Hex’s Collaborative Notebooks Are Squeezing Mode Analytics Out

The Notebook That Does More

Hex Technologies has built something that looks familiar on the surface – a data notebook, not unlike what analysts and data scientists have used for years. But the product it has assembled over the past few years functions less like a scratchpad and more like a full analytics workspace. SQL editors, Python cells, drag-and-drop visualizations, and shareable apps all live inside the same document. That combination is putting pressure on Mode Analytics, a platform that carved out a strong niche with SQL-first business intelligence but has struggled to keep pace with what modern data teams now expect from a single tool.

Mode built its reputation on giving analysts a clean environment to write SQL and publish reports. For a certain kind of data team – one that lives in queries and cares deeply about version control and collaboration – Mode was a natural fit. But the product’s boundaries have always been visible. When analysts needed to move beyond SQL into Python, or when they wanted to build something that looked less like a report and more like an interactive app, Mode required workarounds. Hex does not.

What Hex Actually Built

Hex’s core architecture treats every project as a notebook with cells that can run SQL against connected warehouses, execute Python, render charts, or display no-code components – all sharing the same state. An analyst can pull raw data with a SQL query in one cell, reshape it in Python in the next, and then hand the resulting output to a slider or dropdown that a non-technical stakeholder can use without touching any code. This kind of top-to-bottom workflow used to require stitching together multiple tools.

The collaboration layer is where Hex has invested heavily. Multiple users can work inside the same project simultaneously, with changes syncing in real time, similar to how Google Docs behaves. Mode offers sharing and scheduling, but its collaboration model is closer to file handoffs than live co-editing. For data teams that now include a mix of engineers, analysts, and product managers all trying to work from the same source of truth, that distinction matters more than it once did.

Hex also ships a “magic” AI layer that writes and edits SQL and Python based on plain-language prompts. This is not a peripheral feature – it sits inside the editing experience and reduces the friction for analysts who are competent but not fluent in Python, which describes a large share of Mode’s existing user base. A Mode analyst who keeps hitting the ceiling of what SQL alone can do has a clear migration path into Hex, and the AI assistance lowers the learning curve further.

Mode’s Structural Problem

Mode has not stood still. The company has added Python notebooks and a visual explorer called Mode Studio, and it built an early reputation among growth-stage companies for being approachable and fast to deploy. But Mode’s product architecture still reflects the priorities of the era when it was built – when SQL reporting was the dominant workflow and the main ask from data teams was clean query management with decent charting on top.

That era is largely over. Data teams today are expected to build internal tools, run ad hoc analyses, and produce outputs that non-technical stakeholders can interact with directly. The line between “analytics” and “data app” has blurred. Mode’s user experience does not fully close that gap, and the product’s visual identity as a reporting tool makes it harder to position as an end-to-end workspace even when new capabilities get added.

Where the Competitive Pressure Lands

The category that both companies occupy – sometimes called the “analytics notebook” or “modern BI” space – has gotten crowded quickly. Deepnote, Observable, and Databricks Notebooks all compete for some version of the same workflow. But the most direct comparison is still Hex versus Mode, because both are targeting the same persona: a data analyst working inside a company, not a data scientist building models. That persona is growing fast as companies hire analytically-minded generalists who are comfortable in SQL and increasingly comfortable in Python, but who are not writing production machine learning pipelines.

Hex has raised significantly more capital than Mode at recent valuations, which gives it room to keep expanding the product surface. Mode was acquired by ThoughtSpot in 2023, a move that gave it resources and a larger go-to-market footprint but also created questions about roadmap independence. When a standalone product gets absorbed into a larger platform, the product’s evolution becomes subject to the parent company’s strategic priorities, which do not always align with what the standalone user base needs next.

The ThoughtSpot acquisition context matters for customer retention. Data teams evaluating tools for the next two or three years are not just buying features – they are betting on a product direction. A team choosing between a venture-backed independent company actively shipping and a product sitting inside a larger BI vendor has to weigh how much the product will change, and for whose benefit. That calculus has historically favored the independent vendor when the acquirer is known primarily for a different kind of product.

Hex’s pricing model also creates a natural wedge. The company offers a free tier that lets small teams get started without a procurement conversation, which is how analyst tools tend to spread inside organizations – one person starts using it, shares a project, and the tool earns trust before any formal evaluation happens. Mode’s pricing has always been more enterprise-oriented, which works well for top-down sales but leaves a gap at the bottoms-up adoption layer where Hex is actively competing. The question now is whether Mode, operating under ThoughtSpot’s umbrella, can close that gap before Hex’s user base inside data teams grows large enough to make the replacement conversation irrelevant.