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# Introduction
I am sure if you are GPU-poor like me, you would have come across Google Colab for your experiments. It provides access to free GPUs and has a very friendly Jupyter interface, plus no setup, making it a great choice for initial experiments. But we cannot deny the limitations. Sessions are locked after a period of inactivity, typically 90 minutes of inactivity or a maximum of 12 to 24 hours, even at paid tiers. Sometimes the runtime resets unexpectedly, and there is also a limit on the maximum execution window. These become major obstacles, especially when working with large language models (LLMs), where you may need an infrastructure that remains active for several days and provides some level of persistence.
So, in this article, I will introduce you to five practical alternatives to Google Colab that provide more stable runtime. These platforms provide fewer disruptions and a more robust environment for your data science projects.
# 1. Kaggle Notebook
kaggle notebook They’re like Collab’s sibling, but they feel more structured and predictable than ad-hoc exploration. They give you free access to the GPU and Tensor Processing Unit (TPU) with weekly quotas – for example, about 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 good 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 competitive 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
- Permanent notebook associated with datasets and volumes
- Free GPU and TPU access with defined quota
- Strong integration with public datasets and competitions
- Reproducible Execution Environment
- Versioning for notebooks and output
# 2. AWS SageMaker Studio Lab
AWS SageMaker Studio Lab There is a free notebook environment built on AWS that seems to be more stable than many other online notebooks. You get the 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 get involved with just your email. Unlike standard Colab sessions, your workspace and files remain persistent between sessions due to storage, so you don’t need to reload everything every time you return to a project. You still have limitations on compute time and storage, but for multiple learning experiments or repetitive workflows it’s easy 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 infrastructure, you see fewer random disconnects than free notebooks that don’t preserve state.
// key features
- sustainable development environment
- JupyterLab interface with fewer disconnects
- CPU and GPU runtimes are available
- Reliability of AWS-backed infrastructure
- Seamless upgrade path to full SageMaker if necessary
# 3. RunPod
runpod is a cloud platform built around GPU workloads where you rent GPU instances by the hour and have control over the entire environment instead of running in small notebook sessions like Colab. You can quickly spin up a dedicated GPU pod and choose from a wide range of hardware options, from mainstream cards to high-end accelerators, and pay for what you use, which may be more cost-effective than larger cloud providers if you need raw GPU access for training or inference. Unlike fixed notebook runtimes that disconnect, RunPod lets you continuously calculate until you turn it off, which makes it a solid choice for longer jobs, training LLMs, or estimation pipelines that can run uninterrupted. You can bring your own Docker container, use SSH or Jupyter, and even hook into pre-configured templates for popular machine learning tasks, so setup is very easy once you get 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) simplifies access to cloud GPUs while maintaining a familiar notebook experience. You can launch Jupyter notebooks backed by CPU or GPU instances, and you get some persistent storage so your work persists between runs, which is nice when you want to come back to a project without having to rebuild your environment every time. There’s a free tier where you can spin up a basic notebook with free GPU or CPU access and a few gigabytes of storage, and if you pay for the Pro or Growth plan you get more storage, faster GPUs, and the ability to run more notebooks at once. gradient also gives you tools to schedule jobs, track experiments, and organize your work, so it feels more like a development environment than just a notebook window. Because it’s designed with frequent projects and a clean interface in mind, it works well if you want a smoother transition to a production workflow than long-running tasks, a little more control, and short-term notebook sessions.
// key features
- Continuous Notebook and VM-Based Workflow
- Task scheduling for long running tasks
- Multiple GPU Configuration
- Integrated Usage Tracking
- Clean interface for managing projects
# 5. Deepnote
deepnote It feels different from tools like Colab 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 additional setup. In practice, it feels a lot like Google Docs, but for data work. It also connects easily to data warehouses and databases, making it very easy to extract data. You can create basic dashboards or interactive output directly inside the notebook. The free tier includes basic computing and collaboration, while paid plans include background runs, scheduling, longer histories, and robust machines. Since everything runs in the cloud, you can go away and come back later without worrying about local setup or things getting out of sync.
// key features
- real time collaboration on notebook
- persistent execution environment
- Built-in version control and comments
- Tight integration with data warehouse
- Ideal for team-based analytics workflows
# wrapping up
If you need raw GPU power and long running jobs, tools like Runpod or Paperspace are better choices. 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 depends on what matters most to you, whether it’s calculation, persistence, collaboration, or cost.
If you keep running into Colab limits, moving to one of these platforms isn’t just about comfort. This saves time, reduces frustration, and lets you focus on your work instead of watching sessions disconnect.
Kanwal Mehreen He is a machine learning engineer and a technical writer with a deep passion for the intersection of AI with data science and medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is an advocate for diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitex GlobalLink Research Scholar, and a Harvard VCode Scholar. Kanwal is a strong advocate for change, having founded FEMCodes to empower women in STEM fields.
