Hex vs. Deepnote: Complete comparison (2026)

Hex and Deepnote both originated in similar places, supporting advanced analytics, but the platforms have significantly diverged with the emergence of AI analytics and are now built around different approaches to working with data.

Deepnote still focuses on giving data practitioners and AI agents a collaborative workspace for technical analysis, notebooks, and data applications. Hex has broadened to become an AI-native analytics platform where anyone can ask questions, investigate data, and build dashboards or apps through natural language.

Both platforms have invested heavily in AI, but making data teams more productive only solves part of the problem. As more people across the business turn to AI to work with data, organizations also need a way to prevent analysis from becoming fragmented across disconnected tools, conversations, and dashboards.

That's where the two platforms start to diverge. Hex is built to make AI analytics accessible across the company, while giving data teams the shared context, governance, and observability to keep that work trustworthy.

TL;DR: Hex vs. Deepnote

Choose Hex if: You want to make analytics accessible across your entire company, allowing business users and data teams to ask questions, investigate results, and build dashboards and apps through AI. Hex combines conversational self-service, advanced analysis, and AI-powered building with shared context and observability, helping teams avoid fragmented, ungoverned analytical work as AI adoption grows.

Choose Deepnote if: Your primary need is a collaborative environment for technical data work, including SQL and Python analysis, data science, and agent-driven development. Deepnote offers notebook capabilities, open-source portability, and tools for building analytical applications.

Both support technical analysis and AI-assisted workflows. The distinction is whether you're primarily looking for a workspace where practitioners build analytical work, or a platform designed to make the broader organization capable of working with data and AI.

Hex vs. Deepnote at a glance

The biggest differences emerge when you move beyond what each platform can build and consider who can use those capabilities independently, and whether the resulting work stays connected.

What Hex and Deepnote are built for

Both platforms combine code-based analysis with collaboration and AI, but they organize the experience around different needs.

Hex: AI-native analytics for the entire company

Hex brings conversational analytics, advanced analysis, and AI-powered building together in one platform.

Business users can ask questions and investigate data in natural language through Threads, create dashboards and interactive applications with AI, and access governed analytics from tools like Slack and Claude.

Data teams retain the technical depth needed for advanced SQL and Python analysis, alongside the ability to curate business context, inspect generated work, and monitor answer quality.

Rather than requiring every stakeholder to understand the underlying analytical environment, Hex uses AI to make those capabilities accessible to a much broader audience.

Key capabilities include:

• Threads for multi-turn conversational analysis, follow-up questions, and natural-language exploration.
• The Hex Agent for writing SQL and Python, investigating problems, and performing more complex analytical work.
• Generative Apps for building customizable dashboards and interactive applications from prompts.
• Context Studio for observing AI usage, evaluating answers, and improving the business context agents rely on.
• MCP, Slack, and CLI integrations for bringing governed analytics into other tools and workflows.

Hex also supports traditional analytical workflows for people who prefer working directly with code. The difference is that code is no longer the primary interface everyone needs to learn to work with data.

Deepnote: Collaborative technical analysis with AI agents

Deepnote is a collaborative data workspace built around notebooks, with support for SQL, Python, R, visualizations, analytical applications, and agent-driven workflows.

Its environment is particularly useful for data scientists, engineers, and analysts who want to work together on code, manage complex execution environments, and turn technical analysis into reusable outputs.

Deepnote has also expanded into AI. Its Deepnote Agent can generate and modify code, execute analyses, inspect results, and help users build complete analytical workflows.

With its Agent Workspace, Deepnote also supports agents working through Slack, MCP, APIs, and development environments.

Key capabilities include:

• Collaborative notebooks for SQL, Python, R, and other analytical work.
• AI agents that create, execute, and refine analyses.
• Published dashboards, interactive applications, and Streamlit apps.
• Custom execution environments, GPU compute, and machine learning workflows.
• Open-source notebook formats and integrations with developer tools.

Deepnote has a broad set of capabilities, but its approach remains closely connected to notebooks as the central place where analysis, agent execution, and reusable analytical logic live.

That can be a strength for technical teams that want control over their analytical environment. The more important question is how well that approach serves people who don't want to work in a notebook at all.

Four key differences between Hex and Deepnote

1. Conversational self-service: Can the whole company work with data?

Deepnote makes technical teams more productive with AI, but that doesn't necessarily solve the broader problem of self-service analytics. Business users may still depend on analysts to answer questions outside existing reports and apps.

And increasingly, those users aren't waiting. They're exporting data into ChatGPT or Claude, asking questions, and building their own analyses. The result can be fragmented analytical work, with answers scattered across different tools and little visibility into whether they're using the same trusted business context.

Hex is built to bring those workflows together.

With Threads, anyone with the appropriate access can ask questions, investigate follow-ups, and explore data in natural language without learning SQL or navigating a report builder.

A sales leader might ask why pipeline declined last quarter, follow up by customer segment, and investigate whether changes in conversion rates or deal sizes explain the difference. The Hex Agent can perform the analysis and make the underlying work available for inspection or further exploration.

When a question requires more sophisticated analysis, the agent can also use Python rather than stopping at a simple warehouse query.

And through Slack and MCP, people can access Hex's governed analytical capabilities from the tools they already use instead of moving their questions into disconnected AI workflows.

At Neo Financial, employees began asking data questions through Hex in Slack, with queries governed by their existing Snowflake permissions. The company reported a 10.6x increase in data exploration as more employees gained access to trusted analytics.

The difference: Deepnote helps technical teams perform analysis with AI. Hex extends conversational self-service across the organization, reducing the need for business users to seek answers through disconnected, ungoverned tools.

2. AI-powered building: Who can create dashboards and applications?

Both Hex and Deepnote can turn analytical work into shareable dashboards and applications. Both have introduced AI capabilities that make creating those experiences easier.

Deepnote supports published apps, interactive inputs, and Streamlit applications. Its agents can generate and modify analytical work, while users can publish results for colleagues to interact with.

These capabilities make Deepnote useful for technical teams that want to turn their analysis into operational tools.

Hex makes the creation process accessible to a broader audience.

With Generative Apps, someone can describe the dashboard or application they want in natural language and have AI generate a customizable, code-backed experience.

A marketing leader might request a campaign performance dashboard with filters for channel and customer segment. A finance team could build a forecasting application where users adjust assumptions and immediately see their impact on revenue.

The resulting applications can include custom interfaces, visualizations, and interactive controls, with the underlying queries and logic available for inspection and refinement. That's important because not every analytical workflow fits a predefined dashboard or a handful of filters.

As AI makes it easier to generate applications, organizations also need a way to keep those assets maintainable and connected to trusted business logic rather than accumulating disconnected dashboards and one-off tools.

Hex connects AI-powered building with the same context and analytical environment used for conversational exploration and advanced analysis.

The difference: Deepnote helps practitioners build and publish analytical applications, increasingly with AI assistance. Hex makes it easier for people across the business to build what they need through natural language, without creating another layer of disconnected analytical work.

3. Interoperability: Where can people work with data?

Analytics increasingly happens outside traditional BI platforms. Employees ask questions in Slack, work with AI assistants like Claude and ChatGPT, and build tools in development environments such as Cursor.

Both Hex and Deepnote recognize this shift. Deepnote's Agent Workspace lets users invoke agents from Slack, IDEs, and other applications. Its MCP server also allows external agents to access workspace projects, run analyses, and work with notebooks using existing permissions. Hex likewise supports external analytical workflows through Slack, MCP, and its CLI.

The difference is what those integrations connect back to.

With Hex, users can access conversational analytics grounded in business context maintained across the existing data stack. That context can include trusted warehouse data, metric definitions, documentation, and semantic models.

For example, someone working in Claude can investigate a business question through Hex rather than downloading a CSV and asking a general-purpose assistant to interpret it without the company's definitions.

The resulting work stays connected to Hex's analytical infrastructure, where data teams can inspect and improve the underlying logic.

That becomes increasingly valuable as more employees adopt AI tools. Otherwise, organizations risk creating separate pockets of analysis, each with different assumptions about the business.

With AI data sprawl, broad access to AI analytics creates a need for shared context and visibility across tools.

The difference: Both platforms support external agents. Hex's approach is designed to keep AI analytics connected to shared business context and governance, even as employees work across different applications.

4. Context and governance: How can the data team trust AI at scale?

Making it easier for everyone to analyze data creates a new challenge: how do you ensure answers remain accurate and consistent when people are no longer going through the data team?

Traditionally, analysts could control much of that process by preparing trusted datasets, building reports, and reviewing results before publishing them. But when hundreds of employees begin asking AI agents questions independently, manually reviewing every answer becomes impractical.

Deepnote provides governance capabilities around workspace access, permissions, execution, and AI usage. Its administrative tools support monitoring agent activity and managing how projects and notebooks are shared. Agents can also draw on context from the workspace's existing analytical assets and integrations.

Those capabilities are useful for managing technical workflows and agent execution, but they don't necessarily address the broader challenge of governing answers being generated independently across the organization.

Hex is built around that challenge. Its context architecture allows agents to use information from across the data stack, including endorsed warehouse tables, metadata, Guides, semantic models, and existing analytical work.

Teams can also author semantic models in Hex or sync definitions from tools like dbt MetricFlow, Cube, and Snowflake, avoiding the need to recreate every business definition inside another proprietary modeling system.

And context isn't something teams configure once and forget. Business definitions change, new questions emerge, and agents encounter incomplete or outdated information.

Context Studio gives data teams visibility into the questions people are asking, how agents use context, and where answers encounter problems. It also helps teams evaluate changes and identify where additional context could improve accuracy.

That creates a feedback loop between real usage and the ongoing work of maintaining trusted business context, including analysis happening through supported external entry points.

The difference: Deepnote provides controls for managing analytical workspaces and agent execution. Hex adds dedicated context management and observability to help organizations maintain consistent, trustworthy AI answers as usage expands across the business.

Which platform is right for your team?

Both platforms support sophisticated analytical work, but they fit different organizational priorities.

Choose Deepnote if your priority is collaborative technical analysis

Deepnote is worth considering if your primary goal is giving data practitioners a flexible environment for collaborative analysis, data science, and agent-driven development.

It offers strong notebook functionality, support for SQL, Python, and R, customizable execution environments, and the ability to publish dashboards and applications. Its open-source approach is another meaningful advantage for teams that want portability and control over their analytical assets.

For organizations doing substantial machine learning development, requiring custom Python environments, or prioritizing notebook compatibility, these capabilities can be valuable.

If most of your analytical work happens within a dedicated technical team, it's a capable option.

Choose Hex if your priority is AI analytics across the company

Hex is better suited to organizations that want to make analytics accessible well beyond the people who already know SQL and Python.

Business users can investigate questions through natural language, create dashboards and applications using AI, and access governed analytics from tools like Slack and Claude.

Data teams retain the technical depth needed for advanced analysis while gaining the context management and observability capabilities required to support broader AI adoption.

Rather than having technical teams work in one environment while business users generate answers somewhere else, Hex provides a shared foundation for AI analytics.

For organizations looking to make AI analytics a capability available throughout the company without sacrificing trust, that's the central advantage.

A real-world example: EliseAI expands self-service across the company

At EliseAI, the data team needed to support a growing organization without becoming the bottleneck for every analytical request. The company adopted Hex to give business users more direct access to data through conversational analytics and interactive applications.

That shift helped employees investigate one-off questions themselves while making recurring reporting workflows easier to reuse.

For example, EliseAI turned a quarterly business review process that previously required roughly two days of preparation into an application that produces customized reports in three clicks, reducing preparation time by 97%.

The company also reported that 91% of employees now access data through Hex.

The important result isn't simply that analysts can produce reports faster. It's that more of the organization can work directly with data, while the data team maintains the foundation that makes those answers trustworthy.

What about pricing and technical requirements?

Pricing and implementation requirements still matter, particularly when comparing platforms designed for different audiences.

Hex vs. Deepnote pricing

Hex offers a free Community plan, with Professional starting at $36 per Editor per month and Team at $75 per Editor per month. Enterprise pricing is customized and includes additional governance, security, and business-user access capabilities.

Deepnote offers a free plan for up to three editors, with Team starting at $39 per editor per month when billed annually. Enterprise pricing is customized. Deepnote also includes usage credits for AI and compute in its paid plans.

For teams comparing technical editor licenses, Deepnote may offer a lower starting cost. But the total investment depends on what you're trying to accomplish.

If the goal is to enable broader self-service analytics, consider the cost of business-user access, AI usage, warehouse compute, and the additional tools needed to support trustworthy reporting and analysis.

A lower price per editor doesn't necessarily translate into a lower total cost when hundreds of employees need access to analytical capabilities.

How do their technical environments compare?

Both platforms support SQL and Python analysis, collaborative workflows, and integrations with cloud data warehouses.

Deepnote offers flexible execution environments, including custom Docker images, GPU compute, and support for working with notebooks through development tools like VS Code and Cursor. Its open-source notebook format and conversion tools can also appeal to teams that want portability between development environments.

Hex supports advanced SQL and Python analysis, reactive execution, and integrations with existing warehouse and semantic modeling infrastructure. For teams focused on analyzing warehouse data and extending those capabilities to business users through AI, Hex offers a broader set of integrated analytical experiences.

The relevant question isn't which platform has the longest list of notebook features. It's whether those technical requirements are the primary reason you're purchasing the platform.

Moving from Deepnote to Hex

Migrating from Deepnote to Hex involves more than importing notebooks. The platforms organize analytical work, applications, and execution environments differently, so some workflows will require adaptation.

Hex supports importing existing analytical work and using its AI agent to help recreate analyses, dashboards, and applications. Its switching guide recommends prioritizing important analytical assets and business context rather than migrating every historical report.

For organizations also consolidating traditional BI tools, AI can make that process significantly easier.

At LangChain, the team used the Hex Agent to recreate approximately 90% of its legacy dashboards on the first pass and completed its BI migration in six weeks. LangChain wasn't migrating from Deepnote specifically, but the example demonstrates how AI can reduce the work involved in rebuilding analytical assets. Before migrating, validate critical SQL and Python workflows, packages, permissions, reporting schedules, and published applications.

The biggest opportunity may be broader than replacing an existing technical tool. It's using the transition to make analytics accessible to far more people across the organization.

The bottom line: Different approaches to working with data

Hex and Deepnote both support sophisticated data analysis, collaboration, and AI-assisted workflows.

Deepnote remains a compelling option for technical teams that value notebook-centered development, customizable environments, and the flexibility to build and run analytical workflows with agents.

Hex is designed for a broader goal: making the entire organization capable of working with data through AI.

That means enabling business users to ask questions and investigate results in natural language, create dashboards and applications without coding, and access analytics from the tools they already use.

It also means giving data teams the context, governance, and observability to ensure that broader access doesn't come at the expense of trust or create a fragmented collection of AI-generated answers.

The choice between Hex and Deepnote comes down to whether you're primarily looking for an environment for technical analysis or a platform that can make trusted AI analytics accessible across your company.

Request a demo to see how Hex works for your team, or explore our other analytics platform comparisons.

Frequently asked questions

Is Hex or Deepnote better for self-service analytics?

Hex is designed for company-wide conversational self-service through Threads, allowing business users to ask questions, investigate results, and build analytical experiences without knowing SQL or Python.

Deepnote offers AI-assisted analysis, interactive dashboards, and agent workflows, but its experience is more closely organized around notebooks and technical analytical projects.

For organizations prioritizing natural-language exploration across a broad business audience, Hex is generally the stronger fit.

Can both Hex and Deepnote create dashboards and apps with AI?

Yes. Both platforms support AI-assisted creation and publishing of analytical outputs.

Deepnote agents can generate analytical work that becomes dashboards or applications, including notebook-based and Streamlit experiences.

Hex's Generative Apps lets users create customizable, code-backed dashboards and applications through natural language, integrated with the same analytical and governance capabilities used across the platform.

How do Hex and Deepnote handle AI governance?

Both platforms provide access controls and administrative capabilities for managing analytical work and AI usage.

Hex additionally offers Context Studio for observing AI-generated answers, identifying context gaps, evaluating improvements, and helping data teams maintain answer quality as self-service adoption grows.

Deepnote focuses more heavily on workspace permissions, execution controls, and monitoring agent activity.

Does Hex support advanced SQL and Python analysis?

Yes. Hex supports SQL and Python for advanced analysis, including statistical modeling, forecasting, and custom applications. Analysts can work directly with code or use AI to help generate, modify, and investigate analysis.

These capabilities are part of Hex's broader AI analytics platform, which also supports conversational self-service, AI-powered building, and governed analytics across external tools.