Google Colaboratory is a popular platform for data scientists and machine learning scientists, but its limitations and pricing may not meet your needs. Several alternatives offer unique features and capabilities that cater to different data science needs and scenarios.
Follow the links to see the top Google Colab alternatives:
Deepnote for collaborative data visualizations
CoCalc for mathematics-based machine learning and data science
Kaggle Notebooks for learning from data science competitions
Vendor | Free version | Pricing | Reviews** |
---|---|---|---|
Google Colab | ✅ | Starting from $11 per user/month | 4.3 based on 29 reviews. |
Amazon SageMaker | Free trial (2 months) | ~$80 per user/month* | 4.5 based on 39 reviews. |
CoCalc | ✅ | Starting from $0.01/hour and $0.15/hour with a GPU | N/A |
Kaggle Notebooks | ✅ | Free (30 hours/week.) | 4.7 based on 29 reviews. |
Deepnote | ✅ | Starting from $31 per editor/month | 4.5 based on 211 reviews. |
JupyterLab | ✅ | Free | 4.7 based on 289 reviews. |
*Amazon SageMaker had some pricing examples in their website, this amount is based on those estimations. Users can customize their pricing.
**Reviews are based on Capterra and G2.
Why do data scientists prefer cloud-based platforms?
Cloud-based platforms offer scalable and flexible environments for data scientists to work on complex computations and data analysis. To train machine learning models, scientists need powerful hardware like GPUs and CPUs, but this is not always cost-effective.
In that case, switching to a cloud platform is popular among data scientists since they can access powerful computing resources, storage, and collaboration tools easily.
See if you are only interested in free cloud GPU alternatives.
What are the top 5 Google Colab alternatives?
Choosing the suitable GPU provider depends on various criteria, cloud-on prem deployment, usage of AI assistants, supported programming languages are some of them. In Table 2, you can see a comparison of Google Colab with its competitors.
Vendor | Managed | AI assistant | Version history | Programming languages |
---|---|---|---|---|
Google Colab | ✅ | Gemini | No version history | Jupyter languages |
Amazon SageMaker | ✅ | Amazon Q Developer | File-based (Git) | Jupyter languages |
Deepnote | ✅ | GPT-4o | Built-in | Jupyter languages, SQL |
JupyterLabs | ❌ | Jupyter AI (with multiple LLM options) | File-based (Git) | Jupyter languages |
CoCalc | Both are available | Multiple LLM options | Built-in | Jupyter languages |
Kaggle | ✅ | – | Built-in | Jupyter languages |
Also, users should consider whether they work collaboratively, whether they need data visualizations, and their need for math features. The products are strongly varying in those areas. Below, you can read about our experience and suggestions:
Amazon SageMaker
Amazon SageMaker is a fully managed service that provides data scientists with the ability to build, train, and deploy machine learning models.
It offers one-click training and deployment, built-in ML algorithms, and scalability.
SageMaker is ideal for users who want to leverage the power of machine learning without worrying about the underlying infrastructure.
Kaggle Notebooks
Kaggle is a platform that offers a collaborative environment for data scientists and machine learning enthusiasts.
It provides access to a vast repository of datasets, kernels, and notebooks, and supports multiple programming languages.
Kaggle is ideal for users who want to participate in data science competitions, learn from others, and showcase their skills.
Deepnote
Deepnote is a collaborative data science platform that combines a code editor and a computational environment.
It offers real-time collaboration, customizable environments with an easy-to-use interface.
Users can easily make data visualizations.
Provides an AI assistant powered by gpt-4o.
Deepnote is ideal for collaborative working, especially for the teams in need of visualizing data.
CoCalc
CoCalc is a web-based cloud computing and course management platform for computational mathematics.
It offers real-time collaboration, integrated computational tools, and course management features.
With the usage of Jupyter, SageMath, LaTeX, and collaborative Linux terminal, it is suitable for academics, students, and researchers who want to collaborate on projects and learn from each other.
If users want to use AI assistants, they can choose between multiple LLM’s like ChatGPT, Gemini and Mistral, with free and priced options.
JupyterLab
JupyterLab is a next-generation web-based interface for Project Jupyter. It is an open-source platform.
JupyterLab is suitable for users who want a highly customizable and extensible platform for data science and machine learning.
Since JupyterLab uses your local system, you will be using your own hardware, so it is not the best option if you are looking for alternatives for more powerful GPUs.
FAQ
How to choose the best alternative for your data science needs?
When choosing a Google Colab alternative, consider your specific needs and requirements. Think about the type of projects you want to work on, the level of complexity, and the resources you need.
Evaluate the features and capabilities of each platform, and choose the one that best fits your data science experience and goals.
If you are working with a team, consider suitable ones for data science teams who want to enhance productivity, streamline workflows, and achieve success in your team’s data-driven projects.
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