Hex Alternative: When You Need an AI Data Analyst, Not a Notebook

If you're comparing Hex to other tools, the first question to settle is not which product is better. It's which job you're hiring for. Hex is a collaborative data-notebook and analytics workspace where data people write SQL and Python, build charts, and ship interactive reports and data apps (as of 2026). An AI data analyst is a different thing for a different person: it lets a business user ask a question in plain language and get an investigated answer back, on governed definitions, with the work shown.

Said in one line you can quote: Hex is where an analyst builds the analysis; an AI data analyst is what a non-analyst asks when they have a question and no analyst free. Whether Hex is the right pick or the wrong one depends entirely on which of those two you actually need.

Key takeaways

• Hex is a notebook-based analytics workspace built for data practitioners: SQL plus Python cells, a reactive notebook, charts, and shareable reports and data apps (as of 2026). It is built for exploratory analysis and for packaging that analysis for others to read.
• Hex is still notebook and app-builder centered, but it now includes agentic and conversational self-serve workflows for broader teams. The fair comparison is whether Hex's notebook-agent model or Sundial's governed investigation model better fits your users. A notebook still assumes someone who can author the analysis. That person is usually an analyst, and the analyst is the queue.
• An AI data analyst fills that other job: a non-technical person asks in plain language, the agent runs the multi-step investigation itself, and it returns an answer grounded in your governed definitions.
• The two are not the same category, so "Hex vs. an AI data analyst" comes down to "a workspace for people who build analysis vs. a way for everyone else to get answers." Some teams want both. The honest test below tells you which you need.

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What is Hex, and what is it built for?

Hex is a collaborative data notebook: a workspace where a data person writes SQL and Python together, in cells, and turns the result into a chart, a report, or an interactive data app (as of 2026). Its design is built around the reactive notebook, where editing one cell automatically reruns the cells that depend on it, so the analysis stays consistent as you change it. It connects to your warehouse, it is collaborative the way a shared document is, and it has added AI-assisted features that help write and edit cells.

That is a useful tool. If your job is to do open-ended exploration, prototype a model, or build a polished report a stakeholder will read, a notebook is a natural home for it. The people who get the most out of Hex are the people who can already do analysis and want a faster, more shareable place to do it.

None of what follows is a knock on that. It is about who the tool serves.

The thing to be clear-eyed about: a notebook, including one with AI cell-assist, still assumes an author. Someone who knows SQL or Python sits down and builds the analysis. The output can be shared widely, but the making of it stays with a technical person. That is the line that matters for the next section.

Who is the tool actually for: the builder or the asker?

Every analytics tool serves one of two people, and they have opposite needs. One is the builder: the analyst or data scientist who authors the analysis. The other is the asker: the PM, the marketer, the founder, the operator who has a question and needs an answer, and who is not going to open a notebook to get it.

A notebook serves the builder well. It hands the builder a powerful, flexible canvas. But it does almost nothing for the asker, because the asker cannot write the first cell. So in a notebook-centered workflow, the asker's question becomes a request to the builder, and the builder's queue is the bottleneck the whole company waits on. This is the BI backlog by another name: the data team is the gate, and every ad-hoc question waits in line.

An AI data analyst is built for the asker. The asker types "why did sign-ups drop in Canada last week" in plain language, and the agent does the work a builder would have done: plans the investigation, runs the queries, checks the result, and returns the answer. The builder is freed from the queue. Here is the split, plainly:

Neither column is better. They serve different people. The mistake is buying a builder's tool to solve an asker's problem, then wondering why the data team is still the bottleneck.

Isn't a text-to-SQL feature in a notebook the same thing?

No, and this is the most common place the comparison goes wrong. Many notebook tools, Hex included, have added AI that turns a sentence into a SQL cell or edits your code. That is text-to-SQL, and it is useful. It is also a different thing from an AI data analyst, in the same way that a spell-checker is a different thing from a writer.

The gap is between answering a query and answering a question. "What was revenue last week" is one query, and text-to-SQL handles it. "Why did revenue drop last week" is not one query. It is a dozen, plus the judgment to know which ones matter: decompose revenue into its drivers, find the one that moved, slice it by segment, rule out a tracking change before blaming the product, then say what happened.

A text-to-SQL cell writes one query and stops. It has no concept of that method.

That method is what an analytics playbook encodes: the recorded steps a senior analyst would take for a recurring question, sometimes called a "skill," so the agent runs the same rigorous investigation every time instead of improvising. A text-to-SQL feature also hands you a query with no way to know if it's right. The model can write SQL that runs cleanly, returns a confident number, and is measuring the wrong thing.

An AI data analyst differs on both counts: it runs many steps instead of one, and it carries context about your business so the answer is grounded, not guessed.

A text-to-SQL cell answers the query you typed. An AI data analyst answers the question you actually have, which usually takes a dozen queries and the judgment to sequence them.

Why governed definitions matter more than the editor

An answer is only as trustworthy as the definitions it's built on, and a notebook leaves those definitions to whoever wrote the cell. In a notebook, "active customer" means whatever the SQL in that particular cell says it means. Two analysts can write two different definitions in two different notebooks, both run clean, and now the CEO gets two numbers for the same metric in one meeting.

An AI data analyst reads from a semantic layer, the core of a broader context layer. That layer holds three things: the official definition of every metric, how your tables relate, and which source is the truth when two systems disagree. The agent measures "active customer" the way your company has agreed to measure it, every time, no matter who asks. That is the difference between answers that are fast and answers you can put in a board deck.

Here is the part most tools skip: a notebook reads whatever definition the cell author typed, and most "AI on your data" tools assume you already built a governed layer for them to read. Hex gives you a flexible canvas and works with modeled data, but its center of gravity is not an agent that builds the governed layer from your raw tables. Sundial's is.

Sundial works with the governed semantic layer you already have, whether that is dbt MetricFlow, Cube, or Looker's LookML: bring your own layer and Sundial reads from it. And if you do not have one, its Modeling Agent transforms your raw tables into clean, analysis-ready pipelines and builds the semantic layer itself, on Sundial's own layer that extends dbt MetricFlow, so it stays open and standards-based rather than a proprietary modeling language.

Its Quality Agent validates the data and recommends what to capture next, so the definitions rest on data you can trust. Everything is git-backed, which means the definitions live in your repo and you own the models, rather than locking them inside a vendor.

This is not a feature Hex lacks so much as a different center of gravity. A notebook's center is the editor: the flexible place a skilled person writes whatever they need. An AI data analyst's center is the governed context: a shared rulebook the agent is bound to, so a non-expert can ask anything and still get a consistent, defensible answer. If your worry is "I don't want analysis scattered across a hundred notebooks with a hundred slightly different definitions," that worry is what the context layer addresses.

What about safety: who can change what?

A notebook gives its user a powerful, open canvas, which is the point and also the thing you have to govern. When you put a notebook in front of a wider audience, you are giving people the ability to write and run code against your data. For a data team that is fine. For opening analysis to the whole company, it is a governance question you have to answer.

An AI data analyst splits the two audiences by design. For business users, the people who only ask questions, it is read-only by default: they can ask anything and never change the data. Data practitioners use the same agent to do more: they model the tables, define the source of truth, and run the data-quality checks, so they build and maintain the context layer the business users rely on.

And every answer shows its work, the steps and the queries, carries a confidence signal so a decision-maker knows a solid answer from a rough estimate, and leaves an audit trail. In analytics, a confident wrong answer is more dangerous than a slow one, because someone acts on it before anyone catches the mistake. Being checkable is the point.

Do dashboards and notebooks go away?

No, and this is worth saying plainly because the category is full of "X is dead" claims. Dashboards are not dying: for a fixed KPI everyone watches, a dashboard is still the right surface. Notebooks are not dying either: for a builder doing deep, custom, exploratory work, a notebook is a fine place to do it. What is changing is the open-ended "why did this move" question, the one that today becomes a ticket and a two-day wait for whoever owns the notebooks.

That question is moving from a queue to an agent. So the real picture for most teams is not "replace your notebook." It is: keep the builder's tools for the builders, and add a way for the askers to get answers without standing in the builder's line. Sundial is an AI data analyst platform built for that asker, with dashboard capabilities in the same place, so the fixed views and the ask-anything investigation live together.

So when is Hex the right call, and when isn't it?

Pick the notebook when the job is building analysis; pick the AI data analyst when the job is getting answers to people who can't build it. An honest split:

Hex is a strong fit when:

• Your users are data practitioners who write SQL and Python and want a faster, more collaborative canvas to do it on.
• The work is open-ended exploration, modeling, or prototyping where you need full control of the code.
• You're packaging analysis into polished, interactive reports or data apps for stakeholders to read.

An AI data analyst is the better fit when:

• The people with the questions cannot write code, and you don't want every question to become a ticket for the data team. At Gamma, every product decision-maker became their own analyst, which let a 28-person team stay lean while serving over 50 million users.
• The questions are ad-hoc "why did this move" investigations. At Character, exactly these questions produced insight in weeks that would otherwise have taken six to twelve months.
• You want consistency and governance across the whole company's analysis, not a fast but ungoverned canvas per analyst.
• Your analysts are spending their days on repetitive pulls instead of hard problems. At OpenAI, root-cause investigations dropped from two to three days to minutes.

Many teams want both, and that is a reasonable answer. The point of this page is to stop you from buying a builder's tool to solve an asker's problem. If your bottleneck is that smart, non-technical people keep waiting in line for answers, a notebook, however good, does not move that line. A read-only AI data analyst on governed definitions does.

Frequently asked questions

Is Hex an AI data analyst? Not in the sense this page means. Hex is a collaborative data notebook for practitioners, and it has added AI features that help write and edit cells (as of 2026). That is text-to-SQL and code-assist inside an authoring tool. An AI data analyst is built for a non-technical asker: it runs the whole multi-step investigation itself and returns an answer on governed definitions, rather than helping a coder write a cell.

Can a business user who doesn't code use Hex? They can read the reports and data apps a builder ships, which is a strength. A notebook-centered workflow may still route deeper or governed investigations through authored assets and context setup. Test whether non-coders can originate the specific diagnostic questions you care about, and whether the result is auditable enough.

For that, their question goes to whoever can author the analysis. An AI data analyst is designed for the non-coder to ask directly.

What is the difference between a notebook's AI feature and an AI data analyst? A notebook's AI turns one sentence into one SQL cell, or edits your code. It answers a query.

An AI data analyst plans and runs many queries, follows an encoded analytics playbook, reads from a semantic layer so the definitions are governed, checks its own work, and returns an answer you can audit. One is code-assist; the other is the analysis itself.

Is the AI data analyst's analysis trustworthy if a non-expert is asking? That is what the design is for. It reads from governed definitions so the metric means the same thing every time, it is read-only by default for business users so they cannot change data, it shows its steps and queries, it signals its confidence, and it leaves an audit trail. A confident wrong answer is worse than a slow one, so being checkable is built in.

Do I have to choose one? No. Keep a notebook for the builders doing deep, custom work, and add an AI data analyst so the people with questions stop waiting in the builders' queue. The deciding question is whose problem you're solving: the person building analysis, or the person who needs an answer and can't build it.

Does an AI data analyst replace data analysts? It reshapes the role, and a team likely needs fewer analysts doing manual pulls. The agent handles much of the repetitive pulling and first-pass investigation, so the job shifts from reactively answering query requests to architecting the context: defining the metrics, the relationships, and the methods so everyone's questions get good answers. Humans stay in the loop on judgment and the highest-stakes calls.

If your bottleneck is that non-technical people keep waiting in line for answers, see what an AI data analyst on governed definitions looks like at Sundial.