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# Entry
I’m sure if you’re GPU indigent like me, you’ve stumbled upon Google Colab for some experimentation. It gives you access to free GPUs, has a very user-friendly Jupyter interface, and requires no configuration, making it a great choice for initial experiments. But we cannot deny the limitations. Sessions disconnect after a period of inactivity, typically after 90 minutes of inactivity or a maximum of 12 to 24 hours, even on paid tiers. Sometimes runtimes reset unexpectedly and there is also a limit on the maximum number of execution windows. These become major bottlenecks, especially when working with huge language models (LLM), where you may need infrastructure that runs for several days and provides some level of persistence.
Therefore, in this article, I will present you five practical alternatives to Google Colab that offer a more stable operating environment. These platforms provide fewer interruptions and more resilient environments for data science projects.
# 1. Kaggle Notebooks
Kaggle Notebooks they are like Colab’s siblings, but feel more structured and predictable than ad hoc exploration. They provide free access to GPUs and Tensor Processing Units (TPUs) with a weekly limit – for example, about 30 hours of GPU time and 20 hours of TPU time – and each session can last several hours before stopping. You also get a decent amount of storage, and the environment comes with most popular data science libraries already installed, so you can start coding right away without having to do too much configuration. Because Kaggle integrates tightly with public datasets and competitive workflows, it is particularly good for benchmarking models, running repeatable experiments, and participating in challenges where consistent run times and versioned notebooks are required.
// Key Features
- Persistent notebooks associated with datasets and versions
- Free access to GPU and TPU with certain limits
- Forceful integration with public datasets and competitions
- Reproducible execution environments
- Versioning for notebooks and results
# 2. AWS SageMaker Studio Lab
AWS SageMaker Studio Lab is a free notebook environment built on AWS that seems more stable than many other online notebooks. You get a JupyterLab interface with CPU and GPU options that doesn’t require an AWS account or credit card to get started, so you can get up and running quickly via email. Unlike standard Colab sessions, your workspace and files remain between sessions due to persistent storage, so you don’t have to reload everything every time you return to your project. You still have limits on computation time and data storage, but for many learning experiments or repetitive workflows, it’s easier to go back and pick up where you left off without losing your configuration. It also has good GitHub integration so you can sync your notebooks and datasets if you want, and since it runs on AWS infrastructure, you see fewer accidental disconnections compared to free notebooks that don’t preserve state.
// Key Features
- Persistent development environments
- JupyterLab interface with fewer disconnects
- Available CPU and GPU operating times
- Infrastructure reliability powered by AWS
- Polished 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 maintain control over the entire environment, rather than running in miniature notebook sessions like Colab. You can quickly spin up a dedicated GPU module and choose from a wide range of hardware options, from mainstream cards to high-end accelerators, and you pay for what you employ by the second, which can be more cost-effective than huge cloud providers if you only need access to the raw GPU for training or inference purposes. Unlike fixed notebook runtimes that disconnect, RunPod provides continuous processing power until you stop it, making it a solid option for longer tasks, LLM training, or inference pipelines that can run uninterrupted. You can bring your own Docker container, employ SSH or Jupyter, and even connect to templates that are pre-configured for popular machine learning tasks, making setup quite sleek once you get the hang of the basics.
// Key Features
- Persistent GPU instances with no forced timeouts
- Support for SSH, Jupyter and containerized workloads
- Wide selection of GPU options
- Perfect for training and inference pipelines
- Uncomplicated scaling without long-term commitments
# 4. Paper space gradient
Paper space gradient (now part of DigitalOcean) makes it easier to access GPUs in the cloud while maintaining the familiar notebook experience. You can run Jupyter notebooks backed by CPU or GPU instances, and you’ll get persistent storage so your work stays between runs, which is nice when you want to get back to a project without having to rebuild the environment every time. There’s a free tier where you can upgrade entry-level notebooks with free GPU or CPU access and several gigabytes of storage, and if you pay for the Pro or Growth plans, you’ll get more storage, faster GPUs, and the ability to run more notebooks at once. Gradient also provides tools for scheduling tasks, tracking experiments, and organizing work, making it feel more like a programming environment than just a notebook window. Because it’s built with robust designs and a spotless interface in mind, it works well if you need longer tasks, a bit more control, and a smoother transition to production workflows compared to short-lived notebook sessions.
// Key Features
- Strong notebook and virtual machine-based workflows
- Task scheduling for long-running tasks
- Multiple GPU configurations
- Integrated experiment tracking
- Clear interface for project management
# 5. Deep note
Deep note differs from tools like Colab because it focuses more on collaboration than elementary calculations. It’s designed for teams, so multiple people can work in the same notebook, leave comments, and track changes without additional configuration. In practice, it’s a bit like Google Docs, but for working with data. It also easily connects to data warehouses and databases, making data retrieval much easier. You can create basic dashboards or interactive results directly in the notebook. The free tier includes basic computing and collaboration, while paid plans add background functionality, scheduling, longer history, and more powerful machines. Because everything runs in the cloud, you can leave and come back later without worrying about local configuration or lack of sync.
// Key Features
- Real-time collaboration on notebooks
- Persistent runtimes
- Built-in version control and commenting
- Forceful integrations with data warehouses
- Perfect for team analysis workflows
# Summary
If you need raw GPU power and tasks that run for a long time, tools like RunPod or Paperspace are a better choice. If you’re more interested in stability, structure, and predictable behavior, SageMaker Studio Lab or Deepnote are usually a better fit. There is no one best option. It all comes down to what’s most essential to you, whether it’s calculations, durability, collaboration or cost.
If you’re still pushing Colab’s limits, switching to one of these platforms isn’t just about convenience. It saves time, reduces frustration, and allows you to focus on your work instead of watching your sessions disconnect.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of artificial intelligence and medicine. She is co-author of the e-book “Maximizing Productivity with ChatGPT”. As a 2022 Google Generation Scholar for APAC, she promotes diversity and academic excellence. She is also recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a staunch advocate for change and founded FEMCodes to empower women in STEM fields.