I used to pay for 7 different SaaS tools. Then I discovered that Python libraries do the same thing for free.
Here's every subscription I cancelled and what replaced it.
1. Pandas → Replaced Excel ($14/mo)
I was paying for Microsoft 365 mainly for Excel. Pandas does everything Excel does, but programmable.
import pandas as pd
# Read any format
df = pd.read_csv("sales.csv") # or .xlsx, .json, .sql, .html
# Pivot tables
pivot = df.pivot_table(values="revenue", index="month", columns="product", aggfunc="sum")
# VLOOKUP equivalent
merged = pd.merge(orders, customers, on="customer_id")
Saved: $168/year
2. BeautifulSoup + Requests → Replaced ScrapingBee ($49/mo)
import requests
from bs4 import BeautifulSoup
resp = requests.get("https://example.com")
soup = BeautifulSoup(resp.text, "html.parser")
prices = [el.text for el in soup.select(".price")]
For 90% of scraping tasks, you don't need a paid service.
Saved: $588/year
3. Matplotlib + Seaborn → Replaced Tableau ($75/mo)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.lineplot(data=df, x="date", y="revenue", ax=axes[0])
sns.barplot(data=df, x="product", y="sales", ax=axes[1])
plt.savefig("dashboard.png", dpi=150)
Saved: $900/year
4. Scikit-learn → Replaced DataRobot ($400/mo)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2%}")
Saved: $4,800/year
5. Schedule + APScheduler → Replaced Zapier ($20/mo)
import schedule
import time
def check_prices():
# Your automation logic here
pass
schedule.every(1).hour.do(check_prices)
schedule.every().monday.at("09:00").do(send_report)
while True:
schedule.run_pending()
time.sleep(60)
Saved: $240/year
6. NLTK + spaCy → Replaced MonkeyLearn ($200/mo)
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")
for ent in doc.ents:
print(f"{ent.text} → {ent.label_}")
# Apple → ORG | U.K. → GPE | $1 billion → MONEY
Saved: $2,400/year
7. SEC EDGAR API → Replaced Yahoo Finance Premium ($50/mo)
import requests
headers = {"User-Agent": "MyApp ([email protected])"}
url = "https://data.sec.gov/api/xbrl/companyfacts/CIK0001318605.json"
data = requests.get(url, headers=headers).json()
Saved: $600/year
Total Savings: $9,696/year
| Paid Tool | Cost | Python Replacement | Cost |
|---|---|---|---|
| Excel (M365) | $168/yr | Pandas | $0 |
| ScrapingBee | $588/yr | BeautifulSoup | $0 |
| Tableau | $900/yr | Matplotlib + Seaborn | $0 |
| DataRobot | $4,800/yr | Scikit-learn | $0 |
| Zapier | $240/yr | Schedule | $0 |
| MonkeyLearn | $2,400/yr | spaCy + NLTK | $0 |
| Yahoo Finance | $600/yr | SEC EDGAR API | $0 |
| Total | $9,696/yr | $0 |
Obviously there's a time investment to learn these libraries. But once you do, you never go back.
What paid tool have you replaced with a free alternative?
I'm always looking for more swaps.
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