Tool Evaluation Series: Lightdash

Chris Nguyen
5 min readJan 7, 2024


Lightdash is a dbt-oriented business intelligence (BI) platform designed for self-service analytics and metrics. It markets itself as an open-source alternative to Looker. I will evaluate Lightdash in terms of expanding data analytics capabilities to a more general audience — defined as the audience outside of data teams that may not be highly trained in data but want to use it to answer their own business questions without having to wait for the data team to answer them. Or in other words “Is Lightdash a good self-service tool?”.

The way I will do this is by defining a few criteria that I care about and using Lightdash to see how well it fulfills that criteria. The criteria will be:

  1. Setup & Maintenance: is the tool easy to set up, maintain, and learn?
  2. Useful Features: what are 2–3 things that make this tool stand out?
  3. Cost: how expensive do I think this tool is? I define expensive as “Does this cost me as much as Tableau?”
  4. Audience Fit: do I think the tool fits my intended general audience?

1. Setup & Maintenance

Lightdash itself is pretty easy to set up and start making visualizations online. It has a command line interface (CLI), a self-host option, and cloud hosting. However, because Lightdash is a dbt-native tool, it requires a dbt project to be set up before it can be used. What’s more, the dbt project has to be set up in the correct way — meaning that the project needs proper documentation and definition of metrics in Lightdash to work. This makes setup harder since you need good documentation, which might involve a workflow changes within the team if the docs are constantly outdated. This also means that for new metrics or changes, you’d have to expand dbt capabilities outside of the data team if Lightdash is used.

There are online docs, Youtube tutorials (although not a lot at the moment), and a Slack community so learning resources are there. But it does seem a little lighter than other tools that I evaluated. You’d have to want to tinker with Lightdash to really understand it so it’s not usable right out of the box.

3 out of 5 points

2. Useful Features

  • Integration with dbt Docs: Lightdash is dependent on properly defined metrics. This means that you can define and control how aggregations are summed, counted, etc. in the dbt documentation.
Define Lighdash metrics in the meta field of dbt Docs
  • Viz based on defined metrics: after defining the dimensions and metrics of a dataset, you can visualize the results in a simple interface with customizable charts and filters.
Visualize results after defining them in the docs
  • Lightdash CLI: Lightdash comes with a command line interface tool that can be installed. Very useful in that it can run dbt models that can then subsequently be used in the Lightdash visualizations without leaving the command line. For CLI enthusiasts, this would be a great add-on.
Example Lightdash CLI commands

Lightdash has a lot of features connected to dbt-oriented workflows. For the correct setup, it could work really well but an organization would have to be all-in with regard to dbt for it to work.

4 out of 5 points

3. Cost

What’s nice about Lightdash is that for the Cloud version, the price of it doesn’t depend on the number of users because it is currently unlimited. The catch is that the price point could be considered high, specifically for smaller teams that don’t need to scale as much. For larger teams that are expanding, it might be more of a relief that adding an extra 30 licenses won’t multiply your monthly costs. I do appreciate the upfront pricing plans too and it is definitely cheaper than Looker. And of course, you can always self-host Lightdash.

3 out of 5 points

Lightdash pricing

4. Audience Fit

Do I think Lightdash fits my general audience? Lightdash seems to a dbt-native tool that takes advantage of dbt’s documentation to define metrics in its meta field. But this requires familiarity with dbt itself first and how to contribute to the project to define what and how to count, sum, or aggregate in some way. Expanding dbt to a more general audience as a prerequisite might mean being pushed from dbt Core to dbt Cloud, which seems like an extra cost if you don’t already have Cloud. All-in-all, it could require a major change in how the data team and other teams operate. That’s a high cost in terms of workflow change so unless you’re already well-positioned for that, I think it is too much of a switch for my audience.

1 out of 5 points


Lightdash is an opinionated tool, meaning it defines a specific workflow on how to work with it. This may be seen as inflexible for teams that cannot work in such a way. Its dependence on defining metrics in dbt is good in that it makes sure the metric is consistent as a single source of truth. It’s sort of like a “dbt Semantics Layer-lite”. But setting up that metric isn’t automatic so there’s still a dependence on the data team for that since you have to contribute to the docs and test the metric. For a general audience that just needs basic SQL and filters, I think that is too much to ask.

11 out of 20 points