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# Introduction
I am sure if you are GPU-poor like me, you have come across Google Colab for your experiments. It gives access to free GPUs and has a very friendly Jupyter interface, plus no setup, which makes it a great choice for initial experiments. But we cannot deny the limitations. Sessions disconnect after a period of inactivity, typically 90 minutes idle or 12 to 24 hours max, even on paid tiers. Sometimes runtimes reset unexpectedly, and there is also a limit on maximum execution windows. These become major bottlenecks, especially when working with large language models (LLMs) where you may need infrastructure that stays alive for days and offers some level of persistence.
Therefore, in this article, I will introduce you to five practical alternatives to Google Colab that offer more stable runtimes. These platforms provide fewer interruptions and more robust environments for your data science projects.
# 1. Kaggle Notebooks
Kaggle Notebooks are like Colab’s sibling, but they feel more structured and predictable than ad-hoc exploration. They give you free access to GPUs and tensor processing units (TPUs) with a weekly quota — for example, around 30 hours of GPU time and 20 hours of TPU time — and each session can run for several hours before it stops. You also get a decent amount of storage and the environment comes with most of the common data science libraries already installed, so you can start coding right away without too much setup. Because Kaggle integrates tightly with its public datasets and competition workflows, it works especially well for benchmarking models, running reproducible experiments, and participating in challenges where you want consistent run times and versioned notebooks.
// Key Features
- Persistent notebooks tied to datasets and versions
- Free GPU and TPU access with defined quotas
- Strong integration with public datasets and competitions
- Reproducible execution environments
- Versioning for notebooks and outputs
# 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 jump in quickly just with your email. Unlike standard Colab sessions, your workspace and files stay around between sessions due to persistent storage, so you don’t have to reload everything every time you come back to a project. You still have limits on compute time and storage, but for many learning experiments or repeatable workflows it is easier to come back and continue 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’s infrastructure you see fewer random disconnects compared with free notebooks that don’t preserve state.
// Key Features
- Persistent development environments
- JupyterLab interface with fewer disconnects
- CPU and GPU runtimes available
- AWS-backed infrastructure reliability
- 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 keep control over the whole environment instead of running in short notebook sessions like on Colab. You can spin up a dedicated GPU pod quickly and pick from a wide range of hardware options, from mainstream cards to high-end accelerators, and you pay for what you use down to the second, which can be more cost-effective than big cloud providers if you just need raw GPU access for training or inference. Unlike fixed notebook runtimes that disconnect, RunPod gives you persistent compute until you stop it, which makes it a solid option for longer jobs, training LLMs, or inference pipelines that can run uninterrupted. You can bring your own Docker container, use SSH or Jupyter, and even hook into templates that come preconfigured for popular machine learning tasks, so setup is pretty smooth once you’re past the basics.
// Key Features
- Persistent GPU instances with no forced timeouts
- Support for SSH, Jupyter, and containerized workloads
- Wide range of GPU options
- Ideal for training and inference pipelines
- Simple scaling without long-term commitments
# 4. Paperspace Gradient
Paperspace Gradient (now part of DigitalOcean) makes cloud GPUs easy to access while keeping a notebook experience that feels familiar. You can launch Jupyter notebooks backed by CPU or GPU instances, and you get some persistent storage so your work stays around between runs, which is nice when you want to come back to a project without rebuilding your environment every time. There’s a free tier where you can spin up basic notebooks with free GPU or CPU access 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 more notebooks at once. Gradient also gives you tools for scheduling jobs, tracking experiments, and organizing your work so it feels more like a development environment than just a notebook window. Because it’s built with persistent projects and a clean interface in mind, it works well if you want longer-running tasks, a bit more control, and a smoother transition into production workflows compared with short-lived notebook sessions.
// Key Features
- Persistent notebook and VM-based workflows
- Job scheduling for long-running tasks
- Multiple GPU configurations
- Integrated experiment tracking
- Clean interface for managing projects
# 5. Deepnote
Deepnote feels different from tools like Colab because it focuses more on collaboration than raw compute. It is built for teams, so multiple people can work in the same notebook, leave comments, and track changes without extra setup. In practice, it feels a lot like Google Docs, but for data work. It also connects easily to data warehouses and databases, which makes pulling data in much simpler. You can build basic dashboards or interactive outputs directly inside the notebook. The free tier covers basic compute and collaboration, while paid plans add background runs, scheduling, longer history, and stronger machines. Since everything runs in the cloud, you can step away and come back later without worrying about local setup or things going out of sync.
// Key Features
- Real-time collaboration on notebooks
- Persistent execution environments
- Built-in version control and commenting
- Strong integrations with data warehouses
- Ideal for team-based analytics workflows
# Wrapping Up
If you need raw GPU power and jobs that run for a long time, tools like RunPod or Paperspace are the better choice. If you care more about stability, structure, and predictable behavior, SageMaker Studio Lab or Deepnote usually fit better. There is no single best option. It comes down to what matters most to you, whether that is compute, persistence, collaboration, or cost.
If you keep running into Colab’s limits, moving to one of these platforms is not just about comfort. It saves time, cuts down frustration, and lets you focus on your work instead of watching sessions disconnect.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
