Mode Alternative: When You Need an AI Data Analyst, Not an Analyst Workspace
Mode and an AI data analyst solve two different problems, and the choice comes down to who does the analysis. Mode is a workspace built for the data analyst: SQL, Python and R notebooks, and a charting layer in one place, so a skilled analyst can go from query to report without switching tools. An AI data analyst is for the people who do not write SQL: it takes a business question, investigates it end to end, and hands back a governed answer with its work shown.
Said in one line you can quote: Mode is a workspace where an analyst does the work, and an AI data analyst is a system that does the work for everyone else. As of 2026, Mode is owned by ThoughtSpot, which acquired it in 2023.
Key takeaways
- Mode is an analyst-facing platform: SQL editor plus notebooks (Python and R) plus a visualization and reporting layer. It is built for people who already know how to query and model data. (As of 2026.)
- An AI data analyst is consumer-facing for the business and practitioner-facing for the data team. A non-technical person asks a question in plain language; the agent plans the queries, runs them, checks itself, and returns an answer. It is read-only by default for the people asking.
- The buyer's job decides the tool. If your bottleneck is "our analysts need a faster place to write SQL and build reports," that is Mode's strength. If your bottleneck is "every business question becomes a ticket for the analysts," that is what an AI data analyst is built to fix.
- The two are not mutually exclusive. A team can keep an analyst workspace for deep custom work and add an AI data analyst so the routine "why did this move" questions stop landing in the analyst queue.
What Mode is, fairly
Mode is a cloud analytics platform built for data analysts, combining a SQL editor, Python and R notebooks, and a visualization layer in one workflow. An analyst writes SQL against the warehouse, drops the result into a notebook to do statistics or modeling in Python or R, then builds charts and reports to share. The pitch, fairly stated, is that a skilled analyst stays in one tool from raw query to finished report instead of stitching together a SQL client, a Jupyter notebook, and a separate BI tool. As of 2026, Mode is part of ThoughtSpot, which acquired it in 2023.
That design has a clear strength and a clear assumption. The strength: it is a genuinely good environment for a data professional who lives in SQL and code. The assumption: the person using it can write SQL. Mode is a power-user workspace. It speeds up the analyst. It does not remove the need for one.
So the honest way to frame a Mode evaluation is not "is Mode good." For its intended user, an analyst who codes, it does its job. The real question is whether an analyst workspace is the thing that solves your actual bottleneck, or whether your bottleneck is one layer up: too many questions, not enough analysts to write the SQL.
The buyer's job: who actually needs to get the answer?
Pick the tool by who is stuck, not by feature lists. Two different bottlenecks send teams looking at Mode, and only one of them is a Mode-shaped problem.
The first bottleneck: your analysts have a clunky workflow. They jump between a SQL tool, a notebook, and a BI tool, and a single workspace would make them faster. That is the problem Mode is built for. If that is you, weigh Mode against other analyst workspaces and notebook tools on how well they fit how your analysts actually work.
The second bottleneck: the analysts themselves are the constraint. Every "why did signups drop in this segment" question becomes a ticket, and the queue is days long. Giving the analysts a nicer workspace does not fix this, because the work still has to route through a person who writes SQL. This is the BI backlog problem, and it is what an AI data analyst is built to remove.
The two bottlenecks look similar from a distance and need opposite tools. One asks "how do we make the analyst faster." The other asks "how do we let people get answers without the analyst." Naming which one you have is most of the decision.
Where an AI data analyst fits
An AI data analyst lets a non-technical person get a governed answer without writing SQL or filing a ticket. A product manager asks, in Slack or Teams, "why did activation drop for new iOS users last week." The agent reads the question, maps the words to your governed metric definitions, plans the set of queries the question needs, runs them, checks the result for bad or stale data, and comes back with a diagnosis and a confidence signal, with every step shown so a data person can audit it. The person asking never opens a SQL editor.
This is a different shape of tool from a workspace. A workspace is where a skilled person does the analysis. An AI data analyst does the analysis.
The difference matters most on the open-ended question. A question like "what was revenue last week" is one query, the kind a text-to-SQL feature handles. A question like "why did revenue drop last week" is a dozen queries plus the judgment to know which ones matter. That judgment is what an analytics playbook encodes, an analyst's method written down so the agent runs the same rigorous investigation every time instead of improvising.
It also splits cleanly by audience, which is the part a code-first workspace does not do on its own. Business users get the agent read-only by default: they can ask anything and cannot change the data. Data practitioners use the same agent to model the tables, define the source of truth, and run data-quality checks.
Sundial does this with a system of agents, not one chat box: a Modeling Agent that transforms raw tables into clean pipelines and works with the semantic layer you already have or builds one you own, a Quality Agent that validates the data and flags what to capture next, and an Analysis agent that runs the investigation against that layer. So the people who would never log into Mode get real answers, and the data team gets a tool that builds and maintains the context underneath, rather than building that layer by hand before the analysis can begin.
Side by side
The split is workspace-for-the-analyst versus answer-system-for-everyone. This compares the two by what each is built to do, not by who wins. (As of 2026.)
| Mode | AI data analyst (Sundial) | |
|---|---|---|
| Primary user | Data analysts who write SQL, Python, R | Business users who ask in plain language, plus data practitioners |
| What it produces | The analyst writes queries and notebooks and builds reports | The agent plans, runs, and checks a multi-step investigation, then explains it |
| Open-ended "why did this move" | The analyst does the detective work by hand | The agent runs the investigation via an analytics playbook |
| Self-serve for non-technical people | Available through governed datasets, Visual Explorer, reports, and dashboards; deeper ad hoc investigation still depends on analyst-created structure | Yes: ask in plain language, no SQL |
| Trust model | You trust the analyst who wrote it | Shows its work, a confidence signal, an audit trail, read-only for consumers |
| Governed definitions | Via shared SQL / dbt models the team builds and maintains by hand | Works with the governed semantic layer you already have, or a Modeling Agent builds one you own that is git-backed, the core of a broader context layer |
The row that matters most for the second bottleneck is "self-serve for non-technical people." A workspace can be excellent and still leave that row mostly empty, because its job is to equip the analyst, not to replace the need for one.
Why a governed answer needs a semantic layer
An AI data analyst is only as trustworthy as the context it reads from, which is why it sits on a semantic layer. A semantic layer is the shared rulebook for your data: the official definition of every metric, how your tables relate, and which source is the truth when two systems disagree.
Here is why it matters in plain terms. Today sales and finance often define "active customer" two different ways, so when the CEO asks how many you have she gets two numbers in one meeting. A governed layer makes everyone count it the same way, and the agent reads from that layer, so it measures "active customer" the way your company has agreed to, every time.
In a workspace like Mode, that consistency lives in the SQL and the models the analysts maintain, which works well as long as a careful analyst is the one writing the query. The moment you open analysis to people who do not write SQL, you need the definitions enforced by the system, not by the person, because a clean-looking query can confidently measure the wrong thing and nobody asking the question would catch it.
There is a deeper assumption worth naming here. A workspace, and most tools now adding AI on top of one, assumes the governed layer already exists for the agent to read. Someone still has to build it.
Sundial works two ways here. If you already have a governed semantic layer, whether it is dbt MetricFlow, Cube, or Looker's LookML, Sundial reads from it and works with whatever you have. If you do not, Sundial's Modeling Agent builds that layer from your raw tables, defining the metrics and relationships on Sundial's own semantic layer, which is an extension of dbt MetricFlow, so it stays open and standards-based rather than a proprietary lock-in. Either way the definitions are git-backed models you own in your own repo, not locked inside a vendor, and the agent has a governed source of truth to read from.
A workspace makes a good analyst faster. A governed AI data analyst lets people who are not analysts get an answer the analyst would have trusted.
What you give up, and what you keep
An AI data analyst does not replace a code workspace for genuinely bespoke, code-heavy work, and it is not trying to. If your analysts need to write custom Python for a forecasting model or a one-off statistical study, a notebook environment is the right place for that, and that work stays with a skilled person. The honest boundary: reach for an AI data analyst on the recurring, open-ended questions that today become a ticket, and keep a human in the loop for the highest-stakes, never-changes-twice analysis.
You do not have to choose one and discard the other. A common setup is to keep an analyst workspace for deep custom work and add an AI data analyst so the routine investigation stops flowing through the analyst queue. That also changes the analyst's job for the better. The agent absorbs much of the repetitive pulls and first-pass investigation, so the role shifts from reactively answering query requests toward architecting the context: defining the metrics, the relationships, the playbooks, and the source of truth, so everyone's questions get a good answer. A team that runs this well likely needs fewer analysts doing manual pulls, and the ones who stay work on harder problems.
And dashboards do not disappear either. For a fixed KPI everyone watches, a dashboard is still the right surface. The shift is only in where the open "why" questions go: from a queue to an agent that does the legwork and shows you how it got there.
When Mode is the better fit, and when it is not
If your users are analysts who write code, a workspace is the right shape. If your users are everyone else, an AI data analyst is.
- Reach for an analyst workspace when the people doing analysis already write SQL and code, when you need notebooks for custom statistics or modeling, and when the bottleneck is the analyst's workflow, not the analyst's queue.
- Reach for an AI data analyst when non-technical people need answers without learning SQL or waiting in line, when the same open-ended questions ("why did this metric move," "is growth healthy," "did the experiment work") come up again and again, and when you need every answer to be governed, checkable, and read-only for the people asking.
- Run both when you want analysts in a deep workspace for bespoke work and self-serve agentic analytics for the routine questions, so the queue shrinks and the analysts work on the hard problems.
Frequently asked questions
What is Mode used for? Mode is a cloud analytics platform for data analysts that combines a SQL editor, Python and R notebooks, and a visualization and reporting layer in one workflow, so an analyst can go from query to shared report without switching tools. As of 2026, it is owned by ThoughtSpot, which acquired it in 2023.
Is an AI data analyst a replacement for Mode? Not a like-for-like replacement, because they target different users. Mode equips analysts who write code. An AI data analyst lets non-technical people get governed answers without writing SQL, and gives the data team a tool to model the data and run quality checks. Many teams keep a workspace for deep custom work and add an AI data analyst to clear the routine question queue.
Can business users self-serve in Mode? Mostly through reports an analyst already built, or by writing SQL themselves. The self-serve path for someone who does not code is limited. An AI data analyst is built for exactly that person: ask in plain language, get an answer, change nothing, because it is read-only by default for the people asking.
How is an AI data analyst different from the notebook plus SQL workflow? A notebook plus SQL workflow is a place where a skilled person does the analysis step by step. An AI data analyst does the multi-step investigation itself, following an encoded analytics playbook, then shows its work and how confident it is. One equips the analyst; the other does the analyst's first-pass work for everyone else.
Does an AI data analyst keep numbers consistent the way our SQL models do? It reads from a governed semantic layer, the official definition of each metric and how your tables relate, so the answer uses your agreed definitions rather than whatever a query happened to compute. The difference with Sundial is that you do not have to build that layer first: it works with the governed layer you already have, or a Modeling Agent builds one from your raw tables as git-backed models you own, where most tools assume the layer is already there. That is what makes it safe to open analysis to people who are not writing the SQL themselves.
Can an AI data analyst be wrong? Yes, if it has no governed context and no way to check its work, it can return a clean-looking number that measures the wrong thing. The guardrails are a semantic layer for the right definitions, read-only access for business users, visible queries and a confidence signal so you can judge the answer, and a human signing off on the high-stakes calls.
If you are evaluating Mode because the questions outnumber the analysts, that is the gap an AI data analyst closes. See what that looks like at Sundial.