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Automation Integration · head to head

Dagster vs Relevance AI

Dagster logo

Dagster

Automation Integration

Data orchestration platform with asset lineage and AI-native observability

From
Free
Rated
-
Full reviewWebsite
Relevance AI logo

Relevance AI

Automation Integration

Specialist AI Agents for Every Task

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Dagster has a free tier, so it costs nothing to try first.
  • Each has a real cost: Dagster learning curve steeper than simpler task schedulers; Relevance AI no published standard pricing tiers or plans
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Dagster and Relevance AI actually diverge.

Attributes where Dagster and Relevance AI differ
AttributeDagsterRelevance AI
Starting priceFreeOn request
Pricing modelFree open-source core plus paid cloud platformusage-based
Free tierYesNo
PlatformsCloud, Self-hostedWeb

Identical on both: user rating (Not yet rated), category (Automation Integration).

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 Dagster

  • Asset-centric orchestration
  • Lineage tracking
  • Data quality monitoring
  • Multi-tool integration
  • Branch deployments
  • Dagster+ AI
  • Hybrid deployment

Only in Relevance AI

Nothing recorded that Dagster does not also cover.

What people use each for

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

Dagster

  • Orchestrating multi-stage data transformation pipelinesnot Relevance AI
  • Tracking data lineage across transformation toolsnot Relevance AI
  • Monitoring data quality and asset healthnot Relevance AI
  • Coordinating dbt and Snowflake workflowsnot Relevance AI

Relevance AI

  • AI agent platform for automating business processes with LLMsnot Dagster
  • Low-code automation and workflow orchestration using language modelsnot Dagster

Where each one falls short

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

Dagster

  • Learning curve steeper than simpler task schedulers
  • Asset-centric model requires redesigning existing task-based pipelines
  • Pricing complex with per-credit costs on top of base fee
  • Open-source version requires self-hosting infrastructure
  • Limited to data workflows; not suitable for general automation

Relevance AI

  • No published standard pricing tiers or plans
  • Pay-per-task model pricing varies significantly by AI model selection
  • Enterprise quotes require direct contact with sales
  • Task costs range from 0.01 to 0.11 USD depending on model used

Pricing, plan by plan

Dagster

Free
  • Open SourceFree
    • Self-hosted deployment
    • Full orchestration engine
    • Community support
  • Solo$10/month
    • 1 user and 1 code location
    • Pay-as-you-go credits at $0.040 per credit
    • 1 deployment
  • Starter$100/month
    • Up to 3 users and 5 code locations
    • Better credit rate at $0.035 per credit
    • 1 deployment
  • Pro$null/custom
    • Unlimited code locations and deployments
    • Custom serverless compute pricing
    • Personalized onboarding

Relevance AI

On request

No published plan breakdown. See the Relevance AI review.

Which should you pick?

Choose Dagster if

  • You need asset-centric orchestration.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want lineage tracking.
Dagster review

Choose Relevance AI if

Nothing in the data separates Relevance AI from Dagster on the points above - pick on price and on how each one feels to use.

Relevance AI review

Questions people ask

Is Dagster or Relevance AI better?
Neither clearly leads. Dagster starts at Free and Relevance AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dagster or Relevance AI?
Dagster has a free tier; the other does not. Paid plans start at Free for Dagster and On request for Relevance AI.
Does Dagster or Relevance AI run on more platforms?
Dagster runs on Cloud, Self-hosted. Relevance AI runs on Web.
Can I use Dagster for free?
Yes. Dagster has a free tier, so you can try it without paying. Relevance AI starts at On request.
What is Dagster best used for?
Dagster is most often used for orchestrating multi-stage data transformation pipelines, tracking data lineage across transformation tools, monitoring data quality and asset health, coordinating dbt and snowflake workflows. Of those, orchestrating multi-stage data transformation pipelines and tracking data lineage across transformation tools are not what Relevance AI is typically brought in for.
What can Dagster do that Relevance AI cannot?
Dagster covers Asset-centric orchestration, Lineage tracking, Data quality monitoring, Multi-tool integration.

Answered from the vendors’ own pages

Dagster: What does Solo plan at $10/month include?

Solo includes 1 user, 1 code location, 1 deployment, and serverless compute at $0.010/minute, with 30-day free trial.

Source
Relevance AI: What is Relevance AI's pricing model?

Relevance AI uses a pay-per-task model where costs vary by AI model selected. Average cost per task is approximately 0.09 USD, with actual costs ranging from 0.01 to 0.11 USD depending on which model the system routes to. The platform automatically selects the lowest-cost model that meets performance requirements.

Source
Dagster: How are credits calculated on Dagster+?

Each asset materialization and ops execution costs 1 credit. Solo plan charges $0.040 per credit; Starter reduces to $0.035 per credit.

Source
Relevance AI: How much do Relevance AI tasks cost?

Task costs depend on model selection. Example usage shows customers spending 11800 USD monthly while processing 1.24 million tasks. For enterprise custom pricing, users must book a demo or contact the sales team.

Source
Dagster: Is Dagster open-source available for free?

Yes, Dagster open-source is free and includes the full orchestration engine for self-hosted deployment with community support.

Source
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