Jake Podell
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Jake Podell

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hex.tech vs Deepnote in 2025

In the evolving realm of data science and analytics, the tools and platforms available to teams continue to expand. Among the many options, Hex (hex.tech) and Deepnote stand out as modern, collaborative notebook SaaS (Software as a Service) platforms.

Both aim to improve how data teams explore, analyze, and share insights—but they take slightly different approaches. This article compares Hex and Deepnote with a focus on:

  • Collaborative notebook workflows
  • Data source integrations
  • User experience
  • AI assistants and automation
  • Limitations around Jupyter widgets and iframes

By the end, you’ll have a clearer sense of which platform better fits your workflows and team dynamics.

Introduction to hex.tech

Hex is a modern data workspace designed to bridge the gap between coding and collaboration. It combines:

  • Notebooks that support SQL, Python, and no-code steps
  • Interactive apps and dashboards you can share with stakeholders
  • Collaboration features like comments, branching, and review workflows

Hex focuses on helping data teams go from exploration to polished, shareable “apps” in a single environment, without exporting code or charts elsewhere.

Introduction to Deepnote

Deepnote is another prominent player in the notebook SaaS landscape. It is built around Jupyter-style notebooks but adds:

  • Real-time collaboration, similar to Google Docs
  • Project- and workspace-level organization for teams
  • Quality-of-life features such as scheduling, versioning, and notebook sharing

Deepnote’s goal is to make notebooks a first-class, collaborative medium for both data scientists and non-technical stakeholders—so everyone can work together in the same workspace.

Comparison of Key Features

Collaborative Environment

Hex:
Hex provides a robust environment for teamwork, featuring:

  • Real-time collaboration where multiple users can work on the same project
  • Cell-level commenting and review
  • Version history to understand how an analysis evolved

This is particularly useful for distributed teams who need tight feedback loops between analysts and business stakeholders.

Deepnote:
Deepnote also excels at collaboration:

  • Multiple users can edit and run cells in real time
  • Built-in comments and discussion threads
  • Integrations with popular tools like Slack help keep conversations close to the analysis

For teams used to Google Docs-style collaboration, Deepnote feels familiar and approachable.

Data Source Integration

Hex:
A significant strength of Hex is its ability to integrate seamlessly with a wide variety of data sources, including:

  • SQL databases and data warehouses
  • Data lakes and object storage
  • APIs and other external services

This makes it convenient to pull data from multiple locations without leaving the workspace, and to build reusable datasets for downstream apps.

Deepnote:
Deepnote also supports a broad range of data sources, offering integrations with:

  • SQL databases and warehouses
  • Cloud storage (e.g., files in object storage)
  • APIs and external services through Python

The goal is to provide a flexible environment where data can be easily imported, transformed, and analyzed within a single, shared notebook.

User Interface and Experience

Hex:
The user interface of Hex is sleek and modern, with a focus on combining notebooks and apps:

  • A notebook-like editing experience for analysts
  • A separate “app” or “report” view tailored to non-technical users
  • Intuitive navigation between queries, charts, and narrative text

This design makes it easier for new users to get started and for experienced users to evolve analyses into polished artifacts.

Deepnote:
Deepnote emphasizes a clean, notebook-first experience:

  • Minimal UI that keeps the focus on code, outputs, and text
  • Simple, project-based navigation for organizing work
  • A smooth onboarding process, accessible even for those who are new to notebooks

If your team already loves Jupyter, Deepnote feels like a “cloud Jupyter” with thoughtful enhancements.

AI Assistants and Automation

Both platforms now offer AI-powered features to speed up data work.

Hex:
Hex includes AI features that can help you:

  • Generate or edit SQL and Python cells from natural language prompts
  • Create or modify charts and visualizations
  • Make quick edits to markdown and narrative content

AI is integrated directly into the notebook and app-building workflow, helping analysts move faster from questions to insights.

Deepnote:
Deepnote’s AI capabilities focus on making notebooks easier to build and maintain:

  • Generate and edit code or SQL from natural language
  • Explain complex code blocks in simple terms
  • Help scaffold entire notebooks based on a prompt or objective

For teams that want AI to assist with authoring and understanding notebooks, Deepnote AI acts as a coding partner inside the workspace.

Pricing and Team Fit (High-Level)

Pricing details change over time, but at a high level:

  • Both Hex and Deepnote typically offer free or entry-level tiers, making them accessible for individuals or small teams to start experimenting.
  • Paid tiers introduce higher limits, more integrations, and enterprise features such as SSO, governance, and advanced support.

As a rough guide:

  • Hex often resonates with teams that already have strong data infrastructure and want a powerful workspace for building shareable data apps.
  • Deepnote is often appealing to teams who want Jupyter-style notebooks in the cloud with strong collaboration and scheduling.

For current, exact pricing, it’s best to refer to each vendor’s official pricing page.

Limitations with Jupyter Widgets

Both Hex and Deepnote offer compatibility with Jupyter notebooks, but there are some notable limitations—especially for users who rely heavily on Jupyter widgets (e.g., ipywidgets, custom interactive components).

Limitations in Detail

10MB Size Limitation:
In practice, both platforms impose a 10MB size limit for iframe payloads, which directly affects the usage of some Jupyter widgets. This can be a significant constraint for users who rely on rich, interactive widgets for data analysis and visualization:

  • Large, complex widgets that serialize big states or include heavy front-end bundles may exceed this limit.
  • When the widget state or resources go beyond ~10MB, the widget may fail to render, appear partially, or behave inconsistently in the hosted environment.

This restriction stems from the need to balance performance, security, and resource allocation across multi-tenant cloud infrastructures. At the time of writing:

  • Lightweight widgets (simple sliders, dropdowns, toggles) usually work as expected.
  • Very heavy or custom UI components may require refactoring, reducing payload size, or using alternative approaches (for example, building a separate frontend or relying on lighter-weight visualization libraries).

Practical Workarounds

If you hit widget-related issues in either Hex or Deepnote, you can consider:

  • Simplifying widget state: reduce the number of UI elements or the amount of data kept in widget state.
  • Offloading heavy logic: move complex front-end interactions into a separate app or service and embed only the final visualization or link.
  • Using lighter visualization options: in many cases, you can achieve similar interactivity using JavaScript-based plotting libraries, Vega/Vega-Lite, or other tools that don’t require large, serialized widget state.

Which Should You Choose?

Ultimately, choosing between Hex and Deepnote comes down to your team’s priorities and workflows:

  • Choose Hex if…

    • You want a strong path from notebooks to shareable, polished data apps.
    • Your team works heavily with SQL + Python and wants integrated AI assistance and app-building in one place.
    • You prioritize workflows where analysts build artifacts that business users can explore safely.

  • Choose Deepnote if…

    • You want a cloud-native, Jupyter-like notebook experience with real-time collaboration.
    • Your team values simple onboarding, project-based organization, and scheduling.
    • You’re looking for an AI assistant that can help generate and maintain notebooks, code, and SQL directly.

For teams that rely heavily on Jupyter widgets, both platforms are attractive but share similar iframe-related limitations. Testing your most important widgets and dashboards is key before fully committing.

Conclusion

Both Hex and Deepnote bring powerful features to the table for modern data teams:

  • Rich collaboration in the browser
  • Strong integrations with common data sources
  • Increasingly capable AI assistants that accelerate analysis

The main caveat for power users is the 10MB iframe size limit, which can impact very large or complex Jupyter widgets. For most everyday workflows, this won’t be a blocker—but if your work leans heavily on custom widget UIs, it’s important to prototype and validate in each environment.

By understanding their strengths, limitations, and how they approach collaboration, AI, and notebooks, you can make a more informed decision about whether Hex, Deepnote, or a combination of both best aligns with your data science requirements.