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Verity vs Lightdash

Lightdash alternative for ecommerce teams

Verity vs Lightdash

Lightdash builds a BI layer from a semantic layer you define as code, in a dbt project or in Lightdash YAML files, on a warehouse you already run. Its core is open source and it is made for data teams. Verity is made for ecommerce and marketing teams without one: it loads your store, ad and email data into your own BigQuery, maintains the data models and the KPI definitions, and has the dashboards ready on the first day. That is a different buyer, and this page says which is which.

Checked against Lightdash’s public sources on 13 November 2026.* Compared as dashboards and BI.

Dashboards and BI in Verity →

At a glance

Yes

Partly

No, or not found in public sources*

VerityLightdash*
Where the data comes from
Loads your store, ad and email data for you
Yes: Managed, on a schedule
Not found in public sources: Connects to your warehouse
Works on your own BigQuery
Yes
Yes: And nine other warehouses
Data model built and maintained for you
Yes
Not found in public sources: You define it as code
KPIs and definitions
One definition per KPI in a semantic layer
Yes: You approve it
Yes: dbt or Lightdash YAML
KPIs against targets and budgets
Yes: Targets, budgets, guardrails
Partly: Threshold alerts
Ready made marketing and ecommerce dashboards
Yes: Per source and combined
Not found in public sources
Working with it
Build your own dashboards without code
Yes
Yes: On metrics you defined
Ask questions in plain language
Yes: Data Chat
Yes: AI agents, usage based
MCP server for AI assistants
Yes: 100+ tools
Yes: Cloud, usage based
AI assistant builds dashboards over MCP
Yes: As a draft for review
Yes: Charts and dashboards
Sharing and taking it with you
Share with the team and send on a schedule
Yes: Workspaces, email, spreadsheet
Yes: Email, Slack, Sheets
Dashboards as code
Yes: Export in seven formats
Yes: Its own YAML files
Embed dashboards in your own site or app
No
Yes: Paid add on
Hosting and pricing
Open source or self hosted option
No: Managed cloud
Yes: MIT core
Published pricing
Yes: From €500/mo, unlimited users
Yes: Cloud Pro from $3,000/mo

Where Lightdash is ahead

For a data team, almost everywhere. Lightdash is open source with an MIT licensed core, runs on ten warehouses and can be self hosted. The whole BI layer is code: metrics in dbt or Lightdash YAML, charts and dashboards downloaded, edited and uploaded as files, kept in Git. It embeds dashboards in your own product, which Verity does not do. And it reports on any table in the warehouse, where Verity is built for marketing and ecommerce numbers.

Where the two differ most

In who does the work before the first chart. Lightdash connects to a warehouse and to a semantic layer that your team writes. It does not load data from Shopify or an ad platform, so pipelines and data models are a project of their own. Verity includes that project: managed loading into your own BigQuery, data models that are maintained for you, and a semantic layer it proposes and you approve. Already defining metrics in dbt? Verity reads those too. The choice is not final either: any Verity dashboard can be exported as code in Lightdash format, as a head start for a team that moves on.

How the pricing compares

Lightdash is free to self host as open source, with community support. Lightdash Cloud Pro starts from $3,000 per month with unlimited users, and AI agents, MCP and embedding are charged by usage on top. The warehouse, the pipelines and the people who model the data are separate costs. Verity starts at €500 per month excluding VAT with unlimited users, and the price follows the parts you use: Analytics, Data Management and Activation.

Which one fits

Choose Verity when

You have no data team, and nobody to load Shopify, GA4, Meta Ads, Google Ads and Klaviyo into a warehouse and model them.

You want dashboards per channel and for the whole business on the first day, not after a modelling project.

You want revenue, new customers and return on ad spend defined for you and approved by you, not written in YAML.

You want targets and budgets next to the actuals, attribution models side by side and reports by email in the same tool.

You want to act on the numbers too: segments to Meta Ads, Google Ads and Klaviyo, journeys and onsite personalisation.

You want to start managed and keep the door open. A Verity dashboard exports as code in Lightdash format, so a data team that arrives later can carry on in Lightdash.

Choose Lightdash when

You have a data team that already models the business in dbt and wants BI on exactly those definitions.

You want metrics, charts and dashboards as code, in Git, with previews and checks before a change goes live.

You want to run open source software on your own infrastructure. The Lightdash core is MIT licensed and can be self hosted.

Your warehouse is Snowflake, Databricks, Redshift or another one Lightdash supports. Verity works on BigQuery only.

You need reporting on all company data, or analytics embedded in your own product.

Built in, add on or separate tool

What you get as part of each product, and what comes on a higher plan, as an add on or from another vendor.

VerityLightdash*
Loading store, ad and email dataBuilt in and managedA separate ETL tool, into your warehouse
Data modelsBuilt in and maintained for youYour dbt project or your own tables
KPI definitionsSemantic layer, proposed and approved in the productSemantic layer you write in dbt or Lightdash YAML
DashboardsReady made per source, plus your ownYou build them, in the product or as code
Dashboards as codeExport as code in seven formats, Lightdash among themDownload and upload as YAML files in its own format
AI questions and MCPBuilt inBuilt in on Cloud, charged by usage
Embedding in your own productNot offeredA paid add on for Cloud Pro and Enterprise
Self hostingNot offeredOpen source core, enterprise features need a licence
Segments, journeys and onsite personalisationActivation, a part of the platformNot found in its public sources

What we checked about Lightdash

Each statement comes from Lightdash’s own website or documentation, read on 13 November 2026.*

  • Lightdash describes itself as agentic analytics for modern data teams, and as the governed data layer for all your agents.

    Source: Lightdash homepage, read on

  • Lightdash builds its BI layer from a semantic layer you define as code, either in a dbt project or in Lightdash YAML files that point at tables already in your warehouse.

    Source: Lightdash docs: set up Lightdash, read on

  • The supported warehouses are BigQuery, Postgres, Supabase, Redshift, Snowflake, Databricks, Trino, ClickHouse, DuckDB and Athena. The page names no connection to applications such as a shop platform or an ad platform.

    Source: Lightdash docs: connect a project, read on

  • The pricing page lists an open source, self hosted edition with unlimited users and community support, Cloud Pro from $3,000 per month with unlimited users, and Enterprise on request. AI features (agents, MCP and data apps) are usage based.

    Source: Lightdash pricing, read on

  • The MCP server is available to all Lightdash Cloud users, works with Claude, ChatGPT, OpenAI Codex, Cursor and VS Code, and can read, create and edit charts and dashboards. Self hosted use needs an Enterprise licence.

    Source: Lightdash docs: MCP, read on

  • Embedding works through an iframe with a signed URL or a React SDK, and requires the embedding add on for Cloud Pro or Enterprise.

    Source: Lightdash docs: embedding, read on

  • Dashboards and charts can be sent on a schedule by email or Slack, chart results can sync to a Google Sheet, and an alert notifies you when the values of a saved chart meet a condition you set.

    Source: Lightdash docs: scheduled deliveries, read on

  • Charts, dashboards, spaces and agents can be downloaded as code, edited and uploaded back with the Lightdash command line tool.

    Source: Lightdash docs: content as code, read on

  • The repository is MIT licensed except for its enterprise directory, which has its own source available licence. On a self hosted install, AI agents, MCP and embedding are among the features that need a licence key.

    Source: Lightdash docs: enterprise features when self hosting, read on

Questions

Is Verity a Lightdash alternative?

Only for one kind of team: an ecommerce or marketing team that wants dashboards and KPIs without building pipelines, data models and a semantic layer first. If you have a data team that works in dbt and wants open source BI as code on its own warehouse, Lightdash is the better fit and Verity does not try to replace it.

Does Lightdash need dbt?

Not any more. According to the Lightdash documentation (checked 13 November 2026), the semantic layer lives either in a dbt project or in Lightdash YAML files that point at tables already in your warehouse. Either way your team writes the definitions. In Verity the definitions are proposed from your data and you approve them.

Does Lightdash load data from Shopify or ad platforms?

We found no such connectors in its public documentation (checked 13 November 2026). Lightdash connects to a data warehouse, so the data has to be loaded there by another tool first. Verity loads Shopify, WooCommerce, GA4, Meta Ads, Google Ads, Microsoft Ads, Klaviyo, Brevo and ActiveCampaign into your own BigQuery as part of the product.

Do both have an MCP server?

Yes. According to the Lightdash documentation (checked 13 November 2026), its MCP server is available to all Lightdash Cloud users, charged by usage, and can create and edit charts and dashboards. Verity's MCP server has 100+ tools that also cover data sources, data models, segments and activation, and a dashboard an assistant builds starts as a draft that a person reviews.

Can I use Verity and Lightdash together, or move from one to the other?

Yes. Verity loads and models your marketing and ecommerce data in a BigQuery project you own, and Lightdash connects to BigQuery. A data team can point Lightdash at those tables for company wide reporting while the marketing team works in Verity. A dashboard built in Verity can also be exported as code in Lightdash format. The file holds the structure, the definitions and the queries, never data, and it is a head start that your team reviews in Lightdash, not a one click migration. Verity does not import Lightdash dashboards.

Read next

Dashboards and BI in VerityData models and the semantic layerAI analyticsVerity over MCPDocumentation: dashboard toolsPricing

Also compare

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Sources

Lightdash homepageLightdash pricingLightdash docs: set up LightdashLightdash docs: connect a projectLightdash docs: Lightdash YAMLLightdash docs: metrics catalogLightdash docs: alertsLightdash docs: scheduled deliveriesLightdash docs: AI agentsLightdash docs: MCPLightdash docs: embeddingLightdash docs: content as codeLightdash docs: self hostingLightdash docs: enterprise features when self hostingLightdash licence on GitHub

* Information about Lightdash is based on Lightdash’s public website and documentation as we read them on 13 November 2026. Products, features and prices change, and we may have missed or misread something. Check Lightdash’s own website for current details. Lightdash is a trademark of its owner. Verity is not affiliated with or endorsed by Lightdash. Spotted something that is not right? Email [email protected] and we will correct it.

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