Decision comparison
Lightdash vs Apache Superset
Lightdash and Superset are both open-source BI platforms with browser-based exploration, and they disagree about where a metric is defined. Lightdash reads metrics from the dbt project, so the definition stays in version control. Superset defines datasets and charts inside the application, which works without dbt and puts governance in the platform instead.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are BI platforms.
Quick Comparison
| Decision factor | Lightdash | Apache Superset |
|---|---|---|
| What it is | A BI platform built directly on dbt, where metrics defined in dbt become the metrics people chart | An Apache-governed BI platform where dashboards are built over warehouse datasets defined in the application |
| Where metrics live | In the dbt project, defined once and read by every chart | In datasets and charts defined inside Superset |
| Prerequisite | A dbt project somebody maintains | A warehouse connection |
| Governance | Metric definitions reviewed in version control with the rest of dbt | Role-based access control, dataset permissions and row-level security in the application |
| Chart range | Focused on what dbt metrics express well | A wide library covering most visualisation needs |
| Best fit | Teams on dbt who want its metrics exposed for self-service | Organisations wanting broad self-service dashboards without a dbt prerequisite |
| Connectivity | Queries Snowflake, BigQuery, Redshift, Databricks and Postgres over standard drivers | Queries Snowflake, BigQuery, Redshift, Databricks and Postgres over standard drivers |
Lightdash
- What it is:
- A BI platform built directly on dbt, where metrics defined in dbt become the metrics people chart
- Where metrics live:
- In the dbt project, defined once and read by every chart
- Prerequisite:
- A dbt project somebody maintains
- Governance:
- Metric definitions reviewed in version control with the rest of dbt
- Chart range:
- Focused on what dbt metrics express well
- Best fit:
- Teams on dbt who want its metrics exposed for self-service
- Connectivity:
- Queries Snowflake, BigQuery, Redshift, Databricks and Postgres over standard drivers
Apache Superset
- What it is:
- An Apache-governed BI platform where dashboards are built over warehouse datasets defined in the application
- Where metrics live:
- In datasets and charts defined inside Superset
- Prerequisite:
- A warehouse connection
- Governance:
- Role-based access control, dataset permissions and row-level security in the application
- Chart range:
- A wide library covering most visualisation needs
- Best fit:
- Organisations wanting broad self-service dashboards without a dbt prerequisite
- Connectivity:
- Queries Snowflake, BigQuery, Redshift, Databricks and Postgres over standard drivers
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Lightdash | Apache Superset |
|---|---|---|
| Docker Hub pulls(Product adoption) | 2.8M | 605.5M |
| GitHub commits, 90d(Product adoption) | 4.7k | 2.3k |
| GitHub stars(Product adoption) | 6,000+ | 74,000+ |
| Search interest(Market interest) | 0 | 0 |
| Hacker News mentions, 90d(Community interest) | 1 | 0 |
| npm weekly downloads(Developer adoption) | 30.0k | 13.8k |
| PyPI weekly downloads(Developer adoption) | 53 | Not available |
| Product Hunt comments(Community interest) | Not available | 0 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 69 |
| PyPI weekly downloads(Product adoption) | Not available | 87.1k |
| Stack Overflow questions(Community interest) | Not available | 1.3k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Lightdash
September 21, 2026Package vulnerabilities
npm · @lightdash/common@2.274.1 · PyPI · lightdash@1.1.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Apache Superset
September 21, 2026Package vulnerabilities
npm · @superset-ui/core@0.20.4 · PyPI · apache-superset@6.1.0
0 vulnerabilities
across 2 packages
Repository security score
github.com/apache/superset
5.4/10
Interface Preview
Lightdash
Apache Superset
Feature Comparison
| Feature | Lightdash | Apache Superset |
|---|---|---|
| Modelling | ||
| Metrics defined in dbt | Full support | Not verified |
| Metric definitions under version control | Full support | Partial support |
| Datasets defined in the application | Partial support | Full support |
| Self-service exploration in a browser | Full support | Full support |
| Delivery | ||
| Wide chart library | Partial support | Full support |
| Interactive filtering | Full support | Full support |
| Row-level security | Full support | Full support |
| Embedding in your own application | Full support | Full support |
| Adoption | ||
| Works without dbt | Not verified | Full support |
| Managed service option | Full support | Partial support |
| Large community | Partial support | Full support |
| Runs on your own infrastructure | Full support | Full support |
| Platform | ||
| Warehouse connectivity | Full support | Full support |
| Self-hosted deployment | Full support | Full support |
| Open source | Full support | Full support |
| Scheduled delivery | Full support | Full support |
Modelling
Metrics defined in dbt
Metric definitions under version control
Datasets defined in the application
Self-service exploration in a browser
Delivery
Wide chart library
Interactive filtering
Row-level security
Embedding in your own application
Adoption
Works without dbt
Managed service option
Large community
Runs on your own infrastructure
Platform
Warehouse connectivity
Self-hosted deployment
Open source
Scheduled delivery
Which to choose
Lightdash and Superset are both open-source BI platforms with browser-based exploration, and they disagree about where a metric is defined. Lightdash reads metrics from the dbt project, so the definition stays in version control. Superset defines datasets and charts inside the application, which works without dbt and puts governance in the platform instead.
Best-fit scenarios
Choose Lightdash if:
Choose Lightdash when dbt is already the source of truth for metrics. Defining revenue once in the dbt project and charting it everywhere means the definition is reviewed like code and cannot quietly diverge across dashboards, which is the failure that eventually arrives in every BI deployment.
Choose Apache Superset if:
Choose Apache Superset when there is no dbt project, or when the dashboard audience is wider than the people who maintain one. Datasets defined in the application need no upstream prerequisite, the chart library is broad, and access control and row-level security are handled in the platform.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Is depending on dbt a constraint or a feature?
Both, depending on whether the project is well maintained. A good dbt project means metric definitions are reviewed, tested and consistent, and a BI tool reading them inherits all of that. A neglected one means the BI tool inherits the neglect, and analysts are constrained to models nobody is updating. The tool amplifies whatever the project already is.
How many places will a metric be defined?
One, if the metric comes from dbt and every chart reads it. Several, if each dashboard defines its own — which is the default in any platform where datasets are built in the application, unless somebody enforces discipline. That difference is invisible in month one and decisive in year two, when three dashboards disagree about revenue.
Which is easier to adopt?
Superset, if you have no dbt project, because it needs only a warehouse connection. Lightdash, if you do, because the modelling work is already done and exposing it is straightforward. The adoption cost is almost entirely determined by what already exists upstream rather than by the platforms themselves.
What does each one need to run?
Lightdash needs a running service — self-hosted or Lightdash Cloud — and a dbt project with metrics and dimensions defined in it, because that project is its semantic layer; without one there is little for it to show. Superset needs a metadata database, a cache, workers for asynchronous queries and somebody to upgrade it, and it connects to any database with a driver without assuming anything about how your data is modelled. One presumes an analytics engineering practice; the other presumes only a database.
What happens to the cost as more people use it?
Neither charges per seat, because both are open source, so growth does not trigger a procurement conversation. What grows is warehouse compute in both cases, since both issue SQL on behalf of the people using them — moderated by caching in Superset and by the modelled, generated queries in Lightdash. The other cost that grows is human: Superset accumulates dashboards that need reconciling, and Lightdash accumulates dbt model work as definitions change. Budget for the second kind, because it never appears on any invoice.
How should we evaluate them?
Rebuild one dashboard people already rely on, and answer one question nobody has a dashboard for. The first tests modelling, refresh and permissions against something you already understand, so differences are obvious. The second tests whether a person who is not on the data team can actually get an answer, which is what self-service means and what demos never show.