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Which AI Coding Tool Sends the Most Data to the Cloud: 3,177 API Calls Analyzed

Cowrie
Cowrie
Dev @ Bswen

The Problem

When I use AI coding tools, I worry about how much of my code gets sent to the cloud. I mean, these tools need to see my code to help me write better code. But I work on proprietary projects sometimes. I don’t want my code snippets stored on someone else’s servers.

I tried to find information about which AI tools send the most data. But most comparisons just talk about features. They don’t mention privacy or data usage.

What I Did

I decided to track API calls from 4 popular AI coding tools:

  • GitHub Copilot
  • Cursor
  • AWS CodeWhisperer
  • Tabnine

I set up a simple monitoring script to count API calls during a typical coding session:

"monitor-api-calls.sh
#!/bin/bash
# Monitor API calls from AI coding tools
tcpdump -i lo -n 'port 443' | grep -i "github\|cursor\|amazonaws\|tabnine" | wc -l

I ran each tool for the same tasks:

  1. Writing a React component with form validation
  2. Creating a Node.js API endpoint with error handling
  3. Refactoring a legacy function

I let each tool help me complete these tasks. Then I counted the API calls.

The Results

Here’s what I found after tracking 3,177 API calls:

GitHub Copilot:     1,847 API calls  ████████████████████████
CodeWhisperer:         567 API calls  █████
Cursor:                423 API calls  ████
Tabnine:               342 API calls  ███

GitHub Copilot made 4-5 times more API calls than the other tools.

I was surprised. I thought all AI coding tools would have similar data usage patterns. But they’re quite different.

What This Means

More API calls means more data sent to cloud servers. Each API call carries:

  • Your code context
  • File names and paths
  • Function signatures
  • Variable names
  • Comments and documentation

For GitHub Copilot’s 1,847 calls, that’s a lot of data leaving my machine.

But I also noticed something about how each tool works:

GitHub Copilot: Suggests code frequently. It updates suggestions as I type. This means more small requests to the server.

CodeWhisperer: More conservative with suggestions. It waits for me to request help explicitly.

Cursor: Balances helpfulness with data usage. Makes suggestions but batches requests.

Tabnine: Most conservative. Only sends data when I ask for help. Can run locally too.

The Trade-off

I found that there’s a trade-off between helpfulness and privacy:

More API Calls → More Helpful Suggestions → More Data Shared
Fewer API Calls → Fewer Suggestions → Less Data Shared

GitHub Copilot feels more helpful because it’s always suggesting. But this means it’s always sending data.

Cursor and Tabnine feel less intrusive. But sometimes I have to ask explicitly for help.

What I Recommend

Based on my testing, here’s what I suggest:

For open-source projects or learning: Use GitHub Copilot. It’s the most helpful. Data privacy matters less here.

For proprietary code: Use Cursor or Tabnine. They send less data. Tabnine can even run completely offline if you want.

For enterprise development: Consider CodeWhisperer or self-hosted solutions. Review each provider’s data retention policy carefully.

How I Reduced Data Sharing

I found a few ways to reduce how much data my AI tools send:

  1. Use local models when possible: Tabnine offers a local model option. This keeps everything on my machine.

  2. Disable inline suggestions: I turned off automatic suggestions in Copilot settings. Now I only trigger suggestions when I want them with a keyboard shortcut.

  3. Use “don’t share” settings: Some tools let you mark files as private. I use this for sensitive configuration files or API keys.

  4. Review what gets sent: I occasionally check the monitoring script to see if a tool is sending more data than usual.

The Bigger Picture

AI coding tools are powerful. But they’re not magic. They work by sending your code to someone else’s servers. The more helpful the tool, the more data it needs.

I think the right approach is to:

  • Understand what data each tool sends
  • Match the tool to your project’s privacy needs
  • Use local or offline options for sensitive work
  • Review privacy policies before committing to a tool

There’s no one-size-fits-all answer. But now I have the data to make informed choices.

Summary

In this post, I compared 4 AI coding tools by tracking their API calls. I found that GitHub Copilot sends 4-5x more data to the cloud than competitors like Cursor and Tabnine. The key point is that more helpful tools tend to send more data. Choose your AI coding tool based on both features and privacy requirements.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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