Orchestrate data workflows with observability-first automation
Dagster is an open-source orchestration platform for building, scheduling, and monitoring data pipelines and application workflows. It targets data engineers and ML engineers who need typed, testable pipelines and runtime observability, and offers both a free open-source core and paid cloud/enterprise hosting options. Dagster’s model-based approach and execution APIs make it best for teams that want reproducible, debuggable data apps with clear pricing choices from free self-hosting to paid managed deployments.
Dagster is a workflow orchestration platform that defines, runs, and observes data and ML pipelines. It provides a typed graph-based programming model (solids/ops and graphs/jobs) and strong developer ergonomics for building testable data applications. Dagster’s key differentiator is its type system and asset-aware abstractions that connect code, assets, and schedules into a unified view, aimed at data engineers, ML engineers, and analytics teams. The platform is available as open-source software plus paid Dagster Cloud and enterprise options, making the automation & workflow tool accessible from free self-hosting to managed pricing.
Dagster is an open-source automation and workflow orchestration system created to make data pipelines and ML workflows testable, observable, and maintainable. Launched by Elementl (the company behind Dagster) it positioned itself as a developer-first alternative to classic orchestrators by introducing typed I/O, assets, and composite job primitives. The core value proposition is reproducible, asset-aware pipelines: code, schedules, sensors, and execution logs are first-class, enabling teams to understand lineage and state.
Dagster runs locally, on Kubernetes, or via Dagster Cloud for managed hosting and integrates with CI/CD and observability stacks. Dagster’s feature set centers on a few concrete capabilities. Its typed solids/ops and graphs/jobs provide explicit input/output type checks and I/O managers so you can validate data contracts at runtime.
The Assets API models datasets as first-class assets with materialization events, lineage, and backfills; this enables incremental materialization and asset-aware scheduling. The Dagit web UI provides real-time execution logs, step-level views, and run re-execution (retries and subset runs) so you can inspect and re-run failed steps. Dagster supports sensors and schedules for event-driven and time-based triggers, integrates with popular executors (Kubernetes, Celery), and includes built-in integrations for sources and sinks like Snowflake, BigQuery, S3, and dbt.
Pricing for Dagster starts with the open-source Dagster core which is Free to use and self-host; there are no enforced runtime limits beyond what you host. Dagster Cloud (managed) is a paid service; current public pricing lists a Starter tier and Team/Enterprise plans with per-seat and per-ingestion charges—Elementl publishes Cloud pricing on request for larger teams and offers a free trial and free developer tier features in Cloud for limited usage. Enterprise options include SSO, dedicated support, and fine-grained RBAC.
For accurate, up-to-date numbers consult dagster.io/pricing or request a quote; the open-source core remains free for local or self-hosted production use. Teams using Dagster include data engineers building repeatable ETL and feature pipelines and ML engineers operationalizing training and feature materialization. For example, a Data Engineer at a fintech firm might use Dagster to reduce failed nightly ETL runs by 40% through typed ops and targeted re-executions.
An ML Engineer at an e-commerce company could use the Assets API to incrementally materialize and validate features for model retraining. Compared to Apache Airflow, Dagster emphasizes typed data contracts, asset lineage, and a developer UX centered on testing and local iteration, making it better suited for asset-centric data applications.
Three capabilities that set Dagster apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Open Source | Free | Self-hosted; no platform-enforced quotas, community support only | Individual devs and self-hosted teams |
| Dagster Cloud Developer | Free | Limited managed resources for development, trial features, non-production use | Developers evaluating managed features |
| Dagster Cloud Starter | $95/month | Hosted control plane, up to defined compute/connect quotas; paid add-ons | Small teams wanting managed control plane |
| Dagster Cloud Team / Enterprise | Custom | Unlimited users via quote; SSO, enterprise support, SLA options | Large organizations needing SLAs and support |
Choose Dagster over Apache Airflow if you prioritize asset-aware lineage, runtime type checks, and developer-first testing workflows.
Head-to-head comparisons between Dagster and top alternatives: