Is there an open-source alternative to Looker?

Lightdash is an open-source alternative to Looker. Its core is MIT-licensed on GitHub and can be self-hosted for free, with Lightdash Cloud and Enterprise available as managed options. Like Looker, it is built around a code-based semantic layer rather than drag-and-drop data prep, so LookML concepts such as dimensions, measures and joins map across directly.

Does Lightdash require dbt?

No. You can build the Lightdash semantic layer from a dbt project or define it in standalone Lightdash YAML against your warehouse tables. dbt remains a common path, but it is not required.

Does Looker integrate with dbt?

Not natively. Looker can query tables that dbt builds, but it has no built-in way to use dbt models or metrics as its semantic layer, so teams that use both usually maintain business logic in dbt and again in LookML. Community tools can generate LookML from dbt, but the two definitions still need to be kept in sync.

Can I migrate LookML to Lightdash?

Yes. Lightdash's analytics engineers run our Looker migration script for you. It converts your LookML views, dimensions, measures, explores and joins into dbt models or Lightdash YAML, rebuilds your saved Looks and dashboards as code, and reports anything that didn't convert. Automated migration is included in Cloud Pro.

How long does a Looker to Lightdash migration take?

It depends on how much of your Looker estate is still in use. Converting the model is fast, but most of the time goes on validating numbers, retiring unused dashboards, rebuilding persistent derived tables in your warehouse, and training people. Start with one high-traffic Explore, and begin well before your Looker renewal so both tools can run side by side.

What is the closest replacement for LookML?

A code-based semantic layer defined in YAML. In Lightdash, LookML dimensions and measures become dimensions and metrics in dbt YAML or Lightdash YAML, explores become models with joins, and access filters become sql_filter rules driven by user attributes.

Can I use my own AI model with Lightdash?

Yes. Lightdash agents run on Anthropic, OpenAI, Azure OpenAI, AWS Bedrock or OpenRouter models. Admins can bring their own provider keys and decide which models are available, each agent can have a default, and users can switch model per chat. Looker’s Conversational Analytics is powered by Gemini for Google Cloud.

How often does Lightdash ship compared to Looker?

Every day. Lightdash is open source, and its GitHub releases page shows 312 feature releases in the 30 days to 22 September 2026. Looker publishes a numbered release roughly once a month, and bugs and feature requests are tracked through Google’s Issue Tracker.

Is Data Studio (formerly Looker Studio) the same as Looker?

No. Data Studio (called Looker Studio until April 2026) is Google's free self-service reporting tool, with a Pro tier at $9 per user per project per month. It has no LookML semantic layer. Looker is Google's enterprise platform built on LookML. Teams moving off either can use Lightdash, but the migration paths are different.

Lightdash | Build Intelligence, not just dashboards.

Q: How much does Looker cost compared to Lightdash?

Looker is priced per seat: a platform edition plus a Developer, Standard or Viewer licence for every user, quoted by Google on a 1 to 3-year term. Its AWS Marketplace listing shows the Standard platform at $60,000 a year, and third-party guides report licences from $400 to $1,665 per user per year. Lightdash is a flat fee for unlimited users: Cloud Pro from $3,000 a month, Enterprise custom, and the open-source version is free.

How do Lightdash and Looker compare at a glance?

DecisionLightdashLooker

What it isOpen-source agentic data platform. AI agents build and maintain governed dashboards on a code-based semantic layer.Google Cloud’s enterprise BI platform, built on the LookML modeling language.

Semantic layerMetric definitions in dbt or standalone Lightdash YAML. It’s self-improving: Lightdash learns from real questions and proposes reviewable updates as your business changes.Metric definitions in LookML.

AI and MCPAI agents and MCP work across the Lightdash semantic layer to create and maintain governed analytics from developer tools. Run them on Claude, GPT, or whichever frontier model you choose.Gemini conversational analytics plus a managed MCP server in preview for hosted Looker instances. Gemini only.

Pace of changeShips every day. 312 feature releases in the 30 days to 22 Sep 2026, all public on GitHub.A numbered release roughly once a month. Bugs and requests go through Google’s Issue Tracker.

Data AppsBuild and deploy custom, governed data apps from a prompt. Every query runs through your semantic layer, with permissions and row-level security inherited automatically.Build custom applications using Looker’s APIs, SDKs, embedded analytics, and Extension Framework; typically a developer-led build.

DeploymentOpen-source self-hosting, Lightdash Cloud, or Enterprise cloud and on-premises deployments.Google-hosted Looker core, with customer-hosted deployments available through Looker original.

PricingA flat fee for unlimited users, whatever your headcount. Cloud Pro from $3,000 a month; Enterprise is a custom flat fee.Per seat. A platform edition plus a Developer, Standard or Viewer licence for every user beyond the 12 included, quoted on 1 to 3-year terms.

MigrationAutomated migration tools and hands-on support help translate LookML assets, followed by validation and rollout.Existing LookML models and workflows stay in place if you remain on Looker.

Accessible viaThe UI, your own Git repo, from any IDE, the Lightdash CLI or a coding agent.UI, Looker’s Git-backed IDE, plus a VS Code extension.

Best fit forModern data teams that want an open, code-based semantic layer and developer workflows.Organizations with a mature LookML estate, or where a Google Cloud / Looker commercial bundle outweighs switching cost.

Why do teams look for a Looker alternative?

Looker is sold on annual contracts, often multi-year, and every user beyond the 12 included needs a Developer, Standard or Viewer licence. A Developer seat costs about 4x a Viewer, so the people who build the most are the ones you pay the most for. As access spreads beyond the data team, the bill climbs with it.

Where does the metric logic live in Lightdash vs Looker?

Both products use governed semantic models. The difference is where those definitions live and what your team has to maintain.

dbt or Lightdash YAML

Lightdash keeps semantic definitions in dbt or standalone Lightdash YAML. Explorers, dashboards, AI agents, and developer tools all use that same governed layer. For modern data teams, this removes the need to maintain metric meaning in a separate analytics language.

Lightdash keeps metric definitions as YAML in your dbt project (or in standalone Lightdash YAML against warehouse tables), not as a separate LookML model, and not dependent on dbt’s Semantic Layer. Explorers, dashboards, AI agents, and developer tools all use that same governed layer.

How do Lightdash and Looker compare feature by feature?

Lightdash25 of 29 fully supported

Looker16 of 29 fully supported

FullPartialBetaNot availableAcross all 29 capabilities below.

Only show differences

Semantic layer and modeling

CapabilityLightdashLookerNotes

Code-based semantic layerFullFullLightdash: dbt or Lightdash YAML. Looker: LookML.

dbt as the source of truth for metricsFullNot availableLooker queries dbt-built tables but doesn’t use dbt definitions as its model.

Semantic layer without dbtFullFullLightdash YAML vs LookML.

Row-level securityFullFullLightdash: sql_filter + user attributes. Looker: access filters.

Templated SQL and parametersPartialFullBoth support Liquid-style templating. Lightdash also has parameters; Looker’s Liquid usage is more mature and covers more edge cases today.

Pre-aggregates and cachingFullFullLightdash pre-aggregates are available for Enterprise plans; results caching is available. Looker has aggregate awareness.

How much does Looker cost compared to Lightdash?

Looker charges per seat. Lightdash charges a flat fee. Move the sliders and watch which number moves.

How many people need access, and what will they do?150 users in total

Developers $1,665 each a year5Standard users $799 each a year45Viewers $400 each a year100

Looker, per seatAbout $204,960 a yearEvery seat you add moves this number.

Enterprise platform (over 50 users)$132,000
3 Developer users beyond the 2 included$4,995
35 Standard users beyond the 10 included$27,965
100 Viewer users$40,000

Estimate. Platform price from Looker’s AWS Marketplace listing (22 Sep 2026); per-user prices as reported by Holistics and Toucan Toco.

Lightdash, flat feeOne flat feeCloud Pro from $3,000 a month. Enterprise is a custom flat fee. Unlimited users either way, so the price is the same at 12 users or 1,200.Same price at 150 users. Same price at 1,500.

Lightdash is one flat fee for unlimited users: Cloud Pro from $3,000 a month, Enterprise a custom flat fee. Nobody needs a licence to look at, explore or build with data, so rolling out to the whole company doesn’t change the bill.

Looker is quoted by Google sales on 1 to 3-year terms: a platform edition, plus a licence for every user beyond the 10 Standard and 2 Developer users included, priced by what each person is allowed to do. Google publishes no list prices, so the estimate uses the Standard platform price on Looker’s AWS Marketplace listing (22 September 2026) and the per-user prices reported by Holistics and Toucan Toco. Your quote will differ.

Do Lightdash and Looker both have AI agents and MCP?

Yes. Both Lightdash and Looker now support AI agents and MCP workflows.

Lightdash

Lightdash focuses on building and maintaining governed analytics from coding tools such as Cursor and Claude Code. Charts and dashboards can move through code review and deployment workflows alongside the rest of your analytics project. The model is your call: run agents on Claude, GPT, or any frontier model, with a default per agent and a picker in every chat, and switch the moment a better one ships.

Looker

Looker offers Gemini-powered conversational analytics and a managed MCP server in preview for hosted instances. It lets tools including Cursor and Claude Desktop connect to Looker and work with LookML-governed data. Google is also adding agentic workflows that can create reports, visualizations, and dashboards. Conversational Analytics is powered by Gemini for Google Cloud, so the model choice is made for you.

It’s important to highlight that AI features change monthly on both sides. The best way to decide which AI capability suits your organization more is to give both products the same five questions and the same dashboard change on your own metrics, then compare accuracy, speed and how the change gets reviewed.

How hard is it to migrate from Looker to Lightdash?

Not hard at all. Lightdash’s migration guide starts from your cloned LookML project. A deterministic LookML translator converts about 90% of the model automatically into dbt or Lightdash YAML; an AI coding agent with Lightdash skills and our team help close the rest. Automated migration and hands-on support are included on Cloud Pro.

Moves across well

Views, dimensions and measures become dimensions and metrics
Explores and joins become models with joins
Dashboards and tiles, via the migration tooling
Access filters, mapped to sql_filter and user attributes

Needs rework

Liquid templating (partly covered by Lightdash parameters)
Persistent derived tables, especially incremental ones, which you rebuild in dbt or your warehouse
Merged results and some Looker-specific measure types
User habits, bookmarks and training

What a Looker to Lightdash migration plan looks like

Our analytics engineers run our bespoke Looker migration script to help you migrate from Looker to Lightdash. Then, we’ll work through whatever's left with your team until the numbers match.

Share your LookML project through Git or as a zip, along with your dashboards exported as LookML. If you already have a dbt project, send that too.

Which teams have moved from Looker to Lightdash?

Gen H

Fintech · London, UK

100%

of dashboards built with natural language

70%

drop in dependency on the data team

95%

decrease in time-to-insight

more metrics shipped to dbt

Gen H moved from Looker to Lightdash and rebuilt their data models in dbt. Their business users now build dashboards by asking in plain English. Their Chief Customer Officer shipped production metrics in two days without pinging the data team.

“We started shaping executive-level decisions based on metrics that didn't exist two weeks prior.”
Hal Sarjant, Chief Customer Officer, Gen H

Read the Gen H story →

Ollie

Pet wellness · Nevada, USA

“It was clear that we weren't able to iterate as quickly as we wanted, and the self-service was confusing.”
Ben Pannell, Tech Lead & Data Engineer, Ollie

Ollie left Looker when overlapping Explores and duplicate metric definitions made its LookML hard to iterate on. Our team translated Ollie's LookML into starter YAML, which covered about 80% of the initial migration before the deeper cleanup began. Today almost everyone at Ollie has access to Lightdash. Autopilot, Lightdash's scheduled maintenance agent, now handles 95% of dashboard maintenance automatically.

Read the Ollie story →

Stake

Financial services · Dubai, UAE

“With Looker pricing, we'd be paying a lot for self-serve because it goes up with how many users we have.”
Abdelmounim Boufous, Senior Data Analyst, Stake

Stake moved from Looker Studio, not Looker, and looked at Looker's pricing model on the way. 90% of employees now use Lightdash, and ad-hoc data requests are down 80%.

Read the Stake story →

Frequently asked questions

Yes, for teams that want a governed semantic layer without maintaining LookML. Lightdash reads metric definitions from dbt or Lightdash YAML, offers a Looker-style Explore for business users, and adds AI capabilities and an MCP server on top. However, it is not a like-for-like clone: Looker is still ahead on persistent derived tables and Liquid templating.

Lightdash vs Looker

How do Lightdash and Looker compare at a glance?

Why do teams look for a Looker alternative?

Where does the metric logic live in Lightdash vs Looker?