Obviously AI
4.3No-code machine learning in minutes
The best Data and Analytics AI tools right now are Vanna, ThoughtSpot, and Tableau Pulse. Vanna is our top overall pick (4.4/5), and it includes a free plan. Compare all 8 below by price, features and rating to find the right fit.
★ Top pickVannaOur highest-rated pick, known for RAG-based text-to-SQL trained on your own schemaVisit Vanna
| Tool | Best for | Free | From | Rating | Visit |
|---|---|---|---|---|---|
 Vanna | Best overall | Yes | Free | 4.4 | Visit |
 ThoughtSpot | Most popular | No | $25/mo | 4.4 | Visit |
 Tableau Pulse | Also worth a look | No | $15/mo | 4.4 | Visit |
 BlazeSQL | Best free option | Yes | $39/mo | 4.3 | Visit |
 Pecan AI | Also worth a look | No | $760/mo | 4.3 | Visit |
| Obviously AI | Also worth a look | No | $75/mo | 4.3 | Visit |
 AnswerRocket | Best value | No | — | 4.2 | Visit |
 Graphite Note | Also worth a look | No | $95/mo | 4.2 | Visit |
Best Data and Analytics AI tool for each use case
Querying a database in English
Vanna lets analysts and non-technical staff ask questions in plain language and returns runnable SQL, using a retrieval framework trained on your schema and example queries. It works with many databases and large language models, including local models, which suits teams that want control over where queries run.
Conversational enterprise BI
Databricks Genie gives business users a chat interface to query governed data in Unity Catalog and returns text summaries, tables, and visualizations. Because it draws on technical and business metadata, it can answer questions in an organization's own terminology while keeping data governed.
Analyzing a spreadsheet by chat
Julius AI lets you upload CSV and Excel files, then clean data, run statistics, and generate charts through natural-language prompts. It previews your dataset and suggests questions, which helps people who are comfortable with spreadsheets but do not code.
No-code prediction and forecasting
Pecan AI guides business teams through building validated predictive models for outcomes like customer churn and demand, without coding. Its conversational co-pilot prepares data and builds the model, then shows the drivers behind each prediction for explainability.
How to choose a Data and Analytics AI tool
What to evaluate
- Match the tool to your team's skills — because SQL assistants, spreadsheet chat, and enterprise BI each assume a different starting point.
- Check data source connectors — since the product must reach your warehouses, databases, or spreadsheets to be useful.
- Weigh governance and trust features — because shared metrics and warehouse access need permissions, auditing, and query review.
- Decide whether you need description or prediction — since conversational BI explains what happened while predictive tools estimate what will.
Which one should you pick?
If you want generated SQL you can run against your own databasePick Vanna, an open-source retrieval framework that turns natural-language questions into SQL and can run with local models.
If you already run on a governed enterprise warehouse and want chat answersPick Databricks Genie, which lets business users query Unity Catalog data and get summaries, tables, and charts.
If you need to forecast outcomes without writing codePick Obviously AI, which builds AutoML predictive models from your historical data and explains feature importance.
Best free Data and Analytics AI tools
These Data and Analytics tools offer a genuine free plan or trial, a smart place to start before you pay.
How much do Data and Analytics AI tools cost?
| Price tier | What you get | Examples |
|---|---|---|
| Free | $0, free plan or open-source | Vanna, Vizly, Powerdrill, Hex |
| Budget | Under $15/mo | Power BI Copilot, Numerous.ai |
| Mid-range | $15 to $39/mo | BlazeSQL, ThoughtSpot, Tableau Pulse, Formula Bot, Sourcetable |
| Premium | $40/mo and up | Graphite Note, Pecan AI, Obviously AI, Akkio |
Pro tips
- Train natural-language-to-SQL tools like Vanna and BlazeSQL on your schema, documentation, and sample queries before trusting their output, since context drives accuracy.
- Define your key metrics once in a shared layer, as Tableau Pulse does, so every team reports from the same definitions.
- Always open and read the SQL a tool generates before running it on production data, even when the natural-language answer looks correct.
- For predictive work in Pecan AI or Obviously AI, review feature importance to confirm the model relies on sensible drivers rather than data leakage.
How we test & rank
Our editors hand-test the tools in this category and score them on value, feature depth, popularity and real user ratings. Rankings are never for sale, and affiliate links never change a score. Read our full methodology
Browse all tools
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DataRobot
4.2Enterprise platform for AI and ML
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Tableau Pulse
4.4Proactive AI insights for your metrics
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Polymer
4.3Turn spreadsheets into AI dashboards
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Power BI Copilot
4.4Generative AI inside Power BI
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Qlik
4.3Analytics with AI-powered Insight Advisor
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Domo
4.2Cloud BI platform with AI agents
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Databricks Genie
4.4Conversational analytics on your lakehouse
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Powerdrill
4.1AI analysis for your datasets
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Hex
4.4Collaborative notebooks for data teams
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Julius AI
4.3Chat-based data analysis assistant
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Akkio
4.2No-code predictive modeling for teams
About Data and Analytics AI tools
This category covers software that applies AI to the work of understanding data. It spans natural-language-to-SQL assistants that translate plain English into queries, conversational business intelligence that answers questions over governed warehouses, spreadsheet AI that cleans and charts data inside a familiar grid, and predictive analytics that builds forecasting models with little or no code. Data analysts, product managers, marketers, and operations teams all use these products. Vanna generates SQL through a retrieval framework, Hex blends SQL and Python in one notebook, and ThoughtSpot answers business questions across enterprise data.
Use these criteria to compare options before you commit:
- Data source coverage, because a tool is only useful if it connects to the warehouses, databases, and spreadsheets where your data already lives.
- Accuracy and trust, because a generated query or prediction is worthless if you cannot verify how it was produced.
- Skill fit, since some products assume SQL or Python fluency while others target people who have neither.
- Governance and security, because shared metrics and warehouse access need permissions, auditing, and compliance controls.
Data and Analytics AI tools — FAQ
What are AI data analytics tools?
They are applications that use AI and large language models to help people explore, analyze, and act on data. Some convert plain-English questions into SQL or charts, others build predictive models, and several layer conversational answers on top of existing business intelligence platforms. The goal is to make analysis accessible to people beyond a dedicated data team.
Do I need to know SQL to use these tools?
For many of them, no. Tools like Vanna and BlazeSQL generate the SQL for you from natural-language questions, and spreadsheet products like Sourcetable and Julius AI let you analyze data through chat. That said, products such as Hex are built for analysts who want to work directly in SQL and Python, so the right fit depends on your skills.
Are there free options in this category?
Yes. Vanna is open source and can run locally, and several products including Sourcetable, Julius AI, and Formula Bot offer a free plan with usage limits. Enterprise BI platforms such as Databricks Genie, Tableau Pulse, and Power BI Copilot are typically sold as features within their broader paid suites, so pricing depends on the underlying platform license.
How accurate and trustworthy are the results?
Accuracy varies with the tool and the quality of your data and metadata. Several products address this directly: BlazeSQL supports knowledge notes and query review workflows, Pecan AI validates its models and shows the drivers behind predictions, and Tableau Pulse explains insights in natural language with supporting visuals. Always review generated queries and model outputs before relying on them for decisions.