ThoughtSpot vs Power BI vs Tableau: What Their AI Needs First

ThoughtSpot, Power BI and Tableau all take plain-English questions now: Spotter, Copilot and Tableau Agent. What separates them is what you buy and build before the answers are right. Copilot needs Fabric or Premium capacity on top of Pro, Tableau's Pulse agent answers only over Pulse metrics, and ThoughtSpot Pro gives each user 25 Spotter queries a month.

Our take: the chat box is the easy part now. The definitions behind it decide whether the number is right, and all three keep those inside their own product, behind their own license. If you run a warehouse and want every person and every agent to get the same number, keep those definitions in one place your data team owns and reviews. That's what we built Querio for.

At a glance

	ThoughtSpot	Power BI	Tableau	Querio
What answers questions	Spotter (Spotter 3 on new instances)	Copilot (Q&A goes away in February 2027)	Tableau Agent, and Tableau Agent in Pulse	Querio's AI, in Querio, Slack, Claude and the CLI
What it answers over	A ThoughtSpot Model	A Power BI semantic model	Pulse metrics (in Pulse); Tableau Semantics (in Tableau Next)	The metrics and context in your git repo, over your warehouse
What turns it on	Pro, from $50 per user a month billed annually, with 25 Spotter queries each a month; unlimited Spotter is an add-on	Fabric capacity F2 or higher, or Premium P1 or higher; Pro or PPU alone isn't enough	Tableau+ or Cloud+ (contact sales); Tableau Next from $40 per user a month	Every plan, including the free plan. Core is $2,499 a month, with no per-seat pricing
What you build first	A readable Model: clear column names, indexing, synonyms, reviewed AI context	Prep data for AI: AI data schemas, verified answers, AI instructions	Pulse metric definitions	Metrics, joins and business rules as files in your repo; dbt models import
Agent access	Spotter MCP Server, an add-on on Enterprise and Embedded	Power BI MCP server (preview)	Tableau MCP, hosted for Tableau Cloud	MCP, API and CLI

ThoughtSpot: Spotter

Does ThoughtSpot let people ask questions in natural language? Yes. Spotter is ThoughtSpot's AI agent, and Spotter 3 is the default on new instances; admins on older ones can turn it on. You pick a Model, or let Spotter pick one, and type the question. Spotter shows the search tokens it used, and you can edit them. ThoughtSpot says Spotter queries the warehouse live: it turns your question into search tokens, then "compiles them into identical governed SQL on every run".

Spotter Semantics. ThoughtSpot calls the layer behind Spotter Spotter Semantics. It holds your definitions, join logic and security rules, written in ThoughtSpot's modeling language (TML), and you can keep it under version control.

Making a Model Spotter can read. ThoughtSpot's docs say "your data model needs to be readable by Spotter" before you add memory or instructions. That means human-readable column names, indexing on the columns people ask about, and synonyms for your team's own terms. Keeping a Model under 100 columns improves Spotter's column picks. Spotter can draft AI context for each column, but ThoughtSpot says that draft "should be manually reviewed and refined", and regenerating it overwrites your edits.

What it costs. In ThoughtSpot's per-user pricing, Essentials starts at $25, but ThoughtSpot lists Spotter only from Pro. Pro starts at $50 per user a month, billed annually, and each user gets 25 Spotter queries a month. Unlimited Spotter is an add-on. A usage-based option starts at $0.10 per credit, and the pricing page doesn't say what a credit is. The Spotter MCP Server, which lets Claude, ChatGPT or Gemini query ThoughtSpot, is an add-on on Enterprise and Embedded.

What Spotter won't do. "Spotter bases its answers on metadata, and does not directly handle the data it fetches." It doesn't support parameters.

We compare the two products directly in Querio vs ThoughtSpot.

Power BI: Copilot, and the end of Q&A

Power BI's old natural-language feature, Q&A, is on its way out. Microsoft first announced it would go in December 2026. Its Learn page now says "Q&A experiences are going away in February 2027" and points everyone to Copilot. Existing Q&A visuals stop working when it goes.

What turns Copilot on. A paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher). Microsoft says so directly: "A Power BI Pro or Premium Per User (PPU) license alone isn't sufficient." Trial capacities don't count. Pro is $14 and PPU $24 per user a month, paid yearly, so Copilot is a capacity purchase on top of the seats. The Copilot pane inside a report is generally available. The standalone Power BI agent, which searches every report and semantic model you can open, is still in preview.

What you build first. Microsoft calls it Prep data for AI, still in preview: AI data schemas, verified answers and AI instructions on the semantic model. Instructions don't rescue a bad model, either: "additional instructions can't correct inaccurate relationships or inappropriate aggregations". Copilot reads only the first 200 characters of each description.

Checking an answer. Microsoft's Prep data for AI page says: "AI behavior is nondeterministic. Copilot doesn't always produce the exact same response, even with the same input." Copilot features "work best in the English language". Each answer has a "How Copilot arrived at this" panel that shows the data and filters it used, which is how you check it.

Your data team picks up two jobs: a capacity budget, and a semantic model that's prepared and kept right for AI. More in Querio vs Power BI.

Tableau: Pulse, Tableau Agent and Tableau Next

Tableau retired Ask Data in Tableau Cloud in February 2024 and in Tableau Server 2024.2. Plain-English questions now go through three products:

• Tableau Agent in Pulse, the new name for Enhanced Q&A. It "is limited to working with insights from Tableau Pulse metrics" and "can't answer questions about columns that aren't used in the metrics". It's a Tableau+ feature on Tableau Cloud, and it needs a connected Salesforce org with Einstein generative AI.
• Tableau Agent in Desktop, Prep and web authoring, which helps you build views. On Tableau Cloud it needs Tableau+ or Cloud+; on Tableau Server, version 2025.3 or later.
• Tableau Next, from $40 per user a month on an annual contract. Every buyer gets a Salesforce org and a Data 360 instance, and its agent answers from Tableau Semantics, in Tableau and through Slackbot.

Pricing. Tableau Cloud seats run $75 a month per Creator on Standard and $115 on Enterprise, billed annually. Cloud+ and the Tableau+ Bundle, where the agents live, are "Contact Sales".

Who does the math. Tableau says its agent in Pulse "does not ask the LLM to perform math calculations or query data": a deterministic service works out the insight and the LLM explains it. Its help page still warns that "occasional hallucinations (inaccurate or off-topic answers) may occur, especially when you ask questions that create more complex queries."

On October 12, 2026, Salesforce agreed to buy Loops and plans to bring its technology into Tableau. We covered what that means for Tableau's AI agent.

More in Querio vs Tableau.

ThoughtSpot vs Tableau, and ThoughtSpot vs Power BI

Is ThoughtSpot better than Tableau, and which is easier to use? They're built for different first moves. Spotter takes a question over any column in a Model. Tableau's agents answer over metrics someone already set up in Pulse (or over Tableau Semantics in Tableau Next), or they help you build a view. Which feels easier depends on what your people do all day: ask, or build.

How does ThoughtSpot compare with Power BI? Both want a prepared model before the AI answers well: a ThoughtSpot Model, or a Power BI semantic model with Prep data for AI. They charge differently. ThoughtSpot prices Spotter per user, with a usage option. Power BI sells Copilot by the capacity, on top of whatever seats you already pay for.

What decides whether the answer is right

ThoughtSpot's and Microsoft's own docs say it: the answer is only as good as the model behind it. Tableau's agent in Pulse answers only over metrics someone has already defined. Anthropic found the same with Claude. On its own analytics evals, Claude's accuracy didn't exceed 21% without written context, rose above 95% with it, and drifted to about 65% in a month once that context went stale. We go deeper on this in our AI BI tools comparison.

As Rami Abi Habib, Querio's CEO, puts it, the context that matters "lives in people's heads, and it's exactly what makes an answer accurate rather than just confident."

In these three tools, that context is a ThoughtSpot Model, a Power BI semantic model or a set of Pulse metrics, and each one lives inside that vendor's product. Run two of these tools, or let someone point Claude at the warehouse directly, and you're keeping the same definition in two places. Then one of them changes and the other doesn't.

Where Querio fits

Querio is analytics as code for data teams. You write the definitions once (metrics, joins, business rules) as files in a git repo you own. If you use dbt, we import your models. Every change is reviewed, diffed and reversible, like any other code change. Once it merges, Querio, the Slack bot, Claude and any agent over MCP, API or CLI read the same layer. Every answer is real SQL or Python you can open, check and re-run.

You'll write definitions whichever tool you pick. With Querio you write them once, in code your team already reviews, instead of once per BI tool.

"It's great every business user gets the same definition of a metric no matter which tool they're asking from," says Max, a BI engineer at OpenVPN.

There's no per-seat pricing: unlimited users on every plan, from the free plan to Core ($2,499 a month) and Enterprise (custom). Each plan includes monthly AI credits. Dashboards and automations don't use them, and past the credits AI usage is billed per use, with a limit you can set. See pricing.

FAQs

Which is accurate enough for people who don't write SQL?

The one that answers from definitions your data team has written down and keeps current, because someone who can't read SQL can't catch a plausible wrong answer. In Querio those definitions sit in a repo the data team reviews, so a business user's question runs on the governed definition, and an analyst can open the SQL behind any answer and check it.

Can people ask these questions in Slack?

Spotter answers in Slack channels and threads, with ThoughtSpot's permissions and row-level security. Tableau Next's agent answers through Slackbot. Querio's Slack bot comes with every plan and reads the same definitions as Querio, Claude and the CLI, so the number in the thread matches the one everywhere else.

Prices and features are as each vendor listed them on October 17, 2026.