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# Introduction
I'm sure if you're GPU poor like me, you've come across Google Colab for your testing. It provides access to free GPUs and has a very friendly Jupyter interface, and no setup, making it a good choice for initial testing. But we cannot deny limitations. Sessions are terminated after a period of inactivity, usually 90 minutes of inactivity or 12 to 24 hours at most, even for paid sessions. Sometimes the runtimes are reset unexpectedly, and there is a limit to the maximum execution windows. This becomes a big issue, especially if you are working with large language models (LLMs) where you may need an infrastructure that lasts for days and provides a certain level of persistence.
Therefore, in this article, I will present five effective alternatives to Google Colab that provide stable runtimes. These platforms offer fewer distractions and more robust environments for your data science projects.
# 1. Kaggle Notebooks
Kaggle Notebooks they're similar to Colab's siblings, but feel structured and predictable rather than ad-hoc experiments. They give you free access to GPUs and tensor processing units (TPUs) with a weekly quota – for example, about 30 hours of GPU time and 20 hours of TPU time – and each session can run for a few hours before it stops. You also get a decent amount of storage and the platform comes with many common data science libraries already installed, so you can start coding quickly without much setup. Because Kaggle integrates tightly with its public datasets and competitive workflows, it works especially well for estimating models, using reproducible tests, and participating in challenges where you want consistent runtimes and versioned notebooks.
// Main Features
- Continuous notebooks are tied to datasets and versions
- Free GPU and TPU access with defined quotas
- Strong integration with public data sets and competitions
- Multiple application areas
- Version of notebooks and results
# 2. AWS SageMaker Studio Lab
AWS SageMaker Studio Lab is a free notebook environment built on AWS that feels more stable than many other online notebooks. You get a JupyterLab interface with CPU and GPU options, and it doesn't require an AWS account or credit card to get started, so you can quickly log in with your email. Unlike regular Colab sessions, your workspace and files stay between sessions thanks to persistent storage, so you don't have to reload everything every time you return to a project. You still have time and storage limitations, but for many learning tests or repetitive workflows it's easy to come back and pick up where you left off without losing your setup. It also has good GitHub integration so you can sync your notebooks and datasets if you want, and because it runs on AWS infrastructure you see fewer unplanned disconnections compared to free notebooks that don't save state.
// Main Features
- Areas of continuous improvement
- JupyterLab interface with few breaks
- CPU and GPU runtimes are available
- The reliability of the infrastructure supported by AWS
- Seamless upgrade path to full SageMaker if needed
# 3. RunPod
RunPod is a cloud platform built around GPU workloads where you rent GPU instances by the hour and manage the entire environment instead of working in short notebook times like in Colab. You can quickly browse a dedicated GPU pod and choose from a wide range of hardware options, from basic cards to high-end accelerators, and pay for what you use up to the second, which can be more expensive than the big cloud providers if you just need raw GPU access for training or coaching. Unlike fixed notebook startup times that are interrupted, RunPod gives you continuous computing until you stop it, making it a solid choice for long projects, training LLMs, or index pipelines that can run without interruption. You can bring your own Docker container, use SSH or Jupyter, and even log into the templates that come pre-configured for popular machine learning tasks, so setup is smooth once you get past the basics.
// Main Features
- Continuous GPU instances without forced timeout
- Support for SSH, Jupyter, and containerized workloads
- Wide range of GPU options
- Perfect for training and targeting pipes
- Easy scaling with no long-term commitments
# 4. Paperspace Gradient
Paperspace Gradient (now part of DigitalOcean) makes cloud GPUs accessible while keeping the notebook experience feeling familiar. You can launch Jupyter notebooks supported by CPU or GPU instances, and you get some continuous storage so that your work can last between runs, which is great if you want to return to the project without rebuilding your site every time. There is a free tier where you can browse basic notebooks with access to a free GPU or CPU and a few gigabytes of storage, and if you pay for the Pro or Growth plans you get more storage, faster GPUs, and the ability to run multiple notebooks at once. Gradient also gives you tools for planning tasks, testing tests, and organizing your work so it feels more like a development environment than a notebook window. Because it's built with ongoing projects and a clean interface in mind, it works well when you're looking for long-running jobs, more control, and a smoother transition to a productive workflow compared to short-lived book sessions.
// Main Features
- Continuous notebook and VM-based workflows
- Work planning for long-term projects
- Multiple GPU configurations
- Integrated test tracking
- A clean interface for managing projects
# 5. Deep note
A deep note it feels different from tools like Colab because it focuses more on collaboration than raw compute. It's designed for teams, so multiple people can work in one notebook, leave comments, and track changes without additional setup. Basically, it sounds very similar to Google Docs, but in terms of data functionality. It also easily connects to databases and databases, making data extraction much easier. You can create basic dashboards or interactive outputs right inside the notebook. The free tier includes basic computing and collaboration, while paid plans add background running, editing, long history, and powerful machines. Since everything runs in the cloud, you can leave and come back later without worrying about local settings or things getting out of sync.
// Main Features
- Real-time collaboration on notebooks
- Continuous use cases
- Built-in version control and commenting
- Strong integration with databases
- Ideal for team-based analysis workflows
# Wrapping up
If you need raw GPU power and long-running tasks, tools like RunPod or Paperspace are a better choice. If you care more about stability, structure, and predictable behavior, SageMaker Studio Lab or Deepnote are usually a better fit. There is no single best option. It comes down to what's most important to you, whether that's calculation, persistence, cooperation, or cost.
If you continue to enter the limits of Colab, going to one of these platforms is not just a luxury. It saves time, reduces frustration, and allows you to focus on your work instead of watching the clock tick by.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI and medicine. He co-authored the ebook “Increasing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, he strives for diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.
