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:
CoCalc for mathematics-based machine learning and data science
Deepnote for collaborative data visualizations
Kaggle Notebooks for learning from data science competitions
- Lightning AI
- Modal
- Paperspace Gradient
- RunPod
- Vast.ai
Vendor | Free version | Pricing |
|---|---|---|
Google Colab | ✅ | Starting from $11 per user/month |
Amazon SageMaker | Free trial (2 months) | Pay-as-you-go |
CoCalc | ✅ | Pay-as-you-go |
Deepnote | ✅ | Starting from $39 per editor/month |
JupyterLab | ✅ | Free |
Kaggle Notebooks | ✅ | Free (30 hours/week) |
Lightning AI | ✅ | 15 free Lightning credits/month; Pro from $50/month |
Modal | ✅ | $30 free credits/month; Pay-as-you-go after |
Paperspace Gradient | ✅ | Free tier with 6-hour sessions; paid plans for longer runtimes and faster GPUs |
RunPod | ❌ | Varies by GPU |
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 10 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.
Also, users should consider whether they work collaboratively, whether they need data visualizations, and their need for math features. The products vary strongly 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.
Paperspace Gradient
Paperspace Gradient is a cloud platform built specifically for machine learning, offering both notebook environments and end-to-end MLOps capabilities.
- It provides access to a range of GPUs from the free tier (M4000, RTX4000) up to A100s for demanding workloads.
- Gradient includes pre-configured ML environments, one-click deployments, and workflow automation.
- Paperspace is ideal for users who need more powerful GPUs than Colab’s free tier offers, with straightforward per-hour pricing and no surprise disconnections.
Lightning AI
Lightning AI is a development platform created by the team behind PyTorch Lightning, designed to streamline the ML development lifecycle.
- It offers Lightning Studios. It is a cloud-based development environment with GPU access that feels like local development.
- Tight integration with the PyTorch Lightning framework makes it easy to scale training from laptop to cloud.
- Lightning AI is ideal for teams already using PyTorch Lightning, or those who want a smoother transition between local prototyping and cloud training.
Modal
Modal is a serverless compute platform that lets you run Python code on cloud GPUs without managing infrastructure.
- Pay-per-second billing means you only pay when your code is actually running; it does not charge for idle time.
- Supports GPUs from T4 up to A100/H100, with fast cold starts and easy parallelization.
- Modal is ideal for users who want to run training jobs or batch inference without setting up notebooks or managing environments.
RunPod
RunPod is a GPU marketplace that users can rent GPUs according to their needs.
- Pricing is often 3-5x cheaper than major cloud providers for comparable hardware.
- Offers on-demand and interruptible instances.
- Ideal for cost-conscious users willing to trade some reliability and support for significant savings, particularly for longer training runs.
Vast.ai
Vast.ai is a GPU marketplace that connect users with unused compute capacity from data centers and individual providers.
Deepnote
Deepnote is a collaborative data science platform that combines a code editor and a computational environment.
It offers real-time collaboration and customizable environments with an easy-to-use interface.
Users can easily make data visualizations.
Provides an AI assistant.
Deepnote is ideal for collaborative working, especially for 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 a 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 LLMs like ChatGPT, Gemini, and Mistral, with free and paid 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
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Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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