Dagster is an asset-centric data orchestration platform — open source at the core, with Dagster+ as the managed cloud — that tracks lineage, freshness and quality for every dataset your team and your AI agents rely on.
Traditional schedulers like Airflow are task-centric: they know whether a job ran, not what it produced. Dagster inverts the model. You define the data assets your pipelines create — tables, models, files, ML artifacts — and Dagster derives the execution graph from their dependencies. That single design decision unlocks automatic lineage, asset health dashboards, freshness policies and a precise blast radius when something breaks: instead of ‘job 47 failed,’ you learn which datasets are stale, why, and who downstream is affected.
The platform is code-native and built for local development, testing and branch deployments that validate changes in production-like environments before they touch real data. First-class integrations orchestrate dbt models, Snowflake assets and Fivetran syncs without glue code, and hybrid deployment lets compute run in your own cloud or on-prem while Dagster manages the control plane. The newest layer, Dagster+ AI, reasons over the context Dagster already holds — assets, runs, lineage, failures, automation history — to help teams diagnose incidents and act faster, and the same governed context is what makes Dagster a credible foundation for AI agents consuming enterprise data.
The Dagster orchestration framework is open source and free to self-host. Dagster+, the managed cloud, offers a free tier to get started, with paid plans that add branch deployments, hybrid deployment, role-based access control, cost insights and built-in observability; enterprise agreements are custom. Pricing scales with users and usage rather than per-pipeline fees.
Data platform teams outgrowing cron-and-pray scheduling who need lineage and reliability guarantees, especially dbt and Snowflake shops. Tiny teams with two or three jobs may find the asset modeling overhead more than they need today.