Top Metabase alternatives for modern data teams

Metabase has earned its place as a popular open-source BI tool. It's approachable, handles SQL querying well, and makes it easy to build dashboards that teams can embed or share. For many organizations, especially those just getting started with data visualization, it's been a solid choice.

But as data teams grow and requirements evolve (and AI becomes non-negotiable), Metabase's limitations start to surface. Dashboards sit unused, stakeholders still request manual data pulls, and teams spend more time maintaining reports than generating insights. The reality is that modern data work requires more than visualization—it needs automation, AI assistance, flexible analysis environments, and ways to push insights directly into workflows.

If you're looking for a Metabase alternative that addresses these gaps, here's what's worth considering in 2025.

What to look for beyond basic dashboards

Before diving into specific tools, it's worth understanding what separates modern BI platforms from traditional dashboard builders:

AI and natural language querying - Not everyone on your team writes SQL, and even those who do want to move faster. AI-native tools help both technical and non-technical users get answers without waiting on data teams.

Code flexibility - When dashboards aren't enough, you need the ability to write SQL, Python, or both. A Python dashboard environment lets analysts build custom logic, run statistical analysis, and create more sophisticated visualizations.

Automated insight delivery - The best insight is worthless if nobody sees it. Modern tools push findings to Slack, email, or directly into Google Sheets rather than expecting people to check dashboards.

Diverse data connectivity - Your data doesn't live in just one place. Connecting warehouses, spreadsheets, SaaS tools, and databases without custom pipelines saves enormous time.

Collaboration and governance - As teams scale, you need versioning, clear ownership, role-based access, and ways to track how analyses connect to each other.

The best Metabase alternatives

1. Fabi.ai - AI-first analytics with workflow automation and embedded AI data analyst

Fabi takes a different approach than traditional BI tools. Rather than centering everything around dashboards, it's built for teams that want to combine AI assistance, code flexibility, and automated insight delivery.

Key highlights:

✅ Pros:

• Natural language queries with AI-generated SQL/Python code you can inspect and edit
• Automated workflows push insights to Slack, email, or Google Sheets
• Connects to warehouses (Snowflake, BigQuery) plus apps and spreadsheets (Sheets, Airtable)
• Python dashboard support for custom analysis and visualizations
• AI analyst Agent provides scoped, domain-specific AI assistance

❌ Cons:

• Not open source (proprietary platform)
• No embedded analytics support

What makes it stand out:

We’ve developed a next-generation Jupyter notebook called "Smartbooks"—environments where you can ask questions in natural language, get AI-generated SQL or Python code, then inspect and modify that code yourself. This hybrid approach works well for teams with mixed skill levels. Analysts can dig deep into Python dashboards and custom logic, while less technical users can rely on AI assistance.

Fabi was also the first to introduce an AI Analyst Agent, which can help with everything from code generation to ideation and can be bounded by tight constraints set by the data team.

Where Fabi really differentiates itself is in pushing insights out rather than waiting for people to pull them. You can build workflows that send custom reports to Slack channels, email stakeholders when metrics hit thresholds, or write results directly back to Google Sheets. This addresses the "dashboard graveyard" problem where beautifully built visualizations go unused.

Data connectivity is broad—we handle standard warehouses (Snowflake, BigQuery, PostgreSQL) but also connect to Google Sheets, Airtable, and various apps. For organizations with data scattered across multiple sources, this reduces the need for custom integration work.

2. Apache Superset / Preset

For engineering-heavy teams that want full control, Superset remains a strong open-source option. The SQL editor is powerful, visualization options are extensive, and you can self-host or use the managed Preset service.

It's particularly good if you have the infrastructure resources to maintain it and prefer open-source solutions. However, it lacks built-in AI assistance and natural language querying. Automated insight workflows typically require external tools or custom scripting.

Best for: Organizations with strong engineering capacity that prioritize flexibility and open-source control.

3. Mode Analytics

Mode combines SQL with some R and Python support, making it excellent for data science workflows. The collaboration features are strong, and it's built for experimentation and sharing analyses.

However, it's more oriented toward analyst and data science audiences than business stakeholders. Automated insight delivery to external tools often requires additional setup and custom coding.

Best for: Data science-heavy teams that need robust analysis environments and collaboration where SQL is the primary language.

4. Power BI

If you're in the Microsoft ecosystem, Power BI offers deep integration with Azure, Office 365, and related tools. Recent versions include AI features like natural narratives and Copilot integration, and it has strong governance capabilities.

The trade-offs are licensing complexity and less flexibility for advanced code work (most logic happens in DAX or the GUI rather than Python). Connectivity to non-Microsoft tools sometimes requires extra work.

Best for: Enterprise teams already invested in Microsoft/Azure infrastructure and limited budget.

5. Looker

Looker's modeling layer (LookML) provides excellent governance and metric consistency across large organizations. It's particularly strong for companies that need a semantic layer ensuring everyone uses the same definitions.

The learning curve is steep, though, and it's less optimized for rapid prototyping or AI-assisted analysis. Natural language querying isn't built in and there’s no Python support, and workflow automation needs to be fully custom built aside from connectors to the Google ecosystem.

Best for: Large organizations prioritizing metric governance and consistency and with a large budget.

6. Google Looker Studio

For teams wanting fast, free dashboards tied to Google tools, Looker Studio (not to be confused with Looker) has a low barrier to entry and broad connector support.

It's limited for advanced analysis—minimal code support, basic governance, and weak versioning. Automated insight delivery typically happens through external tools or manual processes. But for simple visualization needs, it's hard to beat the price and ease of use.

Best for: Small teams needing quick dashboards with minimal setup.

Moving beyond the dashboard paradigm

The fundamental issue with traditional BI isn't that dashboards are bad—it's that they're insufficient on their own.

Common problems that you’ve likely run into in your own experiences with legacy BI:

• Dashboard graveyards: Beautifully designed dashboards that nobody checks
• Manual workarounds: Repeated CSV exports and manual manipulation in spreadsheets
• Delayed action: By the time someone notices a trend, the opportunity has passed
• Visibility without action: Seeing a problem doesn't mean having the context or workflow to fix it

The solution is thinking about insights as operations rather than artifacts. Instead of building more dashboards, focus on:

1. Automating delivery - Send insights where people already work
2. Building workflows - Route metrics and alerts based on conditions, not schedules
3. Connecting diverse sources - Bring together warehouse data, spreadsheets, and app data
4. Enabling self-service - Let stakeholders get answers without always involving data teams

This is where AI BI tools and platforms with strong automation capabilities start to show their value.

Choosing the right Metabase alternative

The right choice depends on your specific situation:

If you have mixed skill levels and need both code flexibility and AI assistance for non-technical users, Fabi or Mode might work well. Fabi edges ahead if automated insight delivery and diverse data connectivity are priorities.

If you're engineering-first with infrastructure resources and prefer open-source and don’t need any AI functionality, Superset/Preset gives you maximum control.

If you're Microsoft-centric, Power BI's ecosystem integration might outweigh other considerations.

If you need strong governance and semantic modeling at enterprise scale, Looker's modeling layer is purpose-built for that.

If you just need simple dashboards quickly, Looker Studio gets you there with minimal friction.

The key is matching the tool to your actual workflow needs, not just visualization capabilities. Try building a real use case—automate a weekly insight, push it to Slack, connect a non-warehouse source—and see which platform makes that easiest.

For many modern data teams looking to move beyond static dashboards into automated, AI-assisted analytics, platforms like Fabi represent where the space is heading. But the best Metabase alternative is ultimately the one that fits your team's specific needs, skill sets, and data infrastructure.

If you want to give Fabi a try, you can get started in less than 5 minutes for free by signing up here.