Conversational analytics in BI tools: how ThoughtSpot, Power BI, Tableau, Looker, and Qlik compare in 2026

Conversational analytics is a BI feature that lets people ask questions about company data in plain language, follow up in the same thread, and get back numbers and charts instead of a list of reports. ThoughtSpot Spotter, Looker Conversational Analytics, and Qlik Answers ground every answer in a governed semantic model. Power BI Copilot and Tableau Agent sit on top of existing Power BI semantic models and Tableau Pulse metrics, and both need a premium license or capacity. Sigma, Databricks Genie, and Basedash query your warehouse live, with different trade-offs on setup, row-level security, and price.

Prices and plan names below were checked on each vendor’s official page in September 2026.

What counts as conversational analytics in a BI tool?

For this comparison, a tool qualifies if it answers questions against live or modeled data (not only report titles), keeps context across follow-up questions, returns a chart or table you can inspect, and applies the asking user’s permissions to the query it runs.

The biggest design difference is what the language model is asked to produce. Some tools let the model write SQL directly. Others have the model pick fields from a semantic model and let a deterministic engine compose the query. That choice drives accuracy, setup effort, and how easily you can audit an answer. For more on the SQL step, see how AI BI tools translate natural language to SQL.

How we evaluated the tools

The criteria come from questions buyers ask about this category, such as “how does ThoughtSpot’s conversational analytics compare to Tableau, Power BI, Looker, and Qlik?” and “how mature are browser-based BI tools with chat-style AI assistants for exploring a Postgres warehouse while enforcing row-level security?”

• Grounding: a semantic layer, curated metrics, a single app, or the raw schema.
• Auditability: whether users can see the query or logic behind an answer.
• Permissions: whether row-level security applies to AI-generated queries.
• Surfaces: Slack, Microsoft Teams, embedding, API, or MCP server.
• What you have to buy: the license, edition, or capacity required, and how AI usage is billed.
• Setup effort: how much modeling is needed before answers are reliable.

Conversational analytics comparison table

How does ThoughtSpot Spotter handle conversational analytics?

Best for: enterprises that want the most mature governed chat experience and are willing to model data in ThoughtSpot first.

According to ThoughtSpot’s Spotter page, Spotter does not generate SQL directly from text. It translates the question into search tokens grounded in the semantic layer, then compiles those tokens to SQL. The Spotter Semantics documentation lists row and column security, join logic, and codified metrics as rules enforced on every answer. Spotter can also post results to Slack, open Jira tickets, or update Salesforce records. For a direct comparison with a lighter-weight option, see Basedash vs ThoughtSpot.

Fact card: ThoughtSpot

• Pricing (as of September 2026): Essentials $25/user/month billed annually (5 to 50 users, no Spotter listed); Pro $50/user/month with 25 Spotter queries per user per month; Enterprise custom. A usage-based Pro option starts at $0.10 per credit with unlimited LLM tokens (pricing).
• Deployment: ThoughtSpot Cloud.
• Data: live connections to Snowflake, Databricks, Redshift, and other warehouses.
• Security: row-level security; SSO over SAML, OAuth, and OIDC; SCIM 2.0 provisioning.
• AI features: conversational agent, dashboard and data modeling agents, automated change analysis, KPI monitoring and anomaly alerts, choice of LLM.
• Embedding: ThoughtSpot Embedded with API and SDK; MCP server is an add-on on some tiers.
• Not ideal for: teams that want to skip semantic modeling, or small teams that will hit the 25-queries-per-user cap on per-seat Pro.

How does Power BI Copilot answer questions about data?

Best for: Microsoft shops that already run Power BI on Fabric capacity and have well-prepared semantic models.

Copilot offers a pane on each report, a standalone Copilot that can search any report or semantic model you can access (preview), and app-scoped Copilot that can surface verified answers written by app authors (preview). Microsoft’s Copilot overview states two limits plainly: questions not about semantic model data are answered from the language model’s general knowledge, and without model preparation Copilot “can misinterpret the data and return generic or inaccurate results.”

A Pro or Premium Per User license alone does not unlock Copilot. It requires a paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher), and Copilot usage is billed as capacity units.

Fact card: Power BI

• Pricing (as of September 2026): Pro $14/user/month and Premium Per User $24/user/month, paid yearly (pricing); Copilot also needs F2+ Fabric capacity or P1+ Premium capacity.
• Free tier: free Fabric account for personal authoring; trial capacities do not support Copilot.
• Deployment: cloud (Power BI service); sovereign clouds are not supported for Copilot.
• Data: Import, DirectQuery, and Direct Lake semantic models over most warehouses and databases.
• Security: Microsoft Entra ID sign-in; Power BI row-level security roles; users provisioned through Entra rather than a separate SCIM setup.
• AI features: chat with reports and models, report page generation, DAX query writing, measure descriptions, subscription summaries.
• Embedding: Copilot is available in Power BI Embedded.
• Not ideal for: teams without Fabric capacity, or teams whose models are not documented well enough for Copilot to map business terms.

How does Tableau handle conversational analytics?

Best for: Tableau Cloud customers who already define key metrics in Tableau Pulse and want conversational exploration of those metrics.

Tableau Agent in Pulse, formerly Enhanced Q&A or Discover, is described in Tableau’s help documentation as a conversational way to explore groups of Pulse metrics. Users chat about metrics that Creators have defined, not arbitrary tables. Basic Ask Q&A on a single metric works without a premium edition.

Tableau Agent in Pulse requires a Tableau+ edition (listed as Tableau Cloud+ on the pricing page) and a connected Salesforce org with Einstein generative AI. Existing Cloud sites can run a 60-day Try AI trial. Tableau Next, built on Salesforce’s Agentforce platform, bundles Tableau Agent, Tableau Semantics, and native Slack integration.

Fact card: Tableau

• Pricing (as of September 2026): Tableau Standard from $15/user/month and Enterprise from $35/user/month, billed annually; Tableau Cloud+ and the Tableau+ bundle are quote only; Tableau Next from $40/user/month (pricing).
• Free tier: Tableau Desktop Free Edition for local analysis.
• Deployment: Tableau Cloud and Tableau Server; Tableau Agent in Pulse is a Tableau Cloud feature.
• Data: live connections and extracts across most databases and warehouses.
• Security: user filters and data policies for row-level security; SAML and OIDC SSO; SCIM provisioning.
• AI features: Tableau Agent in Pulse, Pulse insight summaries, Tableau Agent in web authoring for calculations and visualizations.
• Embedding: Embedding API for dashboards and views.
• Not ideal for: open-ended questions about data that has no Pulse metric, or teams that do not want a Salesforce org in their Tableau setup.

How does Looker Conversational Analytics work?

Best for: teams that already maintain LookML and want chat answers that follow the same governed definitions as their dashboards.

Google’s documentation explains that Gemini does not write whole SQL queries here. It picks fields, filters, sorts, and limits from a LookML Explore, and Looker composes the SQL using the model’s join logic, access grants, and user attributes. Data agents can query up to five Explores, carry custom instructions and glossaries, and use verified (“golden”) queries. An optional Advanced Analytics mode runs Python on results.

Documented limits: answers return at most 50,000 rows, charts are limited to supported Vega-Lite types, and the feature does not yet answer forecasting, correlation, or anomaly detection questions.

Fact card: Looker

• Pricing (as of September 2026): Standard (under 50 users), Enterprise, and Embed platform editions are all quote only. Each edition includes a monthly Conversational Analytics data token pool (Standard: 60M input and 1.2M output tokens). Google currently offers promotional access without quota limits and will give 90 days’ notice before overage billing at $3 per 1M input and $20 per 1M output tokens (pricing).
• Deployment: Looker (Google Cloud core) and Looker (original).
• Data: BigQuery, AlloyDB, Redshift, Snowflake, Databricks, and other SQL databases through LookML; live query.
• Security: LookML access grants and user attributes; SAML and OIDC SSO; no native SCIM.
• AI features: Explore conversations, shareable data agents, dashboard agents (preview), Python Advanced Analytics, agentic threshold alerts (preview).
• Embedding: iframe embedding of Conversational Analytics and a dedicated API.
• Not ideal for: teams without LookML developers, or users who need anomaly detection or forecasting answers from chat.

How does Qlik Answers work on structured data?

Best for: Qlik Cloud customers who want one assistant to answer from both Qlik apps and internal documents.

Qlik Answers is an agentic chat that combines structured data from Qlik apps with unstructured knowledge bases built from PDF, DOCX, HTML, and TXT files. For data questions, a semantic search agent picks the app’s master measures and dimensions, and a data analyst agent builds the calculation and chart. A reasoning trace shows every field the agent considered and its similarity score, which makes wrong answers easier to diagnose. Each assistant can use one Qlik app, and apps are re-indexed on every reload. Section Access rules apply to answers.

Fact card: Qlik

• Pricing (as of September 2026): Starter $300/month for 10 users with Answers agents and MCP server access; Standard $825/month for 25 GB with no charge for additional users; Premium $2,750/month for 50 GB; Enterprise custom. All billed annually on capacity (pricing).
• Deployment: Qlik Answers runs in Qlik Cloud only.
• Data: data loaded into Qlik apps from hundreds of connectors; answers reflect the latest app reload.
• Security: Section Access and space permissions; bring-your-own identity provider; SCIM 2.0 with Microsoft Entra ID.
• AI features: structured and document Q&A, chart and sheet authoring agents, predictive analytics on Premium.
• Embedding: Qlik’s embedding APIs cover apps and charts; Answers assistants are configured in Qlik Cloud.
• Not ideal for: questions that span several Qlik apps at once, or teams that want live queries against a warehouse without loading data into Qlik.

How does Sigma Assistant answer questions?

Best for: warehouse-first teams already using Sigma who want chat answers that open as editable workbook analysis.

Sigma Assistant answers questions from data models, tables, datasets, and Snowflake semantic views. It picks the best configured source for each question, and answers can become charts that users keep exploring in a workbook. In workbook drafts, Assistant can also build or edit dashboards from prompts (beta). Sigma requires admins to configure an AI provider, either a model hosted in your data platform or an external provider, so model costs sit with you. Sigma can also call Snowflake Cortex Agents and Databricks Genie Agents from Assistant.

On BI Bench, a benchmark Basedash publishes using each tool’s default setup on a complex schema, Sigma scored 35.2% accuracy. Read that result knowing Basedash runs the benchmark.

Fact card: Sigma

• Pricing (as of September 2026): quote only; the pricing page routes to sales. Free trial available.
• Deployment: cloud (SaaS on top of your warehouse).
• Data: live queries on Snowflake, Databricks, BigQuery, Redshift, and other cloud warehouses.
• Security: the user’s existing data access applies to Assistant; SAML SSO; SCIM provisioning.
• AI features: Assistant, AI dashboard building (beta), formula assistant, chart explanations, Sigma agents.
• Embedding: Assistant in the workbook works in secure embeds with :show_assistant=true.
• Not ideal for: teams that do not want to manage their own AI provider, or buyers who need published list prices.

How does Databricks Genie work?

Best for: companies whose analytics data already lives in Databricks Unity Catalog.

Databricks groups its chat features under Genie. Genie One is the business-user interface; Genie Agents (formerly Genie Spaces) are curated environments that analysts configure. According to the Genie Agents concepts page, an agent can include up to 30 Unity Catalog tables or views plus example SQL, instructions, metrics, and join definitions. Genie generates read-only SQL that runs on a pro or serverless SQL warehouse, and each user’s Unity Catalog permissions apply to the results. Genie is available in Microsoft Teams and Slack, as an iframe embed, and through the Genie API.

Fact card: Databricks Genie

• Pricing (as of September 2026): Genie One and Genie Agents usage by users is free until January 31, 2027; service principal usage is billed, and SQL warehouse compute is billed separately (cost docs).
• Deployment: inside your Databricks workspace (AWS, Azure, Google Cloud).
• Data: Unity Catalog tables, views, metric views, and foreign tables only.
• Security: Unity Catalog permissions, row filters, and column masks; SSO and SCIM at the Databricks account level.
• AI features: multi-turn Q&A with visualizations, verified answers, Genie Code for developers, mobile apps.
• Embedding: iframe embed for signed-in Databricks users; Conversation API for custom chatbots.
• Not ideal for: data that lives outside Databricks, or customer-facing embedding where end users do not have Databricks identities.

How does Basedash handle conversational analytics?

Best for: small and mid-size teams that want to ask questions of a live production database or warehouse without a semantic modeling project first.

The Basedash AI data analyst connects directly to PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, Redshift, and other databases, reads the live schema, and generates SQL that it validates and retries before running. Users can view and edit the SQL, turn answers into charts, and save them to dashboards. Basedash Models add reusable measures, segments, and dimensions that the AI uses automatically. The Slack app answers in threads with a chart image, and the MCP server exposes the same data to Claude, ChatGPT, and other MCP clients.

On BI Bench, Basedash scored 92.1% accuracy at a 28.6-second average response time, the highest of 11 agents tested. Basedash runs that benchmark, and ThoughtSpot, Power BI, Tableau, Looker, Qlik, and Genie are not in it.

Fact card: Basedash

• Pricing (as of September 2026): Startup $1,000/month plus AI usage for up to 25 users, with $1,000/month in AI credits included; Enterprise custom (pricing). 14-day free trial, no card required.
• Deployment: cloud; self-hosting on Enterprise.
• Data: live queries on connected databases and warehouses, plus 750+ SaaS sources synced into the managed Basedash Warehouse.
• Security: row-level security through PostgreSQL policies keyed on the basedash.groups session variable, covering chat, dashboards, automations, and Slack; other databases rely on their own access controls plus Basedash data source permissions. SAML and OIDC SSO, SCIM, and audit logs are on Enterprise.
• AI features: chat analysis, AI-generated dashboards, daily anomaly detection with Insights, scheduled Automations to Slack or email.
• Embedding: white-labeled embedding with AI analysis on Enterprise.
• Not ideal for: teams that need per-user row filtering on Snowflake or BigQuery inside the BI layer, teams that need SSO on a starter budget, or organizations standardized on LookML-style governance.

Which conversational analytics tool should you choose?

You already have a governed semantic layer. Looker if it is LookML, ThoughtSpot if you are willing to build Spotter Semantics. Both constrain the language model to picking fields rather than writing free-form SQL, which is the most reliable pattern when definitions are mature. For more on this trade-off, see the BI tools with built-in semantic layers comparison.

You are a Microsoft or Salesforce shop. Power BI Copilot if you already pay for Fabric capacity; budget for capacity, not only seats. Tableau Agent in Pulse if your team already runs on Pulse metrics and can take on the Tableau+ edition and a Salesforce org.

Your data lives in one warehouse platform. Databricks Genie is the lowest-cost starting point for Unity Catalog data while user usage is free through January 2027. Sigma fits Snowflake-heavy teams that want answers to land in a spreadsheet-style workbook.

You want answers from documents and data together. Qlik Answers is the only tool here that combines app data and document knowledge bases in one assistant.

You have a small team, a Postgres or MySQL database, and no data modeler. Basedash connects to the live database and starts answering without a modeling project, and PostgreSQL row-level security applies to every AI query. ThoughtSpot, Looker, and Power BI will give stronger governance, but each needs modeling work first.

You need chat inside your own product. Looker (signed embedding), Power BI Embedded, Sigma secure embeds, ThoughtSpot Embedded, and Basedash Enterprise embedding all support it. Genie’s iframe requires users to sign in to Databricks, which rules it out for most customer-facing apps.

Questions to ask a vendor before buying a conversational analytics tool

Run the trial on your own data and check these points:

1. Out-of-model questions. Power BI documents that Copilot falls back to general knowledge. Ask what each assistant does, and whether it tells the user.
2. Verifiability. Can a non-analyst see search tokens, a reasoning trace, or the SQL?
3. Permissions on every path. Test AI answers, exports, Slack replies, and embedded sessions with two users who should see different rows.
4. Billing unit. Query caps, capacity units, data tokens, your own model provider, or credits produce very different bills at scale.
5. Ambiguous terms. Ask “how many active customers do we have?” and see whether it asks what “active” means.

For accuracy failure modes, see where hallucinations happen in AI BI tools.

FAQ

How does ThoughtSpot’s conversational analytics compare to Tableau, Power BI, Looker, and Qlik in 2026?

ThoughtSpot Spotter translates questions into search tokens grounded in its semantic model, so users see the logic behind each answer. Looker takes a similar approach, with Gemini choosing LookML fields and Looker writing the SQL. Qlik Answers works on one Qlik app per assistant and shows a reasoning trace. Power BI Copilot answers from semantic models but needs Fabric or Premium capacity. Tableau Agent in Pulse is limited to Pulse metrics and needs a Tableau+ edition. ThoughtSpot has the most complete standalone chat product; Looker is the strongest choice if you already maintain LookML.

Which BI tools offer chat-style AI assistants that enforce row-level security?

All eight tools in this comparison apply some form of user-level data permissions to AI answers. ThoughtSpot enforces row and column rules in its query engine, Looker applies access grants and user attributes, Qlik applies Section Access, Databricks Genie applies each user’s Unity Catalog permissions, and Sigma applies the user’s existing data access. Basedash enforces PostgreSQL row-level security policies on every AI query through a basedash.groups session variable, but that mechanism is Postgres-only. Test with two users who should see different rows before trusting any vendor’s claim.

Does Power BI Copilot require a Premium or Fabric license?

Yes. Microsoft’s documentation states that a Power BI Pro or Premium Per User license alone is not enough for Copilot. Copilot requires a paid Fabric capacity at F2 or higher, or Power BI Premium capacity at P1 or higher, and trial capacities are not supported. Copilot usage is billed as capacity units against that capacity, in addition to Pro ($14) or Premium Per User ($24) seats for report authors, as of September 2026. The standalone Copilot and app-scoped Copilot experiences are still in preview.

Can I ask questions of my data in plain English without writing SQL?

Yes, every tool in this comparison supports plain-English questions, but they differ on how much setup comes first. Looker, ThoughtSpot, Qlik, and Power BI give reliable answers only after a data team models the data. Databricks Genie needs an analyst to curate a Genie Agent with tables and example queries. Basedash and Sigma can answer against connected sources sooner, with accuracy improving as you add models, descriptions, and verified definitions. For a no-SQL team without a data engineer, setup effort matters as much as the chat interface itself.

Which conversational analytics tools can post answers in Slack or Microsoft Teams?

Databricks Genie has native apps for both Slack and Microsoft Teams. Basedash has an official Slack app that replies in threads with a chart image and applies row-level security based on who is asking. ThoughtSpot Spotter can post insights to Slack as an action, and Tableau Next includes native Slack integration. Power BI Copilot runs inside the Power BI service, apps, mobile, and Power BI Embedded. Looker can publish data agents to Gemini Enterprise.

How much does conversational analytics cost in a BI tool?

Costs follow four different models as of September 2026. Per-seat with query caps: ThoughtSpot Pro is $50 per user per month with 25 Spotter queries each. Capacity: Power BI needs F2+ Fabric capacity, and Qlik Starter is $300 per month for 10 users. Token pools: Looker includes monthly data tokens with overage pricing announced. Flat team pricing: Basedash Startup is $1,000 per month plus AI usage for up to 25 users. Databricks Genie user usage is free until January 31, 2027, but SQL warehouse compute is billed. Compare our usage-based vs per-seat BI pricing guide before modeling a rollout.