Mode vs Hex: Collaborative Data Notebooks Compared (2026)

Mode is the better pick if your team is 80%+ SQL and cares about governed reporting. Hex is the better pick if your analysts write real Python and need to ship interactive apps to stakeholders. Both connect to the same warehouses and both outgrew Jupyter's sharing model years ago. The decision almost always comes down to how much Python your team actually writes versus how much they wish they didn't have to.

Side-by-side comparison

How does each workflow actually feel?

The daily experience is different enough that it shapes how your team thinks about analysis. Here's what each path looks like from data to deliverable.

MODE WORKFLOW
=============
  SQL Editor (schema browser, saved queries)
       |
       v
  Query 1 ──> Query 2 ──> Query 3
       |           |           |
       v           v           v
  Result sets (tables, can reference earlier queries)
       |
       v
  Chart builder (drag columns to axes)
       |
       v
  [Optional] Python notebook (pandas on result sets)
       |
       v
  Report  ──>  Scheduled refresh  ──>  Stakeholder URL

HEX WORKFLOW
=============
  Reactive notebook (all cells in one canvas)
       |
       +----> SQL cell ─────> DataFrame "orders"
       |                          |
       +----> Python cell ────> transform(orders)
       |                          |
       +----> Chart cell ─────> viz(transformed)
       |                          |
       +----> Input widget ───> parameter (date range)
       |          |
       |          +──> Changes propagate downstream
       |               (reactive DAG re-executes)
       v
  Toggle "App Mode" ──> Hide code ──> Published app URL
                                       (interactive, live)

The Mode path is sequential. You write SQL, get results, optionally bring Python in, then package everything into a report. The Hex path is a graph. Cells depend on each other, changes ripple downstream, and the notebook itself becomes the deliverable.

Which has the better SQL experience?

Mode, by a clear margin.

Mode was built around SQL. The query editor has years of iteration behind it: schema browsing with column types, query history, saved definitions, the ability to reference one query's results inside another query. If your team's workflow is "write SQL, visualize the result, share a link," Mode's editor feels like home. I've talked to teams of 20+ analysts who live in Mode's SQL editor all day and never touch the Python notebooks.

Hex has SQL cells, and they work fine. You write a query, name the output, and downstream cells can reference it as a DataFrame. But the SQL editing experience is thinner. The autocomplete is less mature. There's no dedicated schema browser panel — it's integrated into the notebook sidebar rather than being a first-class part of the editing surface. For teams that are 90% SQL, Mode's editor is noticeably better.

That said, Hex's SQL cells participate in the reactive DAG. Change an upstream input widget (say, a date range dropdown) and every SQL cell that references that parameter re-runs automatically. Mode's SQL queries are more manual — you re-run them explicitly or wait for a scheduled refresh. If your SQL queries are parameterized and you want live interactivity, Hex's model is more powerful even if the editor itself is simpler.

How does Python fit into each tool?

This is where the comparison gets lopsided.

Hex treats Python as a first-class citizen. Python cells live alongside SQL cells and chart cells in the same notebook. They participate in the same reactive execution graph — change a variable upstream, and every Python cell that depends on it re-executes automatically. You don't get the Jupyter problem of "I ran cell 7 before cell 4 and now nothing works." The execution order is determined by the dependency graph, not by which cell you clicked last.

Mode has Python notebooks, but they live in a separate section from the SQL queries. The workflow is: write your SQL, get result sets, then switch into a Python notebook that can access those results. It works. But the context switch is real — you're moving between two different editing paradigms inside the same report. The Python environment is also more constrained than Hex's, and the execution model is traditional sequential notebook, not reactive.

If your team writes Python daily — pandas transformations, statistical modeling, anything beyond basic aggregation — Hex wins this comparison convincingly. If your team reaches for Python once a quarter for something SQL can't express, Mode's approach is fine. The gap between "fine" and "first-class" only matters when Python is central to the work.

For a broader look at where notebooks fit in the Python data stack, Streamlit and Jupyter solve different halves of the same problem.

How do they handle collaboration?

Different philosophies, both functional.

Hex has real-time multiplayer editing. Two analysts open the same notebook, see each other's cursors, watch edits appear live. It works the way Google Docs works, and it works well. You can comment on individual cells, tag teammates, and resolve threads. For teams that pair on analysis or run working sessions where multiple people are exploring data simultaneously, this is a genuine productivity feature.

Mode's collaboration model is centered on the report. You share reports via URL, control access at the collection level, and stakeholders bookmark the links. Comments live on the report, not on individual queries. It's a broadcast model — one analyst builds, many consume — rather than a multiplayer model. For teams where the workflow is "analyst builds a report, team reviews it, stakeholders check it weekly," this works perfectly well. Not every team needs simultaneous editing.

Mode also has deeper enterprise governance features: collections, spaces, usage analytics, admin controls. If you need to know which 200 analysts are querying which data sources and who hasn't logged in this quarter, Mode's admin tooling is more mature. Hex is building out this layer but it's newer.

Can Hex actually replace a dashboard tool?

Partially, and this is Hex's most interesting differentiator.

Hex's app mode turns any notebook into a published application. Toggle it on, and the code disappears. What's left: charts, tables, markdown, and input widgets — dropdowns, sliders, date pickers, text inputs. A VP opens the URL, picks a region from a dropdown, and the entire analysis re-executes with that parameter. No code visible, no kernel to manage, no "click run all" instructions.

This bridges the gap between "analysis notebook" and "internal tool" without requiring a separate framework. Teams that would otherwise build a Streamlit app or a Jupyter-based dashboard can stay in Hex and publish directly from the notebook.

Mode doesn't have this. Mode reports are collections of charts and tables with scheduled refresh. They're readable and shareable, but they're not interactive applications. A stakeholder can look at a Mode report but can't change parameters and see results update live. For teams that need static reporting with governance, that's fine. For teams building internal analytics tools, it's a limitation.

What does pricing actually look like?

Hex is more transparent. Mode is more expensive but harder to pin down.

Mode uses custom pricing — you talk to sales. From conversations I've had with teams evaluating it, expect $35-75/user/month depending on volume, features, and whether you're bundling with ThoughtSpot. The pricing page says "contact us," which in 2026 usually means "we'll charge what we think you'll pay." If you want more detail on what the Hex side of this equation costs, we broke down Hex's pricing tiers separately.

Hex has a free tier (1 user, limited features), a Team plan at $28/user/month, and Enterprise pricing for larger deployments. For a 10-person team on Team, that's roughly $3,360/year — materially cheaper than Mode's typical pricing for the same headcount.

The hidden cost with both is switching. Mode's reports and Hex's notebooks are proprietary formats. You're not exporting to .ipynb and taking your work to another platform. Whichever you choose, plan on staying for a while.

Should you worry about the ThoughtSpot acquisition?

It's worth thinking about.

ThoughtSpot acquired Mode in 2023. Mode still functions as a standalone product, but the product roadmap has shifted toward ThoughtSpot integration. New features tend to serve the combined story — analysts in Mode, business users in ThoughtSpot's natural-language interface, shared data catalog — rather than Mode as an independent analytics platform.

If your organization uses ThoughtSpot, this is actually a strength. The integration gives you deep analytical work in Mode with self-serve BI in ThoughtSpot, and shared governance across both. If you're evaluating Mode without ThoughtSpot, factor in the possibility that Mode's independent roadmap may continue to narrow.

Hex is independently VC-backed and building for its own roadmap. The product ships features quickly. Whether that independence lasts depends on the usual startup dynamics, but for now, Hex's trajectory feels more self-directed.

FAQ

Can Mode and Hex connect to the same data warehouses?

Yes. Both support Snowflake, BigQuery, Redshift, Postgres, MySQL, Databricks, and other standard warehouses. The connector lists overlap almost entirely. The difference is what happens after the query runs, not which databases you can query.

Is Hex hard to learn if my team only knows SQL?

Not especially. SQL cells in Hex work exactly like you'd expect — write a query, name the result, see a table. The learning curve appears when you start using Python cells, reactive dependencies, and app mode. A SQL-only team can use Hex and never touch Python, but at that point you're paying for features you won't use, and Mode's SQL editor is better for pure SQL work.

Can I replace Tableau or Looker with either of these?

For internal analytics teams, maybe. Both Mode and Hex can produce dashboards and reports. But neither is designed for the "thousands of business users self-serve on dashboards" use case that Tableau and Looker serve. Mode and Hex are analyst-facing tools. The audience for their output might be broad, but the people building in them are technical.

Does Hex's reactive execution slow things down on large datasets?

It can. When you change an upstream parameter and every downstream cell re-executes, the total runtime depends on the heaviest cell in the chain. Hex mitigates this with caching — cells that don't depend on the changed parameter skip re-execution. But if your slowest SQL query takes 30 seconds, and it sits upstream of everything, a parameter change triggers a 30-second wait. Mode's explicit re-run model avoids this because nothing happens until you click run.

How do Mode and Hex compare on version control?

Hex is significantly ahead here. It offers git-like branching and merging inside the product — create a branch of a notebook, experiment, merge it back or discard it. Version history shows diffs between revisions so you can see exactly what changed. Mode tracks report versions but it's more basic: a timeline of saves, no branching, no merge workflow. For teams that iterate on shared analyses and need to track who changed what, Hex's model is closer to what you'd expect from a modern development tool.

What if my team doesn't want to write Python at all?

Then neither tool is the best fit. Mode works well for SQL-only teams, but you're leaving Python notebooks and much of the product surface unused. Hex requires Python for anything beyond basic SQL and charting. If your team wants to ask questions of their data in plain English and get dashboards without writing code, the category you want is AI-powered analytics — not collaborative notebooks. Deepnote, Hex, and Jupyter solve the notebook problem but they all assume someone on the team writes code.

What I'd pick today

For a team of SQL-dominant analysts who need governed reporting and are already in or considering the ThoughtSpot ecosystem: Mode. The SQL editor is excellent, the report model is straightforward, and the enterprise governance is mature.

For a team that mixes SQL and Python and needs to ship interactive analyses to non-technical stakeholders: Hex. The reactive notebook, app mode, and multiplayer editing solve real problems that Mode doesn't address.

For a team of 3-8 people building internal analytics tools and prototypes for stakeholders: Hex, and it isn't close. The notebook-to-app pipeline removes an entire deployment step. You'll spend less time explaining kernels to your VP and more time iterating on the analysis itself.

For a team that doesn't want to write Python or SQL at all — that wants to connect a warehouse, ask questions in English, and get answers back as dashboards: neither. That's a different category entirely, and it's where tools like Fastero sit. Connect your data, describe what you need, and the AI writes the queries. No notebooks, no code, no reactive DAGs to manage. Just questions and answers.

Related: Deepnote vs Hex vs Jupyter: Notebooks Compared | Jupyter vs VS Code for Data Science | Hex Pricing: Is It Worth It?

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