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# Introduction
I’m sure if you’re GPU poor like me, you’ve come to Google Collab for your experiences. It provides free GPU access and has a very friendly Jupiter interface, with no additional setup, making it a great choice for early experiments. But we cannot deny the limitations. Sessions are disconnected after a period of inactivity, typically 90 minutes of idle time or 12 to 24 hours at most, even on paid tiers. Runtimes sometimes 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 an infrastructure that lives up to the day and offers some level of persistence.
So, in this article, I will introduce you to five practical alternatives to Google Collab that offer a more stable runtime. These platforms provide fewer barriers and a more robust environment for your data science projects.
# 1. Kaggle Notebook
Kaggle notebook Collabs are like a sibling, but they feel more structured and predictable than ad hoc research. They give you free access to GPUs and Tensor Processing Units (TPUs) with a weekly quota – for example, 30 hours of GPU time and 20 hours of TPU time – and each session can last for several hours before stopping. You also get a decent amount of storage and the environment comes with most common data science libraries pre-installed, so you can start coding right away without a lot of setup. Because Kaggle integrates tightly with its public datasets and competitive workflows, it works particularly well for benchmarking models, running reproducible experiments, and participating in challenges where you want consistent runtimes and versioned notebooks.
// Key Features
- Persistent notebooks associated with datasets and versions
- Access to free GPU and TPU with fixed quota
- Strong integration with public datasets and competitions
- Environments of reproductive processes
- Versions for notebook and output
# 2. AWS sagemaker studio lab
AWS sagemaker studio lab There 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 with just your email. Unlike standard Collab sessions, your workspace and files stay between sessions due to persistent storage, so you don’t have to reload everything every time you return to a project. You still have limits on compute time and storage, but it’s easy to come back for many learning experiences or repetitive workflows 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 infrastructure you see less random disconnects than free notebooks that don’t store state.
// Key Features
- Sustainable Development Environments
- Jupyterlab interface with less disconnection
- CPU and GPU runtime available
- Infrastructure reliability supported by AWS
- Upgrade seamlessly if needed
# 3. Runpod
Run Pod There is a cloud platform built around GPU workloads where you rent GPU instances by the hour and control the entire environment instead of running them in short notebook sessions like Colab. You can quickly spin up a dedicated GPU pod and pick from a wide range of hardware, from mainstream cards to high-end accelerators, and you pay for what you use, which can be more cost-effective than the big cloud providers if you need access to raw GPUs for training or evaluation. Unlike fixed notebook runtimes that interrupt, Runpid gives you continuous computation until you stop it, making it a solid option for long jobs, training LLMs, or inference pipelines that can run without interruption. You can bring your own Docker containers, use SSH or Jupyter, and even hook into templates that come ready-made for popular machine learning tasks, so once you get past the basics, setup is pretty smooth.
// Key Features
- With constant GPU instances without 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. Paper space gradient
Paper space gradient (now a part of Digitalisen) makes cloud GPUs easy to access while still having a notebook experience that feels familiar. You can launch Jupiter notebooks with support for CPU or GPU instances, and you get some persistent storage so your work lives between runs, which is nice if you want to return to a project without rebuilding your environment each time. There are free tiers where you can roll out basic notebooks with free GPU or CPU access and a few gigabytes of storage, and if you pay for pro or growth plans you get more storage, faster GPUs, and the ability to run more notebooks at once. Milan also gives you tools to schedule jobs, track experiments, and organize your work so that it feels more like a development environment than just a notebook window. Because it’s built with consistent projects and a clean interface in mind, it works well if you want long-running tasks, a bit more control, and a smoother transition to production workflows than short-term notebook sessions.
// Key Features
- Persistent notebook and VM-based workflows
- Job scheduling for long running tasks
- Multiple GPU configurations
- Integrated experience tracking
- Clean interface for managing projects
# 5. Deep
deep Feels different from tools like Collab because it focuses more on collaboration than raw compute. It’s built for teams, so multiple people can work in the same notebook, leave comments, and track changes without any extra setup. In practice, it feels a lot like Google Docs, but for data work. It also easily connects to data warehouses and databases, which makes pulling data much easier. You can create basic dashboards or interactive output directly within the notebook. The free tier covers basic compute and collaboration, while paid plans add background runs, scheduling, long history, and robust machines. Since everything runs in the cloud, you can leave and come back later without worrying about local setup or things going out of sync.
// Key Features
- Real-time collaboration on notebooks
- Consistent execution environment
- Built-in version control and commenting
- Strong integration with data warehouses
- Ideal for team-based analytics workflows
# wrap up
If you need raw GPU power and jobs that run for long periods of time, tools like Runpid or Paperspace are better choices. If you care more about stability, texture, and predictable behavior, SageMaker Studio Lab or DeepNote are usually a better fit. There is no single best option. It matters most to you, whether it’s compute, stability, support, or price.
If you keep running into the limitations of Colab, moving to one of these platforms isn’t just about comfort. This saves time, eliminates frustration, and lets you focus on your work instead of session disconnects.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT.” As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.