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Business Intelligence · head to head

Lightdash vs Seldon

Lightdash logo

Lightdash

Business Intelligence

Open-source BI for dbt users

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

Kubernetes model serving whose current version is licensed under the Business Source Licence

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Lightdash requires existing dbt infrastructure, not suitable for teams without data models; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • They diverge on capability: Lightdash covers dbt Integration, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Lightdash and Seldon actually diverge.

Attributes where Lightdash and Seldon differ
AttributeLightdashSeldon
Pricing modelUnknownfreemium
PlatformsWeb, Cloud (managed), Self-hosted (on-premise)Linux
CategoryBusiness IntelligenceMachine Learning
Founded20212014

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Lightdash

  • dbt Integration
  • Metrics Layer
  • Dashboards
  • Scheduling
  • Version Control
  • dbt
  • BigQuery
  • Snowflake

Only in Seldon

  • Kubernetes custom resources
  • Inference graphs
  • Traffic strategies
  • Open Inference Protocol
  • Alibi Explain
  • Alibi Detect
  • Kafka-backed pipelines in v2
  • Commercial control plane

What people use each for

The jobs each tool is most often brought in to do.

Lightdash

  • Self-service analyticsnot Seldon
  • Data explorationnot Seldon
  • Ad-hoc reportingnot Seldon
  • Collaborative analysisnot Seldon
  • Embedded analyticsnot Seldon

Seldon

  • Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Lightdash
  • Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Lightdash
  • Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Lightdash
  • Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot Lightdash

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Lightdash

  • Requires existing dbt infrastructure, not suitable for teams without data models
  • Enterprise features and AI agents unavailable in open-source MIT-licensed core

Seldon

  • Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
  • Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
  • Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
  • Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.

Pricing, plan by plan

Lightdash

Free
  • Open SourceFree
    • MIT-licensed core
    • Self-hostable
    • dbt integration
  • Cloud Managed$undefined/mo
    • Managed hosting
    • Premium features
    • AI agent capabilities

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

Which should you pick?

Choose Lightdash if

  • You need dbt integration.
  • You want to start without paying.
  • You work on Web, Cloud (managed), Self-hosted (on-premise).
  • You also want metrics layer.
Lightdash review

Choose Seldon if

  • You need kubernetes custom resources.
  • You want to start without paying.
  • You work on Linux.
  • You also want inference graphs.
Seldon review

Questions people ask

Is Lightdash or Seldon better?
Neither clearly leads. Lightdash starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Lightdash or Seldon?
Lightdash starts at Free and Seldon at Free.
Does Lightdash or Seldon run on more platforms?
Lightdash runs on Web, Cloud (managed), Self-hosted (on-premise). Seldon runs on Linux.
Can I use Lightdash for free?
Both have a free tier, so you can try either at no cost before committing.
What is Lightdash best used for?
Lightdash is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what Seldon is typically brought in for.
What can Lightdash do that Seldon cannot?
Lightdash covers dbt Integration, Metrics Layer, Dashboards, Scheduling. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Lightdash: Is Lightdash free?

Yes. Lightdash is free and open source under the MIT license. Self-hosting is completely free. Managed cloud services and enterprise features require separate licensing.

Source
Seldon: Is Seldon open source?

Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.

Lightdash: How does Lightdash integrate with dbt?

Lightdash reads dbt models and metric definitions directly. A team defines metrics once in dbt and reuses them across dashboards, exploration, and AI agents without redefinition.

Source
Seldon: What is the difference between v1 and v2?

Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.

Lightdash: Does Lightdash support SQL queries?

Yes. As a modern BI platform for analysts, Lightdash supports full SQL capabilities alongside dbt model exploration and visual query builders.

Source
Seldon: Do I need Kubernetes?

Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.

Lightdash: What are Lightdash AI agents?

Lightdash AI agents, available on paid plans, allow natural language queries against your data, generating SQL and visualizations automatically from questions.

Source
Seldon: What is MLServer?

Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.

Lightdash: Can Lightdash be self-hosted?

Yes. Lightdash's MIT-licensed core is completely self-hostable and free. Enterprise features and AI agents ship under separate licensing.

Source
Seldon: Do I have to run Kafka?

For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.

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