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Maxwell Alternatives

So Maxwell is down for a while now and you want to still work on your analyses, here are some alternatives (free compute power) that you can use:

Google Colab​

Pros:

  • Free access to GPUs (T4, P100) and TPUs
  • Jupyter notebook interface
  • You can connect to your Google Drive easily without additional libraries
  • Supports Python, R and Julia
  • Allows custom datasets, and pip/apt installs
  • Pro/Pro+ options offer longer sessions, more resources
  • Supports Gemini AI

Cons:

  • Session timeout after 12 hrs (free), idle timeout ~90 mins, there's a hack for this but I will try to explain it on another post
  • Unstable availability of high-tier GPU
  • Your data will be gone once the session ends (So make sure to connect to your Google Drive)

Verdict:

  • I love Google Colab for quick prototyping and testing my code for analysis, Gemini AI can be helpful sometimes too, the only thing that I hate about it is the timeout thing prevents you from running long process

Kaggle Notebook​

Pros:

  • Free GPU (T4) and 16GB RAM, up to 30 hrs/week
  • Preloaded with many ML/DL libraries
  • Versioning (You can run your job and leave it be without the worry of having timeout like Google Colab)
  • Supports Python and R

Cons:

  • GPU sessions limited to 9 hrs max
  • Fewer customizations than Colab
  • Require additional libraries to connect to external storage

Verdict:

  • I usually go with Kaggle Notebook despite I like Google Colab UI better but Kaggle Notebook has everything you need most of the time

Posit Cloud​

Pros:

  • Designed specifically for R
  • Native RStudio interface — no setup needed
  • Great for RMarkdown, Shiny, Quarto, and teaching
  • Supports CRAN, Bioconductor, and GitHub packages
  • Persistent storage and auto-saving

Cons:

  • No GPU/TPU access
  • Limited compute (1 GB RAM on free tier)
  • Session timeout after 1 hour (free)
  • 15 project-hours/month limit on free tier
  • Very slow for heavy data processing and sometimes it fails

Verdict:

  • Only good for testing R packages on a small-sized dataset because of the fast installation of the packages, otherwise I barely use it