Google Colab Alternatives

If you’re reading this, there’s a good chance, that just like me, you started using Google Colab for personal projects and quick experiments, and it was great. Free GPU access, no setup headaches, and everything just worked with Google Drive. Perfect for solo work and learning new techniques.

But now your team has grown, and what once felt simple has become frustrating. Your colleague just accidentally overwrote your analysis because Colab's "collaboration" is really just Google Docs-style sharing. You lost three hours of work to a session timeout yesterday, right before the team meeting. And you're tired of constantly switching between ChatGPT and your notebook, copying code back and forth, when you just want to explore your data naturally.

Despite its name suggesting collaboration, Google Colab was built for individual learning and experimentation, not team data work, and lacks functionality that truly would make it a world-class data analysis experience.

If this sounds familiar, you're definitely not alone. Lots of new platforms have popped up trying to solve these problems. Let's look at what's actually out there and what works for different situations.

Best Google Colab alternatives by use case

The classics

Jupyter/JupyterLab Still solid if you like running things locally. JupyterLab has tons of plugins and you can customize everything. The downside? Getting everyone on your team set up with the same environment is a pain, and sharing work means emailing notebook files around like it's 2010.

Kaggle Notebooks Great for learning and competitions. You get free GPUs and access to tons of datasets. But it's more like a playground than a serious work environment—limited storage, basic sharing, and you can't really customize much.

Paperspace Gradient Decent GPU machines and pre-built environments. Some people love it, but others run into reliability issues. Plus it's more about renting compute power than having a complete workspace.

Databricks Notebooks / Snowflake Notebooks If you're already using Databricks or Snowflake for your data warehouse, their notebook features are convenient since everything connects directly to your data. But honestly, they're just traditional notebooks with some integrations—no reactivity between cells, basic AI assistance at best, and you can't turn your analysis into a dashboard without exporting and rebuilding in another tool. Think of them as Colab that lives inside your data platform.

The team-focused ones

Deepnote This one's built for teams working together. Real-time editing actually works, and it looks pretty nice. They have decent SQL support and sharing is straightforward. Can get slow with bigger datasets though, and the AI help, while useful, doesn't feel deeply integrated into how you actually work.

Amazon SageMaker AWS's machine learning platform. It's comprehensive but honestly pretty complex. If your company is already all-in on AWS, it might make sense. Otherwise, prepare for a steep learning curve and bills that can get expensive fast.

CoCalc Originally made for math and education. Works well for academic stuff and has good real-time collaboration, but the interface feels old and it's missing a lot of the modern data science tools teams expect.

AI-powered notebooks: Fabi.ai Smartbooks

While the platforms above try to fix some of Colab's problems, Fabi.ai Smartbooks takes a different approach entirely. Instead of starting with traditional notebooks and adding features, we’ve rebuilt the whole thing from scratch.

AI that actually gets it Most tools just add generative AI as an afterthought, glorified autocomplete that doesn't understand your workflow. Real AI integration means the LLM understands your data structure, variable context, and can suggest meaningful next steps, not just complete syntax. Fabi’s Analyst Agent actually understands your data structure, remembers what variables you've created, and can help with both Python and SQL. It's more like having a smart colleague who knows your dataset than just a fancy autocomplete.

Real collaboration, not just sharing When multiple people work on the same analysis, Smartbooks figures out how to merge changes without breaking everything. You can see what your teammate changed and why, not just that something's different. No more "wait, did you save over my work?"

SQL and Python together You can write SQL to pull data, then immediately work with that data in Python, all in the same place. No exporting CSV files or juggling different tools or libraries. It actually feels natural instead of like you're fighting with your tools.

From analysis to actually doing something Here's what's cool: when you finish your analysis, you can turn it directly into a dashboard or set it up to run automatically and send results to Slack or email. Your work doesn't just sit in a notebook file somewhere, it becomes useful to other people.

Real results Teams using Smartbooks see big improvements quickly. Hologram cut their analysis time by 94%. Their analyst went from taking days to answer customer questions to doing it in 30 minutes during sales calls. That's the kind of difference that actually matters for business.

How to pick what works for you

Stick with the basics if:

• You're learning or working solo: Jupyter/JupyterLab is still great for personal projects
• You're into competitions: Kaggle Notebooks has everything you need for data science competitions
• Your company is AWS-heavy: SageMaker makes sense if you're already using lots of AWS services
• You're in academia: CoCalc's math focus and educational features work well for research

Consider something more if:

• Your team is growing: When sharing notebooks becomes a pain and you need real collaboration
• You're juggling too many tools: Switching between SQL tools, Python environments, and ChatGPT is slowing you down
• Business people need answers fast: When stakeholders ask questions and you can't respond quickly
• Your analyses need to become repeatable: When one-off explorations need to turn into regular reports or dashboards

Why teams like Fabi.ai

Smartbooks work well when teams want to move fast without sacrificing quality. The main things that make people switch:

• No more losing work to crashes or timeouts
• Stop switching between different tools for SQL and Python
• Actually collaborate instead of just sharing files and convert your work to dashboards or automated AI-powered workflows
• Get help from AI that understands what you're trying to do

The AI goes beyond just writing code, it helps you think through problems and suggests what to try next.

Getting started

Most of these platforms let you test them out for free, so you can see what actually works for your situation. The good ones usually pay for themselves pretty quickly just from the time you save.

Data science tools keep getting better. Google Colab was great for getting people started with cloud notebooks, especially for learning and personal projects. But as teams grow and need to work together more, you need tools that were actually built for collaboration and modern workflows.

The main thing is finding something that helps you get stuff done instead of fighting with it. The right tool should make your work easier, not harder.

Want to see what it's like when AI actually understands your data work? You can try Fabi.ai for free right now—takes just a couple minutes to get started.