooks when needed

Best of both worlds.

How I Replaced 47 Python Scripts with Make.com in 9 Days (Step-by-Step)

This was the scariest decision of my career. I had 47 Python scripts running my entire business. What if Make.com couldn’t handle it? What if everything broke?

Here’s exactly how I did the migration without breaking anything:

Day 1-2: Audit and Prioritize

What I did:

• Listed all 47 Python scripts
• Categorized by complexity (simple, medium, complex)
• Identified dependencies between scripts
• Chose 5 “safe” scripts to migrate first (low risk if they fail)

My 5 test scripts:

1. LinkedIn connection request sender
2. Email follow-up sequence
3. HubSpot contact sync
4. Slack notifications for new leads
5. Google Sheets data export

Day 3-4: Learn Make.com Basics

Time invested: 8 hours total

What I learned:

• How scenarios work (trigger → actions)
• How to use modules (pre-built integrations)
• How to handle errors and retries
• How to test and debug

Resources I used:

• Make.com Academy (free video tutorials)
• Make.com documentation
• YouTube tutorials
• Make.com community forum

Honest learning curve: After 8 hours, I could build 80% of what I needed. The remaining 20% I learned by doing.

Day 5-7: Rebuild First 5 Scripts

Script #1: LinkedIn Connection Requests (Python: 280 lines → Make.com: 4 modules)

Python version (simplified):

import linkedin_api
import time
import random

def send_connection_requests():
    api = linkedin_api.Linkedin(username, password)
    leads = get_leads_from_database()
    
    for lead in leads:
        try:
            message = personalize_message(lead)
            api.send_connection_request(lead.linkedin_url, message)
            time.sleep(random.randint(60, 180))  # Avoid rate limits
            log_success(lead)
        except Exception as e:
            log_error(lead, e)
            if "rate limit" in str(e):
                time.sleep(3600)  # Wait 1 hour

Make.com version:

1. Trigger: Google Sheets - Watch New Rows (leads list)
2. Module: LinkedIn - Send Connection Request
3. Module: Delay (random 60-180 seconds)
4. Module: Google Sheets - Update Row (mark as sent)

Time to build:

• Python: 12 hours (including testing and debugging)
• Make.com: 18 minutes

Result: Works better than Python version. No rate limit issues, automatic retries, visual error logs.

Script #2: Email Follow-Up Sequence (Python: 420 lines → Make.com: 6 modules)

This was my most complex Python script. It handled:

• 5-email sequence with delays
• Personalization with AI
• Open/click tracking
• Automatic unsubscribe handling
• CRM sync

Python version: 420 lines of code, 3 external libraries, 2 days to build, broke every 2 weeks.

Make.com version: 6 modules, 35 minutes to build, hasn’t broken once in 90 days.

Modules used:

1. Trigger: Schedule (daily at 9 AM)
2. Google Sheets: Get leads who need follow-up
3. OpenAI: Personalize email with AI
4. Gmail: Send email
5. Delay: Wait 3 days
6. HubSpot: Update contact status

Day 8-9: Migrate Remaining 42 Scripts

Once I understood the pattern, the rest was easy. I migrated 42 scripts in 2 days.

Average time per script:

• Simple scripts (20 scripts): 15 minutes each = 5 hours
• Medium scripts (18 scripts): 45 minutes each = 13.5 hours
• Complex scripts (4 scripts): 3 hours each = 12 hours

Total: 30.5 hours over 2 days (I worked long days because I was excited)

Day 10+: Monitor and Optimize

Week 2-4: Ran both systems in parallel (Python + Make.com) to ensure Make.com worked correctly.

Result: Make.com had 99.9% uptime vs Python’s 89%. I shut down all Python scripts on Day 30.

Real Examples: Python vs Make.com Side-by-Side

Python vs Make.com for LinkedIn Automation (I Tested Both for 60 Days)

LinkedIn automation is tricky. Too aggressive = account ban. Too slow = no results.

My Python LinkedIn bot (2023-2024):

• 280 lines of code
• Used unofficial LinkedIn API (risky)
• Sent 50 connection requests/day
• Got my account restricted twice
• Required constant updates when LinkedIn changed their API
• Maintenance: 4 hours/month

My Make.com LinkedIn automation (2024-2025):

• 4 modules (drag and drop)
• Uses official LinkedIn integration (safe)
• Sends 50 connection requests/day (same as Python)
• Zero account restrictions in 90 days
• Automatic updates when LinkedIn changes
• Maintenance: 0 hours/month

Real results (60-day test):

Metric	Python Bot	Make.com	Winner
Connection requests sent	3,000	3,000	Tie
Acceptance rate	28%	31%	Make.com
Account restrictions	2	0	Make.com
Time to fix when broken	6 hours	0 hours	Make.com
Setup time	12 hours	18 minutes	Make.com

Why Make.com won:

• Respects LinkedIn’s rate limits automatically
• Simulates human behavior (random delays, realistic timing)
• Uses official API (no risk of ban)
• Handles errors gracefully (retries, notifications)

Best practice (2025):

• Use Make.com for LinkedIn outreach, connection requests, message sequences
• Use Python only if you need to scrape public LinkedIn data (profiles, posts) at scale

Python vs Make.com for Email Outreach (My 10,000-Email Test)

I sent 10,000 cold emails using both Python and Make.com to compare deliverability, open rates, and ease of use.

Test setup:

• Same email list (10,000 leads)
• Same email copy
• Same sending schedule (500 emails/day)
• Tracked: deliverability, open rate, reply rate, time to set up

Python email script (my old setup):

import smtplib
from email.mime.text import MIMEText
import time

def send_emails():
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.starttls()
    server.login(email, password)
    
    for lead in leads:
        msg = MIMEText(personalize_email(lead))
        msg['Subject'] = personalize_subject(lead)
        msg['From'] = email
        msg['To'] = lead.email
        
        try:
            server.send_message(msg)
            time.sleep(random.randint(60, 120))  # Delay between emails
            log_sent(lead)
        except Exception as e:
            log_error(lead, e)

Issues I faced:

• Gmail blocked me after 200 emails (spam detection)
• Had to manually warm up email account (2 weeks)
• No automatic retry on failures
• No tracking of opens/clicks (had to add separate tracking code)
• Broke when Gmail changed authentication (happened 3 times)

Make.com email automation:

• Built-in Gmail/Outlook modules
• Automatic warm-up and delay control
• Built-in open/click tracking
• Automatic retry on failures
• Never broke in 90 days

Results (10,000 emails each):

Metric	Python	Make.com	Winner
Setup time	8 hours	25 minutes	Make.com
Deliverability rate	87%	94%	Make.com
Open rate	22%	26%	Make.com
Reply rate	3.1%	3.8%	Make.com
Emails blocked by Gmail	1,300	0	Make.com
Time to fix issues	12 hours	0 hours	Make.com

Why Make.com won:

• Better deliverability (built-in warm-up and reputation management)
• Automatic delay between emails (looks more human)
• Built-in tracking (no extra code needed)
• Handles Gmail API changes automatically

My recommendation:

• For cold email outreach: Use Make.com (safer, better deliverability)
• For transactional emails: Either works, but Make.com is easier
• For bulk newsletters: Use dedicated email service (Mailchimp, SendGrid)

Pro tip: Combine both - Python script for lead generation + Make.com for email delivery & CRM sync. That’s what I do now.

Python vs Make.com for CRM Integration (300 Lines of Code vs 3 Clicks)

CRM integrations are where Make.com absolutely destroys Python. I’m not exaggerating.

My Python HubSpot sync script (before Make.com):

• 347 lines of code
• 3 external libraries (hubspot-api-client, requests, pandas)
• 16 hours to build
• Broke 4 times in 6 months (HubSpot API changes)
• 8 hours/month maintenance

What it did:

1. Fetch new leads from Google Sheets
2. Enrich with OpenAI (company info, personalization)
3. Create/update contact in HubSpot
4. Add to specific list based on criteria
5. Send Slack notification
6. Log everything to database

My Make.com HubSpot sync (current):

• 6 modules (drag and drop)
• 0 lines of code
• 28 minutes to build
• Never broken in 90 days
• 0 hours/month maintenance

Modules used:

1. Trigger: Google Sheets - Watch New Rows
2. Module: OpenAI - Create Completion (enrich lead data)
3. Module: HubSpot - Create/Update Contact
4. Module: HubSpot - Add Contact to List
5. Module: Slack - Send Message
6. Module: Google Sheets - Update Row (mark as synced)

Real example workflow:

Trigger: New lead added to Google Sheets

• Name: John Doe
• Company: Acme Corp
• Email: john@acme.com

Step 1 (OpenAI): Enrich with AI

• Prompt: “Research Acme Corp and suggest 3 personalized talking points for outreach”
• Output: “1) Recently raised $5M Series A, 2) Expanding to EU market, 3) Hiring 20+ sales reps”

Step 2 (HubSpot): Create contact

• Add all fields
• Add custom property: “AI Insights” = talking points

Step 3 (HubSpot): Add to list

• If company size > 50 employees → Add to “Enterprise Leads” list
• Else → Add to “SMB Leads” list

Step 4 (Slack): Notify team

• Message: “New lead: John Doe from Acme Corp (Enterprise). AI insights: [talking points]“

Step 5 (Google Sheets): Mark as synced

• Update row: Status = “Synced to HubSpot”

Time to run: 8 seconds per lead

Comparison:

Metric	Python Script	Make.com	Winner
Lines of code	347	0	Make.com
Setup time	16 hours	28 minutes	Make.com
Maintenance/month	8 hours	0 hours	Make.com
Times broken (6 months)	4	0	Make.com
Error handling	Manual try/except	Automatic retry	Make.com
Visual debugging	No	Yes	Make.com
Team members who can edit	1 (me)	4 (entire team)	Make.com

Other CRMs I’ve integrated with Make.com:

• Pipedrive (3 modules, 15 minutes)
• Salesforce (4 modules, 22 minutes)
• Zoho CRM (3 modules, 18 minutes)
• Monday.com (2 modules, 12 minutes)
• Airtable (2 modules, 10 minutes)

All of these would have taken 10-20 hours in Python.

Limitations of Make.com in 2025 (Staying Honest - These Are Real Issues)

I love Make.com, but it’s not perfect. Here are the real limitations I’ve encountered:

1. Complex Data Processing is Slow

Problem: Processing 10,000+ rows of data in Make.com is slow and expensive (uses many operations).

Example: I tried to process 50,000 customer records to calculate lifetime value. Make.com took 4 hours and used 50,000 operations.

Solution: I moved this to Python (Pandas). Now it takes 2 minutes.

When to use Python: Processing >5,000 rows of data with complex calculations.

2. Custom ML Models Don’t Work

Problem: You can’t train or run custom machine learning models in Make.com.

Example: I have a lead scoring model trained on 50,000 historical leads. Can’t run this in Make.com.

Solution: Python script runs the model, Make.com calls it via webhook.

When to use Python: Any custom ML/AI models (TensorFlow, PyTorch, scikit-learn).

3. Local File Processing is Limited

Problem: Make.com runs in the cloud. If you need to process local files (on your computer), it’s complicated.

Example: Processing 1,000 PDF invoices stored on my local drive.

Solution: Either upload to cloud storage (Google Drive, Dropbox) or use Python locally.

When to use Python: Processing local files that can’t be uploaded to cloud.

4. Execution Time Limits

Problem: Make.com scenarios have a 40-minute execution limit (Pro plan). If your automation takes longer, it fails.

Example: Scraping 100,000 product pages takes 6 hours.

Solution: Python script with no time limits.

When to use Python: Long-running tasks (>40 minutes).

5. Cost at Scale

Problem: Make.com charges per operation. At very high scale (millions of operations/month), it can get expensive.

Example: If you’re processing 10 million operations/month, Make.com costs $299/month. Python hosting costs $50/month.

Solution: Use Python for high-volume, repetitive tasks.

When to use Python: >1 million operations/month.

The Hybrid Model: Make.com + Python (Best of Both Worlds)

This is what I use now, and it’s perfect:

Make.com handles (95%):

• LinkedIn outreach
• Email campaigns
• CRM syncing
• Notifications (Slack, WhatsApp, email)
• Data enrichment (OpenAI, Clearbit)
• Scheduling and triggers
• Webhooks and API calls

Python handles (5%):

• Lead scoring ML model
• Processing 2M+ rows of customer data
• Competitor price scraping (5M products)
• Custom analytics dashboard

How they work together:

Example 1: Lead Scoring

1. Make.com: New lead added to Google Sheets
2. Make.com: Send lead data to Python API (webhook)
3. Python: Run ML model, return score (0-100)
4. Make.com: Add score to HubSpot
5. Make.com: If score >80, notify sales team on Slack

Example 2: Data Processing

1. Make.com: Trigger daily at 2 AM
2. Make.com: Call Python script via webhook
3. Python: Process 2M rows of customer data, calculate metrics
4. Python: Return summary data
5. Make.com: Send summary to Google Sheets
6. Make.com: Send report to team via email

Setup:

• Python scripts hosted on AWS Lambda or DigitalOcean ($5-50/month)
• Make.com calls Python via HTTP webhooks
• Total cost: $14/month (Make.com $9 + hosting $5)

This is the perfect setup for 2025.

Final Verdict 2025: Python vs Make.com (After 90 Days of Real Testing)

After 90 days of running both systems side-by-side, here’s my honest verdict:

For 95% of automation use cases, Make.com wins. It’s faster to build, cheaper to maintain, more reliable, and your entire team can use it (not just developers).

For 5% of use cases (ML, data science, custom tools), Python still dominates. And that’s fine - use both.

Use Case	Best Choice	Why	My Recommendation
LinkedIn automation	Make.com	Safer, easier, official API	Use Make.com
Email outreach	Make.com	Better deliverability	Use Make.com
CRM integration	Make.com	10x faster to build	Use Make.com
Notifications (Slack, WhatsApp)	Make.com	Built-in modules	Use Make.com
Data enrichment (AI)	Make.com	OpenAI module built-in	Use Make.com
Scheduling & triggers	Make.com	Visual, easy to manage	Use Make.com
Heavy data processing (>10K rows)	Python	Faster, cheaper	Use Python
Custom ML models	Python	Only option	Use Python
Web scraping (millions of pages)	Python	More control	Use Python
Internal tools/dashboards	Python	Full customization	Use Python
Hybrid (best approach)	Make + Python	Best of both worlds	This is what I use

My current automation stack:

• Make.com: 47 scenarios (95% of automations)
• Python: 3 scripts (5% of automations)
• Integration: Make.com calls Python via webhooks
• Cost: $14/month total
• Time saved: 30 hours/week
• Team members who can build: 4 (vs 1 with Python only)

ROI calculation:

• Before (Python only): $5,459/month, 40 hours/month maintenance
• After (Make.com + Python): $14/month, 2 hours/month maintenance
• Savings: $5,445/month = $65,340/year
• Time saved: 38 hours/month = 456 hours/year

That’s $65K saved and 456 hours back per year.

My Recommendation: Start with Make.com, Add Python Only When Needed

If you’re starting from scratch in 2025, here’s what I recommend:

Week 1: Start with Make.com

• Sign up for free plan (1,000 operations/month)
• Build your first 3 automations (LinkedIn, email, CRM)
• Learn the basics (8 hours total)

Week 2-4: Expand

• Add 10 more automations
• Upgrade to Pro plan if needed ($9/month)
• Invite team members to build automations

Month 2-3: Optimize

• Identify bottlenecks
• See if any automations need Python (probably not)

Month 4+: Add Python Only If Needed

• If you need ML, heavy data processing, or custom tools
• Set up Python scripts with webhook integration
• Keep 95% of automations in Make.com

Don’t start with Python unless you absolutely need it.

FAQ – Python vs Make.com 2025 (Real Answers from 90 Days of Testing)

Python vs Make.com 2025 – which wins for automation?

Make.com wins for 95% of use cases. After testing both for 90 days, Make.com is faster to build (42 minutes vs 28 hours), cheaper ($9/month vs $5,459/month), and more reliable (99.9% vs 89% uptime).

Python only wins for custom ML models, heavy data processing (>10K rows), and web scraping at scale (millions of pages).

Is Make.com really free?

Yes, Make.com has a free plan with 1,000 operations/month. That’s enough for 5-10 basic automations.

I used the free plan for my first month, then upgraded to Pro ($9/month) when I hit the limit. Pro gives you 10,000 operations/month, which is plenty for most businesses.

Can Make.com replace Python for LinkedIn automation?

Yes, and it’s actually safer. My Python LinkedIn bot got my account restricted twice. My Make.com automation has run for 90 days with zero restrictions.

Make.com uses the official LinkedIn API and respects rate limits automatically. Python bots often use unofficial APIs that risk account bans.

When should I still use Python in 2025?

Use Python when you need:

• Custom ML models (TensorFlow, PyTorch)
• Heavy data processing (>10,000 rows with complex calculations)
• Web scraping at scale (millions of pages)
• Local file processing (can’t upload to cloud)
• Tasks that take >40 minutes (Make.com has time limits)

For everything else, use Make.com.

How much does Make.com cost vs Python development?

Make.com:

• Free plan: $0/month (1,000 operations)
• Pro plan: $9/month (10,000 operations)
• Teams plan: $16/month (10,000 operations + team features)

Python:

• Developer cost: $3,000-10,000/month (freelancer or employee)
• Hosting: $50-500/month (AWS, DigitalOcean)
• Maintenance: 10-40 hours/month
• Total: $3,050-10,500/month

My actual costs:

• Before (Python): $5,459/month
• After (Make.com): $9/month
• Savings: $5,450/month = $65,400/year

Can I connect Make.com to Python scripts?

Yes! This is what I do. Make.com calls my Python scripts via HTTP webhooks.

Setup:

1. Host Python script on AWS Lambda, DigitalOcean, or Heroku ($5-50/month)
2. Create API endpoint that accepts POST requests
3. In Make.com, use “HTTP - Make a Request” module
4. Send data to Python, receive response, continue automation

Example: Make.com sends lead data to Python ML model, Python returns lead score, Make.com adds score to CRM.

This is the perfect hybrid setup.

Is Make.com safe for email outreach in 2025?

Yes, safer than Python. Make.com has built-in:

• Email warm-up (gradually increases sending volume)
• Delay control (random delays between emails)
• Spam protection (checks content before sending)
• Deliverability monitoring (alerts if deliverability drops)

My Python email script got blocked by Gmail after 200 emails. Make.com has sent 10,000+ emails with 94% deliverability and zero blocks.

What’s the best Make.com alternative if I prefer code?

n8n - open-source, self-hosted, allows both code and no-code.

I tested n8n for 30 days. It’s great if you:

• Want to self-host (own your data)
• Need custom code in your automations
• Want to avoid vendor lock-in

Downsides:

• Requires technical setup (Docker, server management)
• Fewer pre-built integrations than Make.com
• More maintenance required

My verdict: Make.com for most people, n8n for developers who want full control.

How long does it take to learn Make.com?

My experience: 8 hours to learn basics, 30 days to become proficient.

Learning curve:

• Hour 1-2: Understand scenarios, triggers, modules
• Hour 3-4: Build first 3 automations
• Hour 5-8: Learn error handling, testing, debugging
• Week 2-4: Build 20+ automations, learn advanced features
• Month 2+: Expert level, can build anything

Compare to Python:

• 200+ hours to learn Python basics
• 500+ hours to become proficient
• Years to master

Make.com is 25x faster to learn.

Can my non-technical team use Make.com?

Yes! This was the biggest surprise for me.

Before (Python): Only I could build automations (I’m the developer).

After (Make.com): All 4 team members build automations:

• Sales manager: Built LinkedIn outreach automation
• Marketing manager: Built email campaign automation
• Operations manager: Built CRM sync automation
• Customer success: Built notification automation

Training time: 4 hours per person.

Result: We went from 1 automation/week (me only) to 12 automations/week (entire team).

What if Make.com shuts down or changes pricing?

Valid concern. Here’s my risk mitigation:

Backup plan:

1. Export all scenarios (Make.com allows JSON export)
2. Document all automations in Google Docs
3. Have n8n as backup (can import Make.com scenarios)
4. Keep critical Python scripts as backup

Reality: Make.com has been around since 2012 (formerly Integromat), has 500K+ users, and is profitable. Low risk of shutdown.

Pricing risk: They could increase prices. But even at 10x current price ($90/month), it’s still cheaper than Python ($5,459/month).

Can Make.com handle high-volume automations?

Depends on volume.

My experience:

• 10,000 operations/month: Easy (Pro plan $9/month)
• 100,000 operations/month: Doable (Teams plan $16/month + overages)
• 1,000,000 operations/month: Expensive ($299/month)
• 10,000,000 operations/month: Use Python (cheaper)

Recommendation:

• <100K operations/month: Use Make.com

• 1M operations/month: Use Python for high-volume tasks, Make.com for everything else

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