Top tools for vibe analytics today

No one’s happy with how BI works today. 

Data practitioners are awash in one-off data pipeline requests and data questions from users, with little time left to work on infrastructure. Data stakeholders, meanwhile, feel like they’re drowning in dashboards, without a clear way to get access to the burning questions they need answered.

What if both teams could break out of this impasse by leveraging AI for BI? Data practitioners could use AI for BI tools to generate the skeleton of a new, robust data product. Meanwhile, data consumers could use the same tools for exploratory data analysis, generating fast answers to urgent questions using natural language queries. 

That, in a nutshell, is the promise and potential of “vibe analytics.”

With vibe analytics, both data practitioners and business stakeholders can work together to generate insights faster, prototype solutions more effectively, and break down the barriers between technical and non-technical users. 

Using vibe analytics effectively requires tools that work for both technical and non-technical users alike. In this guide, we'll explore what vibe analytics is and review the top tools available today that enable easy data exploration and data collaboration. 

What is vibe analytics?

Vibe analytics uses Generative AI (GenAI) to produce unique insights into data at a fraction of the time required by traditional analytics. The term is modeled after "vibe coding," coined by AI pioneer Andrej Karpathy. Developers use Large Language Models (LLMs) to create the skeleton of new applications from natural language descriptions.

With vibe analytics, users feed data directly into an LLM, which outputs the base components of a new data product: SQL queries, Python code, AI data visualizations, and analysis of the data and its meaning. The key difference from traditional analytics is that vibe analytics produces code and outputs that data practitioners can further edit, test, and refine.

How vibe analytics works

Vibe analytics follows a four-stage process:

Input. Users take data in structured, semi-structured, and unstructured formats and supply them to an LLM. This data can include Excel spreadsheets, CSV, JSON, PDF, and more. Users then query this data using natural language questions rather than SQL or Python code.

Query. The LLM analyzes the data sources and determines what types of queries it needs to make on each. It brings this data into a unified format for analysis. This step saves time because users don't have to understand how to extract data from a source or debug code.

Output. The LLM produces the direct output the user requested, such as an AI data visualization (graph, chart, histogram, Sankey diagram, etc.) or a textual summary. Critically, the LLM also produces the intermediate product: the data and code it used to generate the results. This transparency increases trust in the output.

Analysis and refinement. Users analyze the output and can either refine their prompts to improve results or edit the code generated by the LLM directly and build upon it.

Why vibe analytics matters for data teams

Vibe analytics isn't just a tool for either data practitioners or data consumers. It's a new way of working with data that acts as a bridge between the two groups, providing collaboration that traditional analytics workflows can't match:

For data practitioners: Vibe analytics enables them to prototype new data-driven solutions more quickly. They can rely on AI to generate 60 to 80 percent of a solution, preview it with consumers, incorporate feedback, and make it production-ready. This dramatically reduces the time spent on exploratory work and allows practitioners to focus on delivering production-quality solutions.

For data consumers: Vibe analytics provides working examples to engineers instead of verbal requirements. Stakeholders can build a prototype within hours and then collaborate on subsequent versions with the data engineering team. This reduces "lost in translation" errors and gives both teams a common collaboration tool.

The result is faster data exploration, better requirements gathering, and more agile decision-making across the organization.

The right tool for vibe analytics

Choosing the right tool is critical to success with vibe analytics. The market offers several options, ranging from traditional BI solutions adding AI features to purpose-built AI-native platforms. Each category serves different needs and comes with different trade-offs.

Non-AI-native solutions

Some modern analytics platforms take a progressive approach to analytics but haven't fully embraced AI as their core paradigm. These tools offer robust analytics capabilities, layering AI features on top of existing architectures.

Sigma

Sigma is a cloud analytics platform that operates directly on your data warehouse. The platform uses a spreadsheet-like interface that makes it accessible to business users who are comfortable with Excel but need more powerful analytics capabilities.

Sigma's approach simplifies data exploration by allowing users to work with live data directly in a familiar interface. The platform supports SQL generation and collaborative analytics, making it useful for teams that want to democratize data access without requiring technical expertise.

However, Sigma's AI capabilities are limited compared to AI-native solutions. The platform focuses primarily on providing a more accessible interface to data rather than using AI to generate code, visualizations, and insights automatically.

Omni

Omni takes a “semantic layer” approach to analytics. The platform aims to provide a single source of truth for business metrics while making data exploration more accessible to non-technical users.

Omni's strength lies in its ability to define business logic once and reuse it across multiple analyses. This reduces inconsistencies in reporting and makes it easier for business stakeholders to explore data without worrying about getting definitions wrong.

Like Sigma, Omni is adding AI-assisted features but hasn't rebuilt its platform around AI as a core principle. The tool excels at traditional BI workflows but offers less automation than true AI-native platforms.

Traditional BI solutions with AI capabilities

The established players in business intelligence are adding AI features to their platforms. These solutions offer mature ecosystems, extensive integrations, and proven reliability, but they're adapting existing architectures rather than starting fresh with AI-first design.

Tableau

Tableau remains one of the most widely used visualization and analytics platforms. The tool excels at creating interactive dashboards and complex visualizations through a drag-and-drop interface.

Tableau has added AI capabilities through features like natural language queries and automated insights. Users can ask questions in plain English and get visual answers. The platform also offers automated analytics that surface trends and anomalies without manual analysis.

However, Tableau's AI features feel like additions to an existing platform rather than core functionality. The tool still requires significant configuration and setup, and the learning curve remains steep for new users.

Looker

Looker Studio, now part of Google Cloud, takes a code-first approach to BI. The platform uses LookML, a modeling language that defines business logic and relationships between data.

Google has integrated AI capabilities into Looker, including natural language queries and AI-powered data exploration. These features leverage Google's AI infrastructure to provide intelligent suggestions and automated insights.

Looker's strength is its semantic modeling approach, which ensures consistency across analyses. But the platform requires technical expertise to set up and maintain, making it less accessible for business users who want to do ad-hoc data exploration.

Power BI

Microsoft's Power BI offers deep integration with the Microsoft ecosystem and strong data modeling capabilities. The platform has added AI features, including natural language Q&A, automated insights, and integration with Azure AI services.

Power BI's AI capabilities are growing, particularly for users already invested in the Microsoft stack. The platform offers good value for organizations that standardize on Microsoft tools.

However, like other traditional BI tools, Power BI's AI features are enhancements rather than core functionality. The platform still follows traditional BI workflows with AI layered on top.

AI-native BI tools

AI-native BI tools are built from the ground up with AI as their core paradigm. These platforms don't just add AI features to existing workflows. They fundamentally rethink how users interact with data by putting conversation and collaboration at the center of the data exploration experience.

The best AI-native vibe analytics tools share several characteristics:

Show the code. Users can verify results and modify the output to refine results even further. Transparency in how AI generates insights builds trust and enables collaboration.

Enable using AI as much or as little as needed. A data engineer may only want an LLM to create skeleton code they can fill in. A less-technical data consumer may want the AI to generate all or nearly all of the code. This flexibility streamlines collaboration and enables the transformation of prototypes into production applications.

Create a collaborative environment. The platform provides a centralized place for both data practitioners and data consumers to work so data doesn't get lost in siloes.

Track dependencies between code blocks. Tools that just append code to the end of a notebook turn projects into unmaintainable messes. The tool should be aware of any code that came before it and know where to insert or update code without breaking existing work.

Support caching. Rerunning scripts that hit your data warehouse every single time causes compute costs to skyrocket. Data caching is non-negotiable for AI data analysis tools.

Here’s a look at some of the best tools on the market today.

Fabi

Fabi is a vibe analytics platform built AI-native from the ground up. The platform is an all-in-one tool for AI data exploration, enabling teams to generate data applications based on Python and SQL 10 times faster than building them from scratch.

As an AI-native tool, Fabi produces SQL and Python code that users can verify and edit themselves. The platform supports frequent round-tripping between AI assistance and manual coding, providing a unified, collaborative workspace for data producers and data consumers.

Fabi tracks dependencies and inserts new code blocks intelligently, avoiding the mess that comes from simply appending code to notebooks. The platform caches data to keep data retrieval costs to a minimum.

Getting started with Fabi is straightforward. Users can upload their own data and start exploring it immediately. Fabi automatically analyzes the data and generates Python code to load it. Using the AI assistant, users can ask both simple and advanced questions in natural language.

For example, when working with CRM sales data, users can ask Fabi to "Create a leaderboard of sales agents by total won deal revenue and number of deals." Fabi breaks this down into discrete steps and generates Python code for each step. Data consumers can ask further clarifying questions to dig deeper into the data, while data practitioners can edit the Python code directly to refine visualizations or add new outputs.

Julius

Julius is an AI analysis tool that uses Python and AI to help with academic research and data exploration. The platform targets researchers and analysts who need to work with data but may not have extensive programming experience.

Julius excels at data cleaning, statistical modeling, and creating visualizations. The platform automates data preparation, focusing on what matters: gaining insights quickly. Users can export results instantly to CSV or Excel for easy sharing.

The tool offers capabilities for exploratory data analysis and provides expert-level insights without requiring deep technical knowledge. However, Julius is more focused on individual analysis for students than team collaboration.

Zerve

Zerve is an AI-native data analytics platform that uses AI Agents to automate coding, orchestration, and infrastructure tasks. The platform runs within your own environment and includes a distributed computing engine called Fleet that enables parallel code execution using serverless technology. It connects to data sources like Snowflake, BigQuery, and Google Sheets, processing data through SQL for querying, Python for analysis, and AI for natural language processing.

The platform is designed for code-first data teams and builds a structured understanding of your data to support faster retrieval and project development. Zerve's multi-layered pipeline transforms business data into insights through connected data sources, semantic modeling, and automated workflows. The platform aims to reduce the time from prototype to production by providing transparency into how data is processed and analyzed.

isotopes.ai

isotopes.ai offers an AI Agent for Analytics used by Finance, Sales, HR, and other business teams. The platform addresses analytics workflows by combining Data Engineering, Data Wrangling, and BI capabilities into a single AI-powered system. The AI agent operates as a collaborative tool that identifies trends, suggests deeper analysis, and incorporates user feedback into future recommendations.

The platform includes Ambient Analytics, which performs daily analytics and analyzes changes in metrics over time. isotopes.ai also features Continuous Learning and Organizational Memory capabilities that retain knowledge from user interactions across data and processes. Organizations use the platform for ad hoc and repeated analysis tasks, including daily KPI summaries, operational meetings, and monthly reporting cycles.

Wisdom

Wisdom is an AI-native platform that allows users to ask questions in plain English and receive data-driven answers. The platform connects to data sources including PostgreSQL, SQL Server, Snowflake, and Google BigQuery. Wisdom organizes data into business domains like Sales or Marketing and creates relationships and definitions to structure analysis.

The platform generates interactive Stories that combine visualizations and text for sharing insights. These Stories can be delivered via email or embedded in dashboards. 

Wisdom serves two user types: Admins, who configure the platform, connect data sources, and manage access; and Explorers, business users asking questions and working with visualizations. The platform uses feedback from user interactions to refine its responses over time.

Choosing the right vibe analytics tool for AI-powered BI

Done right, vibe analytics can enable a new level of data productivity and data literacy. Data practitioners can leverage it to speed development, while data stakeholders can use it to test hypotheses and generate quick answers to pressing questions. 

The “right” vibe analytics tool will ultimately depend on your current data/BI stack and data workflows. You may end up using multiple tools for different workflows or within different teams. 

The best tools, however, will generally have three key attributes: 

• AI-native - this enables easy round-tripping, top-notch performance, and support for AI-specific capabilities, like snapshotting.‍
• Easy to use - it shouldn’t be so “tech-y” that only engineers can figure it out; rather, it should be intuitive enough that even users without a deep technical background can leverage it.‍
• Collaborative - the tool should foster and encourage collaboration and transparency between data producers and stakeholders.

We built Fabi to deliver in all three areas. To try it for yourself, create a free account, upload some data, and see how Fabi instantly opens new doors for rapid data exploration.