Choosing the right IDE for your Jupyter Notebook projects
Choosing the right IDE for working with Jupyter Notebook can have a significant impact on a developer’s productivity and effectiveness. With the growing popularity of notebooks, there are now numerous IDE options available for developers, each with its unique set of features and advantages.
In this article, we’ll compare some of the most popular IDEs for working with Jupyter notebooks and highlight their key features and use cases to help you make an informed decision.
Quick answer: For notebook-centric work, JupyterLab is the best default — it’s free, open-source, built by the Jupyter team, and offers native AI integration via MCP. If you want a single editor for notebooks and general code, VS Code with the Jupyter extension is a decent alternative. For free cloud GPUs, use Google Colab; for managed team/cloud ML, Amazon SageMaker AI.
Here’s how the main options compare at a glance:
| IDE | Type | Cost | Best for | AI assistance | Version control via Git |
|---|---|---|---|---|---|
| JupyterLab | Local (browser UI) | Free, open-source | Notebook-first, exploratory data work | Extensions + Claude Code over MCP — works directly on notebooks | Via jupyterlab-git extension |
| VS Code | Local | Free | One editor for notebooks and general code | GitHub Copilot integration (free tier) | Built-in |
| PyCharm | Local | Free; paid Pro tier for advanced features | Larger Python projects, JetBrains users | JetBrains AI / Junie | Built-in |
| Google Colab | Cloud | Free tier + paid | Quick prototyping, free GPU access | Gemini built-in | Limited (save to GitHub) |
| Amazon SageMaker AI | Cloud | Pay-as-you-go (AWS) | Teams on AWS, ML pipelines | Amazon Q Developer | Built-in (Studio) |
The sections below go deeper on each.
PyCharm
PyCharm is a Python IDE with solid integration for Jupyter notebooks. As of the 2025.1 release, JetBrains unified PyCharm into a single product and made Jupyter notebook support free — running, debugging, output rendering, and code assistance are all included at no cost, with a paid Pro subscription adding advanced features. (Previously, Jupyter support was limited to the paid Professional edition.)
For instance, the per-cell toolbar facilitates quick actions like running individual cells, while the tool window provides you quick access to the Jupyter Notebook server logs. The Jupyter variable tool window simplifies debugging by showing values of all variables in use. PyCharm allows you to configure server settings flexibly, either by using a managed server or connecting to a local or remote kernel through a URL.
Pro Tip: Remember that, for security reasons, inbuilt JavaScript in notebooks won’t run unless the notebook is explicitly marked as trusted.
If you’re already using PyCharm professional edition then you might want to consider using the same IDE for Jupyter notebooks as well.
Visual Studio Code
VS Code by Microsoft is a free editor that has become the default choice for many Python and data teams. Its Jupyter notebook support is strong — you edit .ipynb files directly, with GitHub Copilot available for in-editor AI assistance (though, like most code-first editors, its AI works more naturally on .py files than on notebook cells).
The automatic generation of a Table of Contents for notebooks is a key feature, making navigation and organization easier. Another useful addition is the local notebook diffing functionality, which allows you to show or hide notebook metadata and output diffs, simplifying the comparison of changes between versions.
Pro Tip: While VS Code offers git diffs for local notebook changes, you might need ReviewNB for GitHub PR / Commit diffs.
VS Code also offers connections to remote kernels, providing an advantage when collaborating or working with cloud resources. The notebook search functionality saves time by allowing you to find specific content within your notebooks quickly.
Dataframes can be displayed in a data viewer table that is filterable and sortable, making it ideal for data analysis tasks.
VS Code’s range of data analysis tools, strong Jupyter Notebook support, and compatibility with multiple programming languages make it a good choice for notebook users who also work across regular code files.
Amazon SageMaker
Amazon SageMaker (now branded Amazon SageMaker AI) is a managed cloud service offering a JupyterLab-based IDE with custom extensions, preset environments, and built-in Amazon Q Developer AI assistance.
SageMaker caters to various needs by offering two distinct notebook options—
- Studio Notebooks, and
- Notebook Instances
Studio Notebooks is a fully managed, multi-user notebook service designed for team collaboration that provides features such as a shared workspace and real-time co-editing of notebooks. In contrast, Notebook Instances delivers a more basic, individualized experience on EC2. Studio Notebooks come with more integrated tooling, so weigh the added features against the cost for your workload.
A significant constraint of SageMaker is its cloud-only nature, with no option to connect to a local Jupyter kernel. To address this limitation, consider pairing SageMaker with a local Jupyter IDE like JupyterLab, PyCharm, or VS Code Notebooks. This combination will allow you to benefit from the flexibility of cloud while not racking up unnecessary bills when local compute is sufficient.
SageMaker is an excellent choice for running model-building pipelines, particularly if your company already uses AWS. However, before committing to SageMaker as your primary Jupyter Notebook environment, ensure you explore the platform thoroughly and remain aware of potential challenges & limitations.
JupyterLab
JupyterLab is the web-based interface for Project Jupyter and the most notebook-native option here — it is the reference Jupyter UI. It’s open-source, maintained by the Jupyter community, and the current release line is JupyterLab 4.
JupyterLab supports various document types such as Notebooks, Markdown and code files & caters to diverse workflows. Here are some notable features of JupyterLab:
- A highly customizable interface with many extensions & enhancements, empowering users to tailor their workspace to suit their preferences.
- A built-in extension manager that streamlines discovering and installing new extensions to augment your JupyterLab experience.
- JupyterLab RTC support, enabling real-time collaboration by allowing multiple users to edit the same document simultaneously (still an opt-in, experimental feature).
Because JupyterLab is open and extensible, it also has the most flexible AI story. Instead of a single bundled assistant, you can connect tools like Claude Code over MCP, which work directly on your notebooks — reading cells, running code, and editing the notebook through the live kernel — rather than treating the .ipynb as a static text file. That notebook-native behavior is hard to match in code-first editors, where AI assistance is tuned for .py files.
For notebook-first, exploratory data work, JupyterLab is the strongest default: free, open-source, endlessly customizable, and squarely inside the Jupyter ecosystem. For Git workflows inside JupyterLab, see our guide on the JupyterLab Git extension.
Google Colab
Google Colab is a cloud-based IDE that supports Jupyter notebooks and integrates with Google Drive. This integration enables users to store, share, and collaborate on notebook files easily. Colab grants access to Google’s computing infrastructure, including free GPU and TPU resources for accelerated machine learning tasks, and now ships with built-in Gemini AI assistance for code generation and explanation.
While Google Colab provides free access to GPU, there’s no guarantee on the type & duration of the compute resource you will get, even on their paid plans. This makes Google Colab unsuitable for production workflows.
When evaluating Google Colab, consider these distinct features:
- Smooth integration with other Google services like Google Sheets, Google Cloud Storage, and Google BigQuery, streamlining data import and export tasks.
- Built-in support for TensorFlow, making it an attractive choice for deep learning projects.
- A variety of preinstalled libraries and environments, allowing users to dive into their projects without spending time on configuration.
Google Colab is a good choice for users seeking a free, cloud-based IDE for quick prototyping or exploration. For anything serious, you’ll need to see elsewhere.
FAQ
What’s the best IDE for Jupyter notebooks?
For notebook-centric work, JupyterLab is the best default — it’s free, open-source, built by the Jupyter team, and offers native-AI integration via MCP.
VS Code vs JupyterLab for notebooks — which should I use?
Use JupyterLab if your work is notebook-first and exploratory; it’s the native Jupyter environment, and its extension ecosystem and AI tooling are built around notebooks. Use VS Code if you want one editor for notebooks and general code, with features like Live Share and Copilot integration. Both are free.
Is PyCharm good for Jupyter notebooks?
Yes. PyCharm has solid Jupyter support — cell execution, debugging, output rendering, and code assistance. It’s a good fit if you already work in PyCharm but the AI integration here is limited.
What’s the best free IDE for Jupyter notebooks?
JupyterLab (fully free and open-source) and VS Code (free) are the strongest free options for local work, and PyCharm’s core — including Jupyter support — is now free as well. For free cloud notebooks with GPUs, Google Colab has a free tier.
What’s the best AI IDE for Jupyter notebooks?
Most editors now bundle an AI assistant — Copilot in VS Code, Gemini in Colab, Amazon Q in SageMaker — but they’re tuned for code files more than notebooks. For notebook-native AI, JupyterLab paired with Claude Code over MCP lets the assistant read cells, run code, and edit the notebook through the live kernel.
Conclusion
There’s no single right answer, but for most notebook-centric work JupyterLab is the safest default with the best native-AI integration via MCP. VS Code is a good pick if you want one editor for notebooks and everything else. The cloud options (Google Colab, Amazon SageMaker AI) make sense when you need hosted GPUs or are already in that ecosystem, and PyCharm is a natural fit for existing JetBrains users.
Once your team moves from working solo to collaborating on notebooks, you’ll hit a gap none of these IDEs fill: reviewing notebook changes on a pull request. They show local diffs, but reviewing .ipynb changes on GitHub — with a readable rich diff and cell-level comments — is what ReviewNB is built for.