Hex vs. Mode: complete comparison

Hex and Mode have a lot in common. Both emerged to give data analysts a better way to work with and share data, combining code, visualizations, and analytical work in one environment.

But as AI has started fundamentally changing what's possible with data, the two platforms have taken different paths.

Mode remains primarily focused on analyst-led workflows, giving data practitioners the tools to query, analyze, and distribute results. Hex has evolved into an AI-native analytics platform where anyone can ask questions, investigate results, and build dashboards or apps through natural language.

That distinction matters because the biggest change happening in analytics isn't just that AI helps analysts work faster. It's that AI is dramatically lowering the technical bar of who can work with data and what they can accomplish.

So which platform is better positioned for what your organization needs today, and where analytics is heading next?

TL;DR: Hex vs. Mode

Choose Hex if: You want to expand analytics beyond the data team, allowing business users to ask questions, investigate results, and build with data through AI, while analysts retain the full power to extend into deeper analysis. Hex combines these workflows with shared business context, governance, and observability to make AI analytics trustworthy at scale.

Choose Mode if: Your primary need is an analyst-led environment for writing SQL, working with Python or R, building visualizations, and distributing recurring reports. Mode remains a capable option for organizations where most analytical work is performed by data practitioners.

Both platforms support code-based analytics. The biggest difference is how far they've extended those capabilities beyond the people writing the code.

Hex vs. Mode at a glance

The important distinction is whether your organization wants to optimize a traditional analyst workflow or make AI a new interface for working with data across the company.

From code-first analytics to AI-native analytics

To understand where Hex and Mode differ today, it helps to look at where they started.

Shared roots in code-first analytics

Mode was founded in 2013 by Derek Steer, Benn Stancil, and Josh Ferguson, who previously worked together at Yammer. The company set out to make data analysis more collaborative by bringing SQL queries, Python and R notebooks, and visualizations into one reporting environment.

Before tools like Mode, analysts often had to move between SQL editors, notebooks, spreadsheets, and BI platforms to complete an analysis and share it with stakeholders. Mode made that process more efficient, giving analysts a central place to query, analyze, and distribute their work.

Hex emerged several years later, founded by Barry McCardel, Caitlin Colgrove, and Glen Takahashi in 2019. Its early focus was similarly code-first: combining SQL, Python, and interactive data applications so data teams could analyze data and share their work with a broader audience.

Both products addressed a real problem. Analysts needed more flexible tools for working with data, and organizations needed better ways to make that work accessible to others.

How the platforms diverged

In June 2023, ThoughtSpot announced its $200 million acquisition of Mode, completing the transaction that July.

The acquisition was intended to combine Mode's code-first analytical capabilities with ThoughtSpot's strengths in self-service BI. Today, Mode continues to offer its own product, centered on SQL, Python, R, reporting, and visualization, while ThoughtSpot offers broader conversational analytics capabilities through its product portfolio.

Hex took a different approach, expanding its original notebook foundation into an AI-native analytics platform.

Today, the Hex Agent helps broader business users investigate questions in natural language, perform deeper analysis, and build interactive dashboards and applications. Those workflows draw on shared business context and connect back to the analytical environment data teams already use.

The distinction isn't that one platform has AI and the other doesn't. Mode has AI assistance, and ThoughtSpot offers conversational analytics elsewhere in its portfolio.

The bigger difference is what each platform lets people do with AI, and how far those capabilities extend beyond the data team.

Five key differences between Hex and Mode

1. Conversational self-service: Who can ask questions?

The traditional BI model assumes that analysts perform the analysis and business users consume the results.

Mode largely follows that model. Analysts write queries, build reports, and publish dashboards stakeholders can review and interact with. When someone needs an answer outside an existing report, they often need an analyst to produce additional analysis.

Mode also offers AI Assist, which lets users generate SQL from natural-language instructions within the editor. It's useful for accelerating query writing, but remains oriented around the analyst's SQL workflow.

Hex approaches self-service differently. With Threads, business users can ask questions in natural language, investigate follow-ups, and explore data without learning SQL or navigating a report builder.

A sales leader might ask why pipeline declined, follow up by customer segment, and investigate which factors contributed most. The agent can generate the underlying analysis and return results that business users and analysts can inspect.

And when the investigation requires more than a warehouse query, the Hex Agent can use advanced analytical capabilities, including Python.

The difference: Mode helps analysts answer questions more efficiently. Hex is built to let far more people ask and investigate those questions themselves.

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

Mode provides a traditional environment for building reports. Analysts can write SQL, explore results with Python or R, create visualizations, and configure parameters that let stakeholders interact with their work.

But creating something new generally still requires someone comfortable working in Mode's analytical environment.

Hex makes it easier for anyone to build and share analytical work with AI. Users can describe what they want to create, whether that's a campaign dashboard, revenue forecasting tool, or customer retention application.

With Generative Apps, a natural language prompt becomes a customizable, code-backed application. The underlying queries, calculations, and application logic remain accessible, so data teams can inspect and refine the results.

This is particularly valuable when a workflow needs more than a static dashboard. Finance teams might need to adjust forecasting assumptions, while customer success teams might want to explore churn scenarios through interactive controls.

Both platforms allow analysts to produce reusable analytical work. Hex extends that ability to people who may never have written SQL or Python.

The difference: Mode gives analysts tools to build analytical experiences. Hex uses AI to make building accessible across the entire organization.

3. Advanced analytics: What happens when questions get complicated?

Despite its focus on AI and self-service, Hex hasn't abandoned the technical capabilities it was built around.

Hex combines SQL and Python, allowing analysts and data scientists to query data, perform statistical analysis, build models, and create visualizations.

The Hex Agent can generate and modify code, investigate errors, and assist with more complex analysis using project and business context. Analysts can inspect and edit the underlying work themselves.

Mode has meaningful strengths here, too. Its SQL editor, Python and R notebooks, and Helix engine provide a mature environment for practitioners who prefer working directly with code.

For established analytics teams with recurring SQL-based workflows, Mode may already provide much of what they need.

The distinction becomes more significant when those capabilities need to reach beyond the data team.

In Hex, business users can start with a natural-language question and rely on the agent to investigate further, while analysts retain full access to the technical environment behind the work.

The difference: Both platforms support advanced analysis. Hex brings AI throughout that workflow and makes insights easy to share with non-technical users.

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

Making analytics more accessible introduces a challenge: ensuring everyone gets answers grounded in the same business logic.

Mode supports traditional governance through permissions, organized reports, and integrations with tools like dbt. Analysts maintain control over the queries and calculations behind published results.

That works well when most analytical work passes through the data team. But when hundreds of employees start asking AI agents questions independently, governance needs to extend beyond analyst-authored reports.

Hex is designed around that challenge. Its context architecture allows agents to draw on business knowledge across the data stack, including endorsed warehouse tables, metadata, documentation, Guides, semantic models, and existing analytical work.

Teams can author semantic models in Hex or sync definitions from tools like dbt MetricFlow, Cube, and Snowflake, reusing their existing modeling investments.

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

Context Studio gives data teams visibility into what users are asking, how agents use context, and where answers encounter problems. It also helps teams evaluate and prioritize improvements rather than manually reviewing every generated query.

The difference: Mode primarily governs analysis through analyst-controlled workflows and reporting assets. Hex also provides context management and observability for governing AI-generated analysis at scale.

5. Interoperability: Where can people work with data?

Traditional BI assumes people will visit a dedicated platform to view dashboards, investigate questions, or create reports. Increasingly, employees want to work with data from applications they already use, including Slack, Claude, ChatGPT, and Cursor.

Mode supports integrations for sharing reports, scheduling delivery, and connecting analytical workflows to other systems. These help analysts distribute their work without requiring stakeholders to open Mode for every report.

Hex also makes its analytical capabilities available through external AI workflows. Through MCP, Slack, and the Hex CLI, users can access governed analytics from other tools while drawing on shared business context.

Rather than analyzing a downloaded CSV with no connection to approved definitions, an employee can investigate company data through Hex's analytical infrastructure. That becomes increasingly important as organizations adopt more AI tools. Teams need a way to extend access without fragmenting the context and governance behind every answer.

The difference: Mode helps analysts distribute their work across the organization. Hex brings governed AI analytics into the tools and agents employees already use.

Which platform is right for your team?

The right choice depends less on individual features and more on what you're trying to accomplish with analytics.

Choose Mode if your priority is analyst-led reporting

Mode remains a strong option for organizations where most analysis happens within a dedicated team of SQL, Python, and R practitioners.

Its strengths include a mature SQL environment, flexible reporting workflows, visual exploration, and scheduled distribution. Analysts can create parameterized reports and share results without requiring business users to write queries themselves.

If your primary goal is improving the productivity of a small group of analysts, and stakeholders are comfortable consuming published reports, Mode may be a good fit.

Choose Hex if your priority is AI-native analytics across the company

Hex is better suited to organizations that want to move beyond analysts producing answers for everyone else.

Business users can investigate questions conversationally, analysts retain workflows for advanced analytics, and anyone can create dashboards and applications with AI.

Those workflows share a foundation of business context, permissions, and observability, allowing data teams to spend less time fulfilling repetitive requests and more time improving the infrastructure for trusted self-service.

For organizations expecting AI to dramatically expand how many people work with data, that's the central distinction.

A real-world example: LangChain's move to AI-native analytics

At LangChain, the data team wanted to move beyond traditional BI and make analytics accessible to more of the company.

The team used the Hex Agent to help migrate its existing dashboards, providing configuration files from its previous BI platform and prompting the agent to recreate the analytical work. The agent completed roughly 90% of the dashboard recreation on the first pass, and the team completed the migration in six weeks.

But the more important change came afterward. Instead of primarily fulfilling reporting requests, the data team shifted toward maintaining the context that helped employees find trusted answers independently.

LangChain illustrates a broader opportunity for teams migrating from traditional BI tools: moving from analyst-driven report production toward company-wide AI-native analytics.

What about pricing and migration?

Pricing and implementation still matter, even if they're not the main reason to choose one platform over the other.

Hex vs. Mode 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 security, governance, and business-user access capabilities.

Mode offers a free Studio tier for small teams, while its Pro and Enterprise plans use custom pricing. See Mode's website for current details.

When comparing costs, consider more than the editor license: business-user access, AI usage, warehouse compute, and ongoing data-team work all affect the total investment.

A lower cost per analyst may not translate into a lower overall cost if the goal is to enable hundreds of employees to work independently with data.

Migrating from Mode to Hex

Moving from Mode to Hex involves more than transferring SQL queries. The platforms organize notebooks, reports, parameters, and applications differently, so some workflows need to be rebuilt.

However, AI can significantly reduce that effort. Hex's switching guide recommends prioritizing important analytical assets and trusted business context instead of migrating every historical dashboard.

The Hex Agent can help recreate analysis from existing queries, configurations, and descriptions of desired outputs.

As the LangChain example illustrates, much of this work can be accelerated with AI, although effort will depend on the complexity of your Mode environment.

Before migrating, validate critical queries, permissions, reporting schedules, and stakeholder workflows.

The bottom line: Two platforms built for different eras of analytics

Hex and Mode share a similar origin story. Both helped analysts work more effectively by combining code, visualizations, and shareable results.

Mode remains capable for teams whose analytical work is primarily driven by SQL and Python practitioners. But AI is fundamentally changing what people expect from analytics.

Business users increasingly want to ask questions in natural language, investigate answers, and build what they need without waiting on the data team. They also want to do that from the applications where they already work.

Data teams, meanwhile, need to maintain trust as more people generate analysis independently.

Hex has evolved to support that shift, combining its code-first foundation with conversational analytics, AI-powered building, and shared context and observability.

The choice between Hex and Mode comes down to whether you're optimizing analytics for the people who already work with data, or preparing for a world where AI makes the entire organization capable of doing so.

Request a demo to see how Hex compares for your team, or explore other BI platform comparisons.

Frequently asked questions

Does Hex support the scheduled reporting capabilities Mode offers?

Yes. Hex supports scheduled project runs, alerts, and recurring analytical workflows. Agent Tasks can run analyses on a schedule and deliver findings through Slack or email. Mode also offers established scheduling and delivery options, including parameterized reports and different export formats. Teams with specific scheduling or export requirements should compare them directly.

Can both Hex and Mode connect to cloud data warehouses?

Yes. Both support major warehouses such as Snowflake, BigQuery, and Redshift, alongside additional database connections. For less common sources or particular authentication requirements, check each platform's current integration documentation.

Is Hex only for teams adopting AI analytics?

No. Hex remains a full-featured analytical environment for data teams. Analysts can write queries, perform advanced analysis, create visualizations, and publish dashboards and apps without relying on AI. Those workflows coexist with conversational exploration and AI-powered building, allowing data teams to scale AI analytics across the entire organization with trust.