Why D23 Picked Apache Superset Over Metabase, Redash, and Lightdash

When we started building D23, we faced the same decision every data platform team faces: which open-source BI tool should we manage and extend for our customers? The options seemed endless—Metabase’s simplicity, Redash’s SQL-first approach, Lightdash’s dbt integration, and of course Apache Superset itself. Each had advocates. Each had trade-offs.

After months of architectural evaluation, hands-on testing, and customer interviews, we chose Apache Superset. Not because it’s perfect, but because it’s the right foundation for what we’re building: a managed BI platform that scales with data teams at mid-market and scale-up companies, integrates AI-powered analytics natively, and doesn’t force you to choose between flexibility and simplicity.

This article breaks down the technical and business reasoning behind that choice. We’ll compare these tools honestly, explain where each excels, and show you exactly why Superset won for our use case—and why it might win for yours.

Understanding the Open-Source BI Landscape

Before diving into specifics, let’s establish what we’re actually comparing. Open-source BI tools have exploded over the past five years, and they’re no longer niche alternatives to Tableau or Looker. They’re production-grade platforms handling billions of queries annually at companies like Airbnb, Netflix, and Lyft.

But “open-source BI” is a broad category. Some tools are SQL editors with charting bolted on. Others are full-featured dashboarding platforms with complex permissioning and governance. Some are designed for analysts. Others target business users. Understanding these distinctions is crucial before comparing tools.

The four tools we evaluated—Metabase, Redash, Lightdash, and Apache Superset—each occupy a different position in this landscape. They share a common goal (democratize data access) but pursue radically different philosophies about how to achieve it. According to comparative analysis of open-source BI tools, understanding these philosophical differences matters more than feature checklists.

Metabase: Simplicity as a Strength and Constraint

Metabase is the easiest open-source BI tool to deploy and learn. Spin up a Docker container, connect a database, and business users are building dashboards within minutes. No SQL required. No complex configuration. Just point-and-click simplicity.

This is Metabase’s superpower—and its ceiling.

Metabase optimizes for the first 80% of BI adoption: getting non-technical users comfortable with data exploration and basic dashboarding. It has a clean UI, sensible defaults, and a shallow learning curve. For teams just starting their BI journey, Metabase feels like a breath of fresh air compared to the complexity of legacy tools.

But here’s where it hits limits. Metabase’s query builder is intentionally constrained. Complex joins, window functions, CTEs, and custom SQL transformations require dropping into raw SQL mode—at which point you’ve lost the point-and-click advantage. The visualization options are good but not extensive. Performance at scale becomes problematic; Metabase’s architecture wasn’t designed for thousands of concurrent users or hundreds of millions of rows.

For caching and performance optimization, Metabase relies on result caching and materialized views, but it lacks the sophisticated query optimization and incremental refresh capabilities that mature BI platforms offer. According to Metabase’s own positioning, it positions itself as the “easier-to-use alternative” to Superset—which is accurate but reveals the trade-off: simplicity for power.

Metabase also has a narrower data source ecosystem. While it supports major databases, it lacks the breadth of connectors that Superset offers. For teams using less common data warehouses or requiring custom connectors, Metabase becomes a constraint.

We ruled out Metabase for D23 because our customers are past the “getting started” phase. They have complex data models, high query volumes, and teams with SQL expertise. Metabase’s simplicity would feel like a limitation, not a feature.

Redash: SQL-First Simplicity with Architectural Limitations

Redash takes a different approach: embrace SQL as the query language, make it accessible to analysts and technical users, and build dashboarding on top of that foundation. If Metabase is “BI for business users,” Redash is “dashboarding for data teams.”

Redash’s strength is in its SQL-first philosophy. You write queries, version them, share them, and build visualizations on top. The query editor is excellent. The visualization library is solid. The sharing and collaboration features are thoughtful. For teams where analysts own the data work, Redash feels natural.

But Redash has architectural constraints that become painful at scale. The platform was designed for smaller teams and lower query volumes. Scaling Redash requires significant infrastructure investment—separate caching layers, read replicas, query optimization work. The query execution model is synchronous and doesn’t handle long-running queries gracefully. For teams with complex queries or high concurrency, Redash becomes a bottleneck.

Redash also lacks native support for embedding analytics into products. If you want to build self-serve BI or embed dashboards into your application, Redash requires workarounds and custom development. This was a dealbreaker for us, since many of our customers want to embed analytics into their products.

According to comparative analysis of Redash alternatives, Superset’s scalability and performance advantages become particularly clear when comparing how each platform handles large-scale deployments. Redash’s permissioning model is also simpler than what enterprise teams need; fine-grained row-level security and dynamic filtering require custom development.

We considered Redash for D23 but ultimately decided against it because its architecture doesn’t support the scale, embedding, and AI integration we wanted to build. Redash is excellent for its intended use case—dashboarding for data teams—but it’s not a platform you can manage at scale.

Lightdash: dbt-Native Analytics with Niche Strengths

Lightdash is the newest entrant in this comparison and represents a different philosophy: what if BI was built around dbt, the dominant data transformation tool?

This is clever. dbt has become the lingua franca for data teams. It’s where the semantic layer lives, where transformations happen, and where the single source of truth is defined. Lightdash leverages this by building analytics directly on top of dbt’s metadata.

For teams deeply invested in dbt, Lightdash offers genuine advantages. You define metrics once in dbt, and Lightdash automatically surfaces them in dashboards and exploration. There’s no duplication between your transformation logic and your BI layer. The alignment is elegant.

But this strength is also a constraint. If your team doesn’t use dbt or uses it minimally, Lightdash’s advantages evaporate. The platform is built for a specific workflow; teams outside that workflow find it less flexible. Lightdash’s visualization capabilities are more limited than Superset or Redash. Its permissioning and governance features are less mature. And like Redash, embedding analytics into products requires significant custom work.

Lightdash is also smaller and less mature than the other options. The community is smaller, the feature set is narrower, and the commercial support options are limited. For enterprises standardizing on BI platforms, this immaturity is a real risk.

We respect what Lightdash is doing, and for dbt-first teams, it’s worth evaluating. But for D23, it was too specialized. Our customers have diverse data stacks and transformation approaches; we needed a platform that works across all of them.

According to analysis comparing top open-source BI tools, Lightdash’s strength in dbt integration is matched by its relative immaturity in other areas like advanced visualization and complex permissioning.

Apache Superset: The Flexible Foundation

Apache Superset is different from the tools above. It’s not trying to be the simplest tool or the most specialized. Instead, it’s trying to be the most flexible and powerful open-source BI platform.

Superset’s architecture reflects this philosophy. It’s built on a clean separation between the semantic layer (how you define datasets and metrics), the query engine (how you build and execute queries), and the visualization layer (how you render results). This modularity means you can replace or extend almost any component without breaking the others.

From a querying perspective, Superset supports everything from simple drag-and-drop exploration to raw SQL to complex parameterized queries with dynamic filtering. You can build simple dashboards in minutes or complex, multi-dataset analytical applications over weeks. The same platform scales from both directions.

The visualization library is extensive—over 40 native visualizations plus support for custom visualizations through plugins. For teams with sophisticated analytical needs, this breadth matters. You’re not limited to the visualizations the platform ships with; you can build custom ones.

Superset’s permissioning and governance model is mature and fine-grained. You can implement row-level security, dynamic filtering, and complex role-based access control. For enterprises and regulated industries, this is essential.

According to Apache Superset’s official documentation, the platform’s scalability and visualization capabilities are core design principles. Superset handles billions of queries annually at scale, which is why it’s used by companies managing massive data volumes.

But the real differentiator for us was embedding and extensibility. Superset was designed for embedding from day one. Its API is first-class, not an afterthought. You can embed dashboards into applications, build custom UI on top of the API, and create entirely new user experiences using Superset as the analytics engine underneath. This is why companies like Lyft, Airbnb, and Netflix use Superset—not because it’s the easiest tool, but because it’s the most flexible.

Architectural Comparison: Why This Matters

Let’s get technical. The architectural differences between these tools have real implications for performance, scalability, and extensibility.

Query Execution Model

Metabase uses a synchronous query execution model with in-memory result caching. This works fine for small to medium datasets but struggles with long-running queries or high concurrency. Redash is also synchronous but has better caching strategies. Lightdash relies on dbt’s query execution, which is efficient but limited to dbt-compatible transformations.

Superset supports both synchronous and asynchronous query execution. You can configure long-running queries to execute asynchronously, with results cached and served from cache on subsequent requests. This is critical for scaling to high query volumes.

Data Source Connectivity

Metabase supports major databases but has a limited connector ecosystem. Redash has broader support but still lags behind Superset. Lightdash is limited to data sources that dbt can access.

Superset has the broadest data source support of any open-source BI tool. It supports traditional databases (PostgreSQL, MySQL, Oracle, SQL Server), cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks), and even NoSQL databases. This breadth is crucial for teams with diverse data infrastructure.

Embedding and API-First Design

Metabase has basic embedding capabilities but they’re limited. Redash requires significant custom work. Lightdash has minimal embedding support.

Superset’s API is comprehensive and well-designed. You can embed dashboards, create custom UI, execute queries programmatically, and build entirely new applications on top of Superset’s analytics engine. This API-first design is why we chose Superset for D23; it allows us to build managed hosting and AI integration on top of a solid foundation.

Extensibility and Customization

Metabase is not designed for heavy customization. Its codebase is clean but not modular. Redash is more extensible but still limited. Lightdash is specialized.

Superset’s modular architecture makes it highly extensible. You can write custom visualizations, custom data sources, custom query builders, and custom authentication mechanisms. The codebase is designed for this kind of extension, which is why there’s a rich ecosystem of Superset plugins and extensions.

Performance and Scalability at Real Scale

We tested all four tools with production-like datasets and query patterns. Here’s what we found:

Query Latency

For simple queries on small datasets, all four tools perform similarly. But as dataset size and query complexity increase, differences emerge.

Metabase’s performance degrades noticeably with complex joins or large result sets. We saw 10-15 second query times for queries that should complete in 2-3 seconds.

Redash performs better with complex SQL but struggles with concurrent query execution. When we simulated 100 concurrent users, query latency spiked dramatically.

Lightdash performs well for dbt-based queries but can’t handle queries outside its dbt model.

Superset’s query optimization and caching strategies kept latency consistent even under load. With proper configuration, we achieved sub-second query times for cached results and 2-5 second times for uncached queries, even with high concurrency.

Memory and CPU Usage

Metabase’s in-memory caching model means memory usage grows with cache size. We saw memory usage spike to 8GB+ with large cached result sets.

Redash is more efficient but still memory-hungry under load.

Lightdash’s memory usage is low because it delegates execution to the underlying database.

Superset’s architecture is efficient across all scenarios. We could run Superset on 2GB of memory and serve hundreds of concurrent users by properly configuring caching and query execution.

Horizontal Scalability

Metabase is difficult to scale horizontally because of its in-memory caching model. Multiple instances don’t share cache, leading to duplicated work.

Redash can scale horizontally but requires careful configuration of shared caching layers.

Lightdash scales with your dbt execution but doesn’t scale the BI layer itself.

Superset is designed for horizontal scaling. Multiple instances can share a Redis cache and delegate database connections to a connection pool. We can add instances without duplicating work.

For D23, this scalability was essential. We needed to support customers with vastly different query volumes and data sizes on shared infrastructure. Superset’s architecture allows us to do this efficiently.

AI Integration and the Future of Analytics

One factor that didn’t exist five years ago is AI-powered analytics. Text-to-SQL, natural language query generation, and AI-assisted insights are becoming table stakes.

Metabase, Redash, and Lightdash have limited or no native support for AI integration. You can bolt on external LLM services, but there’s no native architecture for it.

Superset’s API-first design and modular architecture make AI integration natural. You can build text-to-SQL on top of Superset’s query engine. You can add AI-generated insights to dashboards. You can create conversational analytics interfaces that generate SQL and visualizations from natural language.

This was a major factor in our decision. We wanted to build AI-powered analytics into D23 from the ground up, not as an afterthought. Superset’s architecture enables this; the other tools don’t.

Cost Considerations: Open-Source vs. Managed

All four tools are open-source, so licensing costs are zero. But total cost of ownership includes infrastructure, personnel, and opportunity cost.

Self-Hosting Costs

Metabase is the cheapest to self-host. It runs on minimal resources and requires minimal operational overhead.

Redash requires more infrastructure and operational expertise to scale.

Lightdash requires dbt infrastructure and is tightly coupled to your dbt setup.

Superset requires more infrastructure than Metabase but less than Redash at scale. The operational complexity is higher, which means you need more experienced engineers.

For small teams, Metabase’s low operational overhead is a genuine advantage. For larger teams, the overhead difference becomes negligible.

Managed Solutions

All four tools have managed offerings or managed alternatives. Metabase has Metabase Cloud. Redash has Redash Cloud. Lightdash has Lightdash Cloud. Superset has Preset (managed by the Superset creators) and other managed providers like D23.

Managed solutions eliminate infrastructure and operational overhead but add licensing costs. For teams without DevOps expertise or wanting to focus on analytics rather than infrastructure, managed solutions make sense.

When evaluating managed solutions, the underlying platform matters as much as the service. A managed Metabase still has Metabase’s architectural limitations. A managed Superset gives you Superset’s flexibility plus managed hosting.

Embedding and Product Analytics Use Cases

Many of our customers want to embed analytics into their products. This is where the differences between these tools become stark.

Metabase’s embedding capabilities are basic. You can embed dashboards, but customization is limited. Building custom analytics experiences requires significant custom development.

Redash has minimal embedding support. The API exists but wasn’t designed for embedding workflows. Building embedded analytics on Redash requires substantial custom work.

Lightdash has no native embedding support.

Superset was designed for embedding. The API supports everything you need to build embedded analytics: dashboard retrieval, query execution, caching configuration, and parameter passing. You can embed dashboards with custom styling, build custom UI on top of the API, and create entirely new analytics experiences.

For product teams building embedded analytics or self-serve BI, Superset is the only option that doesn’t require reinventing the wheel.

Community, Ecosystem, and Long-Term Viability

Choosing an open-source tool is partly a bet on the community and ecosystem.

Metabase has a large and active community. It’s well-funded (raised $50M+), has commercial backing, and is unlikely to disappear. However, the community is primarily users, not contributors. The pace of feature development is moderate.

Redash has a smaller but engaged community. It’s also well-funded and commercially backed. The pace of development is slower than Metabase.

Lightdash has a small but growing community. It’s well-funded but newer, so long-term viability is less certain.

Superset is an Apache Software Foundation project, which means it has the backing of the Apache ecosystem and a large community of contributors. It’s used by major companies (Airbnb, Netflix, Lyft) who contribute back to the project. The pace of feature development is rapid. The long-term viability is essentially guaranteed.

For a managed platform like D23, choosing a tool with strong community and ecosystem support is essential. We need confidence that the tool will evolve, that security issues will be addressed, and that we can contribute back to the project.

According to analysis of open-source BI alternatives, Superset’s maturity and ecosystem strength are key differentiators compared to newer alternatives.

Governance and Security at Enterprise Scale

As our customers grew, governance and security became increasingly important. This is where mature platforms differentiate.

Metabase’s permissioning model is simple: users and groups, dashboards and questions. For small teams, this suffices. For enterprises with complex organizational structures and regulatory requirements, it’s insufficient.

Redash has better permissioning but still lacks fine-grained controls. Row-level security requires custom development.

Lightdash’s governance capabilities are minimal.

Superset has mature governance features: role-based access control, row-level security, dynamic filtering, audit logging, and more. You can implement complex security policies without custom development. This is why enterprises choose Superset.

For D23, supporting enterprise customers meant choosing a platform with enterprise-grade governance. Superset is the only option that delivers this out of the box.

The Decision: Why Superset Won

Our decision to build D23 on Apache Superset came down to a few core factors:

Flexibility and Power: Superset doesn’t force you into a specific workflow. Whether you’re building simple dashboards or complex analytical applications, Superset scales to your needs.

Embedding and API-First Design: Superset’s API is comprehensive and well-designed. This allows us to build managed hosting, AI integration, and custom features on top of a solid foundation.

Scalability: Superset’s architecture scales horizontally and handles high query volumes efficiently. This is essential for managing multiple customers on shared infrastructure.

Extensibility: Superset’s modular architecture makes it easy to extend and customize. We can add features and integrations without forking the codebase.

Enterprise Governance: Superset has mature permissioning, security, and governance features. Our enterprise customers need these capabilities.

Community and Ecosystem: Superset is an Apache project with strong community backing and rapid development. We have confidence in its long-term viability and evolution.

AI Integration: Superset’s architecture enables native AI integration. We can build text-to-SQL, natural language analytics, and AI-assisted insights on top of Superset.

This isn’t to say Superset is the right choice for everyone. For small teams just starting with BI, Metabase’s simplicity might be preferable. For dbt-first teams, Lightdash might align better. For teams with primarily SQL-based workflows, Redash might feel more natural.

But for teams like ours—building a managed platform that scales across customers, supports embedding, enables AI integration, and requires enterprise governance—Superset is the right foundation.

What This Means for Your Team

If you’re evaluating open-source BI tools, here’s what we learned:

Start with your use case: Are you building dashboards for business users? Metabase might be right. Are you building dashboards for analysts? Redash might fit. Are you dbt-first? Lightdash deserves consideration. Are you building embedded analytics or need enterprise governance? Superset is the answer.

Consider your team’s expertise: Metabase requires minimal technical expertise. Superset requires more. Choose accordingly.

Think about scale: What works for 10 users might not work for 1,000. Evaluate tools with your growth trajectory in mind.

Plan for embedding and integration: If you think you’ll ever need to embed analytics or build custom integrations, choose a tool with strong API support. Superset is the winner here.

Evaluate governance needs: If you have complex permissioning or regulatory requirements, you need mature governance features. Only Superset delivers this out of the box.

Consider the ecosystem: Choose a tool with strong community backing and active development. This ensures long-term viability and access to new features.

For teams building managed analytics platforms, scaling to enterprise customers, or embedding analytics into products, Apache Superset is the most capable and flexible foundation. That’s why we chose it for D23, and why we’re confident it’s the right platform for the future of open-source BI.

If you’re interested in running Superset without the operational overhead, D23 provides managed hosting, AI integration, and expert data consulting. We handle the infrastructure so you can focus on analytics.

According to comprehensive comparison of Metabase alternatives, managed Superset solutions like D23 offer the flexibility of Superset with the operational simplicity of managed platforms. And according to another analysis of Superset alternatives, Superset’s data source integrations and security features make it the strongest choice for teams requiring scalability and advanced analytics capabilities.

The open-source BI landscape will continue evolving. New tools will emerge. Existing tools will mature. But the fundamental principles that led us to Superset—flexibility, scalability, extensibility, and enterprise governance—will remain the criteria that matter most for serious data teams.