The Day My Python Script Outperformed a Paid SaaS Tool

The Day My Python Script Outperformed a Paid SaaS Tool.

A few months ago, I found myself in a situation every developer secretly dreams of: my scrappy little Python script outperformed a polished, shiny SaaS tool that someone was charging $49/month for.

Now, before you think this is going to be one of those “I built my own Notion in a weekend” stories (spoiler: I did not), let me set the stage.

I wasn’t trying to disrupt a market. I wasn’t even trying to “build a startup.” I just had a problem. And that’s always where the best projects start.

The Problem That Triggered It All I was working on a client task that involved scraping and cleaning product data from multiple suppliers. The SaaS tool I was paying for promised:

Automated data extraction
Cleaning + deduplication
CSV export

Sounds good on paper, right? Except it was painfully slow, choked on non-English characters, and had a nasty habit of silently skipping rows without telling me. Imagine reviewing a spreadsheet and realizing you’re missing 20% of the data. Not fun. So, I had two options:

Keep paying for the SaaS and hope their support team eventually fixed it.
Roll up my sleeves and automate it myself with Python.

You already know which option I chose. The Script That Replaced $49/Month The SaaS tool was basically three things in disguise:

Scraper Grab the raw data
Cleaner Standardize and fix formatting
Exporter Package it into a friendly format

I realized I didn’t need 10 dashboards, a Chrome extension, and an onboarding video to do that. I just needed the right libraries. Here’s what my stack looked like:

requests + BeautifulSoup Fetch + parse supplier data
pandas Clean, deduplicate, and restructure
openpyxl Export to Excel for clients

And here’s the core cleaning snippet that became the heart of my replacement:

import pandas as pd
# Load raw data
df = pd.read_csv("supplier_data.csv")
# Clean step: drop duplicates, fill missing values
df = df.drop_duplicates()
df["price"] = df["price"].fillna(0)
# Normalize column names
df.columns = [col.strip().lower().replace(" ", "_") for col in df.columns]
# Export to Excel
df.to_excel("cleaned_data.xlsx", index=False)

That’s it. Fourteen lines of Python. The SaaS tool? It had taken ~15 minutes per dataset. My script chewed through the same file in under 10 seconds.

Where the Script Won To be fair, SaaS tools usually come with nice UIs, customer support, and “no code required” vibes. My script? It had none of that. It wasn’t pretty. It required me to open VS Code, not a browser.

But here’s the thing:

Speed My script was 90x faster.
Transparency If something broke, I knew exactly why.
Cost Free (aside from caffeine).

And once I built it, I could reuse it. The SaaS tool didn’t scale with me; my script did. The Hidden Lesson

At first, I felt like I had just “saved $49.” But it hit me later that the real win wasn’t financial it was confidence.

I proved to myself that I could replicate (and improve on) a product people were literally paying for. That’s a shift in mindset. Suddenly, I stopped looking at SaaS tools as magical black boxes and started seeing them as “things I could probably build on a weekend.”

“Software is just someone else’s script wrapped in a UI.”

Pro Tip for You If you ever catch yourself paying monthly for a tool that frustrates you pause and ask:

What’s the actual problem it’s solving?
Could a Python script + a few libraries handle this instead?

Time-box it. Give yourself a weekend. Worst case? You learn something new. Best case? You build something faster, smarter, and reusable.

Read the full article here: https://medium.com/pythoneers/the-day-my-python-script-outperformed-a-paid-saas-tool-2a229e641ff5