Choose Airflow when you need the most widely deployed orchestrator, a managed service from every major cloud, and operators for almost any system your pipelines touch. Choose Dagster when your platform is mostly data assets (tables, files, models) and you want lineage, local testing and data quality checks built into how pipelines are defined. Airflow 3 closed part of the gap with first-class assets and DAG versioning, so for many teams the deciding factors are existing code, managed hosting and how your engineers like to work.
Airflow started from tasks. You write a DAG, each node runs something (a SQL statement, a Spark submit, an API call, a Kubernetes pod) and the scheduler decides when each node can run. The data those tasks produce was historically invisible to Airflow. That model is flexible: Airflow can orchestrate infrastructure jobs, ML training, report emails and file transfers just as easily as table builds.
Dagster started from the output. You declare an asset, say a daily_orders table, write the function that produces it, and name the upstream assets it depends on. Dagster derives the graph from those dependencies, tracks when each asset was last materialized, and can tell you which downstream tables are stale. The question shifts from "did the task succeed" to "is this table fresh and correct", which is the question analysts actually ask.
Airflow has moved toward that model. The Airflow 3.0 announcement describes datasets evolving into data assets with decorator-based definitions, event-driven scheduling that reacts to asset updates and external events such as Amazon SQS messages, and DAG versioning so a run finishes on the version of the DAG it started with. The difference that remains is emphasis: in Airflow, assets sit on top of tasks; in Dagster, tasks sit underneath assets.
Per the Airflow announcements page, 3.0.0 shipped on April 22, 2025, followed by 3.1.0 in September 2025, 3.2.0 on April 7, 2026 and 3.3.0 on July 6, 2026. The 3.x line matters for this comparison because it fixed several of the complaints that pushed teams toward Dagster:
Upgrading from 2.x is a real project, not a version bump. Plan for import path changes, removed direct database access from task code and provider upgrades.
Neither orchestrator should be doing heavy compute. Both hand work to warehouses, Spark clusters or containers, so pipeline speed is mostly about the engines they call. Where they differ is scheduling overhead. Airflow at large scale (thousands of DAGs) needs attention to scheduler replicas, DAG parsing time, pool and concurrency settings and the metadata database. Teams that keep top-level DAG code light and avoid dynamic DAG generation from slow API calls rarely hit limits. Dagster's daemon and run launchers scale well for asset-heavy estates, and its per-run isolation (each run as its own process or pod) trades a little startup latency for clean failure boundaries.
Airflow gives you retries, SLAs, callbacks, sensors with deferrable mode and task-level reruns, all battle tested across thousands of companies. Dagster's equivalent is re-executing from failure and, more usefully, materializing only the stale or failed assets in a selection. When a broken upstream table is fixed, Dagster's asset graph makes it obvious which downstream tables to rebuild, while in Airflow you reason about it through task dependencies or asset events you configured.
This is Dagster's clearest edge. Assets and resources are plain Python with dependency injection, so a unit test can call an asset with a fake warehouse resource and check the output. Airflow tasks can be tested too, and the Task SDK helps, but most Airflow shops still lean on a staging environment for confidence. If your pipelines break in production because nobody ran them locally, that difference is worth real money.
Airflow's provider packages are its moat. Whatever system you need to call, there is very likely a maintained hook and operator for it, plus years of Stack Overflow answers and blog posts. Every major cloud runs a managed version, so procurement and security reviews are easy.
Dagster integrates with fewer tools but goes deeper with the ones that matter for analytics. Its dbt integration turns each dbt model into an asset with lineage, so dbt and Python assets share one graph. Snowflake, Databricks, BigQuery, Fivetran, Airbyte, Sling, Spark and Kubernetes integrations follow the same pattern. Teams running a modern ELT stack often find Dagster's catalog-like UI replaces a separate lineage tool for internal use.
For ML work, both can run training and batch inference jobs. Dagster's assets map well to feature tables, training sets and model artifacts. Airflow is common as the scheduler around MLflow, SageMaker or Vertex jobs. If you are building feature pipelines for AI systems, see our MLOps services and machine learning development pages.
Both cores are free open source software. The bill comes from hosting, managed service fees and engineering time.
Credit-based pricing rewards coarse assets and punishes thousands of tiny partitions materialized hourly, so model your partition counts before you sign. On MWAA, the common leak is an oversized environment left at peak worker counts all day.
Airflow experience is far more common. Most data engineers who have worked at a mid-size or larger company have written Airflow DAGs, which makes Airflow the safer choice if you expect turnover or need to staff up quickly. Dagster experience is rarer but growing, and engineers who know it tend to be strong Python developers who care about testing and software design, which is a useful signal in itself.
When you screen for either, look past syntax. Ask how the candidate would handle late-arriving data, idempotent reruns, partition backfills, secrets, and alerting that wakes the right person. Our data engineers are screened on those production habits, and you can compare current pay on our data engineer rates page.
Our data teams have built and run both orchestrators in production, from Airflow estates on MWAA to Dagster deployments driving dbt and Python assets. Only the top 1% of the tens of thousands of engineers we interview make it, they work on your team on US business hours, and most ship code in their first week. Bring in individual Python developers to clear an orchestration backlog, or a dedicated squad through our data engineering services to run a migration or an Airflow 3 upgrade. If those pipelines feed LLM or ML systems, our AI pod teams build the downstream system in your cloud.
Mid-level engineers run $7,000 to $10,000 per month, seniors $10,000 to $15,000, and leads are quoted per team; you see named engineers and a scoped plan before work starts. If you only want a vendor recommendation and nobody to build, a consulting firm is a better fit than us.
Dagster is better for teams whose pipelines are mostly data assets and who want lineage, testing and data quality checks built in. Airflow is better for mixed workloads, very large existing estates and teams that want a managed service from their cloud provider. Neither wins in general; the shape of your work decides.
Airflow 3 turned datasets into data assets with decorator-based definitions and lets DAGs run when assets update or when external events arrive. The underlying model is still task-based, so Dagster's stale-asset tracking, asset checks and asset-first UI go further.
Dagster lists a Solo plan at $10 a month and a Starter plan at $100 a month for up to three users, both billed per credit, where each asset materialization or op execution is one credit. Serverless compute is billed per minute, Hybrid runs on your own infrastructure, and Pro pricing comes from sales.
Dagster can observe and trigger Airflow DAGs, which lets you migrate gradually and see Airflow-produced tables in Dagster's asset graph. Most teams still rewrite pipelines as assets over time rather than keeping both systems forever.
Airflow, by a wide margin, because it has been the default orchestrator for most data teams for years. Dagster skills are less common, but strong Python engineers usually pick up Dagster quickly because its concepts map to ordinary functions, dependencies and tests.
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