Count vs Hex: Which is Better? [2026]

Count is a collaborative data canvas - an agentic analytics platform built on an infinite canvas. Hex is a cell-based notebook with an analytics agent.

Choose Hex for heavy Python and ML work. Choose Count if you want to explore deeper on a canvas, analyse qualitative data alongside your metrics, keep agent queries (and cost) off your warehouse, and bring the wider business into the analysis so it actually drives a decision.

The difference is the surface, the kind of data each handles well, and what happens after the chart.

Count sits between BI tools, notebooks and chatbots. Where a notebook like Hex produces an analytical output, Count brings the full analytical workflow - exploration, reasoning, discussion, decision - into one workspace. A notebook gets you to a chart. Count gets the team to a decision. The problem worth solving is time-to-decision, not time-to-chart.

Hex: give analysts a powerful agentic notebook.

Count: turn that analysis into a decision the whole team trusts.

Count explores deeper than Hex's notebook

Hex gives you SQL, Python and AI in a linear sequence of cells, with an app-builder layer on top. It is a great notebook.

Count gives you all of that on an infinite canvas, and that is not cosmetic: the canvas is what lets you and the agent go deeper. You can branch into competing hypotheses, fork assumptions, lay hundreds of queries and charts out spatially, and backtrack without losing the thread. Analysis is not linear, and a notebook forces it to be. The room to spread out is the room to dig in.

Count blends qualitative and quantitative data; Hex is built for numbers

This is the difference most teams underestimate. Hex manages compute well within Python, but it is built for numerical, warehouse data. It is not strong with qualitative information.

Count treats qualitative and quantitative data as equals. Connect support tickets, sales-call transcripts, CRM notes, spreadsheets or documents via MCP sources and analyse them alongside your metrics in the same investigation. No new pipelines, no ticket to file - the question drives the analysis, not what has already been modelled. The most valuable answers usually combine the number with the reason behind it, and that is exactly the combination a notebook struggles to hold.

Every query Count's agent runs is auditable

An agent's answer is only worth acting on if you can see how it got there. In Hex the work lives in notebook cells; in Count every query the agent runs is laid out on the canvas - visible, editable, and there to build on. No black boxes. That auditable methodology is what makes the output safe to take into a decision, and easy for a colleague to verify or extend.

"I can be on a call regarding a data issue and everyone on that call can view the queries I'm writing in real time, see the data, and visualise the problem. It gives end users confidence in how we got to the end dataset." - the data team at Loqbox

Count brings the business into the workflow

Hex co-edits inside a notebook and routes business users to Threads, a separate, lighter interface. Two surfaces, two audiences.

Count is one surface for everyone. Real-time multiplayer means analysts, stakeholders and the agent in the same canvas, with sticky notes, screenshots, comments and live presentations. Stakeholders follow the logic, challenge the numbers, and add the context the analyst does not have. The biggest unlock with a notebook-heavy workflow is rarely writing SQL faster - Hex is already fine at that. It is bringing the wider business into the analytical work so they buy into it and act on it. That is the gap between an analysis that ships and an analysis that changes a decision.

Warehouse cost: is Count cheaper than Hex?

It depends on usage, but the architectures pull in different directions. Agents are query-hungry, and Hex's larger machines and GPU compute are metered, pay-as-you-go, so spend climbs with usage. Count's compute layer distributes work across the warehouse, Count's servers and the browser, running ~80% of queries outside the data warehouse with no per-query charge. That keeps agentic exploration off your warehouse bill, and Count also includes free viewers and collaborators where broad access in a per-editor notebook adds up. For sustained heavy Python and ML compute, Hex's model can still be the right one.

"The ability to query the smallest granularity, cache it in Count's local DuckDB server, and play, aggregate and recalculate as much as you like without any further costs - this is amazing." - the data team at Runa

Where Hex wins

We are not going to pretend otherwise. Hex is ahead in a few places that matter:

• Full Python VMs today. Dedicated VMs with compute sized to your plan, GPU included - the better choice for heavy data science and ML right now. Count's Python runs in-browser (WASM) today, with full Python VMs rolling out.
• Embedded analytics. Mature embedding and generative data apps. Count's embedding is lighter.
• Self-hosted / private cloud. Available on Hex's enterprise tiers. Count is cloud-only (SaaS, with US/EU data residency).
• A long track record in data science. Hex is well established with data science and ML teams.

If your work is mostly solo, code-heavy data science, Hex is a genuinely excellent tool.

Count vs Hex: feature comparison

Example: investigating a revenue dip in Count vs Hex

Revenue dropped 8% last week and you need to know why.

In Hex, the Notebook Agent handles the quantitative side well. It writes the SQL, breaks revenue down by region, plan and cohort, and surfaces where the dip is concentrated - a rigorous numerical answer. What it cannot easily do is the rest of the picture: it works in a linear notebook rather than a branching canvas, it is weak at bringing in the qualitative evidence that explains the number (the support tickets and call transcripts hinting at a billing problem), and the output is a notebook to read afterwards rather than a shared space to reason in.

In Count, the agent runs the same breadth of queries and lays dozens of charts out spatially, so you can branch a new line of investigation off any of them. Then you pull in the support tickets and sales-call transcripts via MCP sources and analyse them next to the metrics, and the billing-bug hypothesis the numbers only hinted at becomes obvious once the qualitative evidence is in the same view. Every query is auditable, stakeholders weigh in on the canvas, and you leave with a decision and a record of how you reached it.

Hex gets you a rigorous quantitative answer. Count gets you the whole picture - the number and the reason - and the team to a decision.