Apache Airflow vs Dagster: when each one fits a data platform team
Apache Airflow fits when the estate is large and already working. If your organization runs many scheduled jobs, has engineers fluent in task-based Python DAGs, and treats orchestration as a reliable scheduler for batch work, Airflow remains the lower-risk choice. Dagster fits a different situation: a team that thinks in tables and models, wants lineage and asset health as first-class views, and is willing to rebuild pipelines around assets rather than port tasks one for one. Your decision turns on three questions: how old the current Airflow estate is, whether you need asset-level features in open-source Dagster or through Dagster+, and what your team already knows well.
The age of the estate matters more than it first appears. Airflow 2 reached end of life on April 22, 2026, according to the Apache Airflow supported versions page, and Airflow 3.3.2 is the current release. Inventory every DAG, operator, custom plugin and connection, then check which ones depend on Airflow 2 behavior. Ask your team to confirm the effort of an upgrade for your own DAGs, since the size of that work depends on your code and cannot be read from a release note. The inventory tells you whether a move to Airflow 3 is a contained upgrade or the first step toward Dagster.
Asset-level features are where the choice becomes a budget question. Dagster's open-source project is Apache-2.0 licensed, but its pricing page lists role-based access control, SAML SSO, catalog search, column lineage, branch deployments, alerts and audit logs as Dagster+ features rather than part of the free open-source tool. If your governance requirements depend on those controls, price Dagster+ into the comparison and ask the vendor which edition provides each one you need in writing. Also assess your team: a rebuild of pipelines around assets is real engineering work, so a team with strong Airflow skills and a stable estate should weigh that cost against the benefits before committing.
Which scenario points to Airflow and which to Dagster
This table maps common situations to the orchestrator that tends to fit them, with the condition that must hold for each choice. Read each row as a starting point, then check the condition against your own estate, team skills and compliance requirements before deciding.
| Scenario | Better fit | Why |
|---|---|---|
| Large Airflow 2 estate that must move soon | Airflow 3 (upgrade path), or Dagster if you accept a rewrite | Airflow 2 reached end of life on April 22, 2026 (per the Apache Airflow supported versions page), so the estate needs a supported line. Airflow 3 keeps DAG concepts familiar, while Dagster means reworking task-based DAGs into assets. Confirm the upgrade effort with a dependency and operator inventory first. |
| Greenfield platform built around dbt-style models | Dagster | Dagster organizes work around assets, logical units such as tables or models, so lineage and freshness map directly onto dbt-style transformations. This holds when most jobs produce data assets rather than run arbitrary scripts. |
| Team with deep Airflow operators and plugins | Airflow | Years of custom operators, sensors and provider packages already encode business logic. Rewriting them for an asset model adds effort with no direct gain. This holds when those plugins are central to daily operations and have no Dagster equivalent you trust. |
| Team needing asset lineage without buying a commercial tier | Dagster (open source), with limits to confirm | Dagster's open-source core is Apache-2.0 and models assets natively, so lineage is part of how jobs are defined. Catalog search and column lineage are listed as Dagster+ features, so a column-level requirement needs a paid tier or a separate catalog such as OpenMetadata or DataHub. |
| Regulated team needing SSO and audit logs | Airflow, or Dagster+ if the commercial tier is approved | Dagster's pricing page lists role-based access control, SAML SSO and audit logs as Dagster+ features rather than open-source Dagster. Teams that must run only open-source components should plan identity and audit controls at the platform layer and confirm them with their assessor. |
| Small team with few pipelines | Either, favoring the team's existing skills | With few DAGs, migration cost and learning curve outweigh architectural gains. Choose the tool your engineers already know unless a lineage or asset requirement is pressing. Revisit the choice when pipeline count or ownership grows. |
| Platform team standardizing many data domains | Dagster | Asset-level dependencies and per-domain code locations help separate ownership across teams. This holds when multiple teams publish shared datasets and need to see upstream impact before a change. |
| Event-driven or ad hoc scripting heavy workloads | Airflow | Task-centric DAGs suit workflows that run commands, call external services and trigger jobs in sequence without producing a persistent data asset. This holds when the output is an action rather than a table or model. |
How task-centric DAGs and asset-centric orchestration differ in daily operation
Airflow and Dagster both schedule Python code, but they ask engineers to think about pipelines in different units and they leave different traces when something breaks. The cards below cover the differences that show up on call and in everyday maintenance, along with the checks to run before committing.
How each defines a pipeline
In Apache Airflow, a DAG of tasks is the unit: you declare operators and the dependencies between them, and the graph describes what runs in what order. In Dagster, the unit is the software-defined asset, a logical piece of data such as a table or model, and dependencies follow from which upstream assets each one reads. For your team, the question is whether engineers naturally think in steps or in datasets. Test this by modeling one of your real lineages in both and checking which version is easier to review.
How runs are scheduled and observed
Airflow centers its UI on DAG runs and task instances, so the first view for an operator is usually a grid of tasks across scheduled runs. Dagster centers its UI on assets and their materialization history, so the operator sees whether each dataset is current. Confirm with a proof of concept how each tool surfaces a late upstream feed, since the on-call response to a stale table differs from the response to a failed task.
What the team reads when a job fails
With Airflow, the usual starting point is the failed task's log and its place in the DAG, which tells you what ran and where it stopped. With Dagster, the failure is attached to an asset, so responders also see which downstream assets are affected. Ask both vendors what a runbook for a failed upstream extract looks like in your environment, and time how long a new on-call engineer takes to find the root cause.
Backfills and partial reruns
Airflow reruns are organized around DAG run dates and task instances, so backfills are expressed as a range of logical dates and clearing selected tasks. Dagster lets you select specific assets to rematerialize, including downstream ones, which tends to suit partial recomputation of a single table. Both approaches need clear rules for idempotent writes and for which outputs are safe to overwrite. Document those rules before the first large backfill, regardless of tool.
Python code footprint
Airflow DAG files are Python that instantiates operators and wires them together, and the surrounding business logic often lives in provider-specific operators. Dagster code is typically decorated Python functions that return or consume assets, with resources and definitions grouped into a code location. Measure the footprint on one representative pipeline: count the lines of orchestration boilerplate, the number of custom operators or wrappers, and the dependencies each project pulls in.
What operations staff must run
Airflow 2 reached end of life on April 22, 2026, and Airflow 3.3.2 is the current release, so an Airflow 2 estate needs a planned upgrade before it is supported. Running Airflow means operating the scheduler, the web server, metadata database and workers under your chosen executor. Dagster open source has its own webserver and daemon components, and the advanced features such as RBAC and SAML SSO are listed as Dagster+ features. Ask each vendor which components you host and who patches them.
What stays in open-source Dagster and what moves to Dagster+
Apache Airflow is licensed under Apache-2.0, so the software itself carries no license fee. The current release line is 3.3.2, while Airflow 2 reached end of life on April 22, 2026, which means estates still running Airflow 2 no longer receive upstream security fixes and must plan an upgrade path. Cost sits in the people and infrastructure that run it, not in the license.
- Licensing: Apache-2.0 covers the core project, so you can run, modify and redistribute it without a per-seat or per-node license from the project.
- Version status: Airflow 2 reached end of life on April 22, 2026. Estates on that line carry unpatched exposure until they move to a supported release.
- Migration work: Moving DAGs and operators from Airflow 2 to the current line is a one-time engineering cost that depends on the number of DAGs, custom operators and provider packages in use.
- Recurring cost: Scheduler, worker, database and upgrade work recur in your own infrastructure budget and staffing.
- Support: Community support is free, while commercial support, if needed, comes from a separate contract.
Confirm with your vendor or service provider which Airflow release line your support terms cover, and check the supported versions page before setting an upgrade date.
Dagster open source is licensed under Apache-2.0, and the current release is 1.13.26, released on October 9, 2026. It organizes orchestration around assets, which are logical units of data such as tables or models, so pipelines are described by what they produce rather than only by the tasks that run. The core orchestration capabilities are part of the free open-source distribution.
- Licensing: Apache-2.0 applies to the open-source project, with no license fee for running it in your own environment.
- Asset model: Pipelines are defined around assets, which makes lineage between tables and models a property of the code.
- Self-hosting: You operate the deployment, including the database, compute, upgrades and access controls you build around it.
- Identity and access: Role-based access control, SAML SSO, catalog search, column lineage, branch deployments, alerts and audit logs are listed on the pricing page as Dagster+ features, not as part of open-source Dagster.
- Recurring cost: Infrastructure, upgrades and the staff who maintain them recur, and they grow with the number of environments you run.
Before committing, list which of the identity, audit and lineage requirements in your control set must be met inside your own deployment, and confirm how the open-source edition satisfies each one.
Dagster+ is the paid offering that adds features the open-source distribution does not include. The pricing page lists role-based access control, SAML SSO, catalog search, column lineage, branch deployments, alerts and audit logs as Dagster+ features. Because these capabilities sit behind a paid plan, the licensing decision and the access control design are linked for teams with audit or identity requirements.
- Paid features: Role-based access control, SAML SSO, catalog search, column lineage, branch deployments, alerts and audit logs are Dagster+ features rather than open-source features.
- Plan cost: Plan prices are not stated on this page. Obtain current pricing from Dagster directly, since plans and their included features can change.
- Recurring cost: Subscription fees recur, while the work to connect Dagster+ to your identity provider and logging systems is typically one-time.
- Cost drivers: The number of users, deployment model and feature tier you select are the items to compare across quotes.
- Alternatives: Where a required feature is not available in the open-source edition, check whether your team can build it, or whether a Dagster+ plan is the lower total cost.
Ask Dagster for a written list of which features each plan includes, the billing unit, and the terms for renewal and change of plan.
Which capabilities are in each platform and which need Dagster+
This table sets the open-source releases of Apache Airflow and Dagster side by side, and marks which Dagster capabilities are listed as Dagster+ features on the pricing page. Read the Airflow column as a list of what the supplied facts confirm, then verify the remaining controls against your own deployment. The main takeaway is that Dagster's governance and lineage features should be costed as a Dagster+ decision, not assumed to come with the free project.
| Capability | Apache Airflow | Dagster |
|---|---|---|
| License | Apache-2.0 | Apache-2.0 for the open-source project |
| Current release | Airflow 3.3.2. Airflow 2 reached end of life on April 22, 2026 | 1.13.26, released October 9, 2026 |
| Orchestration model | Task-centric Python DAGs | Asset-centric, where assets are logical units such as tables or models |
| Lineage and catalog search | Not covered by the supplied facts. Confirm with the vendor or through your own tests | Catalog search is listed as a Dagster+ feature. Confirm what open-source Dagster shows in its asset graph |
| Access control and SAML SSO | Not covered by the supplied facts. Confirm against your chosen deployment | Role-based access control and SAML SSO are listed as Dagster+ features, not part of free open-source Dagster |
| Audit logs and alerts | Not covered by the supplied facts. Confirm against your chosen deployment | Audit logs and alerts are listed as Dagster+ features, not part of free open-source Dagster |
| Branch deployments | Not covered by the supplied facts. Confirm against your chosen deployment | Listed as a Dagster+ feature, not part of free open-source Dagster |
| Column lineage | Not covered by the supplied facts. Confirm against your chosen deployment | Listed as a Dagster+ feature, not part of free open-source Dagster |
How SSO and audit requirements change the Dagster open-source decision
For a regulated team, the first question is not which orchestrator is better but who controls access to the pipelines. Dagster's pricing page lists role-based access control, SAML SSO and audit logs as Dagster+ features rather than part of the free open-source Dagster. That means a team that wants federated sign-in, scoped permissions and a log of who changed what has two paths: buy Dagster+, or build those controls around the open-source deployment. Make this decision before the platform goes into production, because retrofitting access control later touches every job, sensor and schedule.
If the team builds, it owns the work and the proof. Plan for an identity provider integration at the reverse proxy or web tier, group-to-role mapping that limits who can launch runs, edit code locations or read secrets, and a logging path that captures authentication events, configuration changes and run launches from the orchestrator and the infrastructure underneath it. Then test the controls: confirm that a removed user loses access, that a read-only role cannot trigger a materialization, and that logs cannot be altered by the people they describe. An assessor will typically ask for the access control design, the list of roles and their members, a sample of access reviews, change records for the deployment, and log extracts showing the retention period. DeployOpen can plan, build and document these controls and collect the evidence, but it does not issue audit reports or certifications; the independent auditor reaches that conclusion.
Before choosing, ask Dagster in writing which features are included in each tier, how SSO and role definitions map to the product, what audit events are recorded and how long they are kept, and whether the open-source release supports any of these controls at all. Ask your compliance lead which control IDs and evidence formats your assessment expects, and confirm that the chosen path produces them. Also confirm that the migration target version is current; the researched facts show Airflow 2 reached end of life on April 22, 2026, so an Airflow 2 estate is already outside vendor support while the access model is being decided. Check the current Dagster pricing page before you finalize the budget, since tier contents can change.
How to move an Airflow 2 estate to Dagster without a big-bang cutover
This sequence moves an Airflow 2 estate to Dagster one asset at a time, with both systems running in parallel until outputs reconcile and each DAG is retired on sign-off. Airflow 2 reached end of life on April 22, 2026, so the work should start soon, but the cutover itself can be staged.
Inventory every DAG, schedule and dependency
The platform team exports a list of every DAG in the Airflow 2 environment, including its schedule, upstream and downstream dependencies, connections, variables, pools and the teams that own it. Include DAGs that run rarely, such as quarterly regulatory extracts, because they are the ones most often forgotten. The step is done when the inventory has an owner, a consumer list and a schedule for each DAG, and someone outside the platform team has reviewed it. Confirm which DAGs read credentials from the Airflow secrets backend, since those secrets must be re-created in Dagster before any run.
Mark which outputs are data assets
Work with data owners to decide which task outputs are data assets, meaning the tables, files or models that other people or systems consume. Many Airflow tasks only move data between steps and should stay as internal ops rather than become named assets. Record each asset with its upstream sources and downstream consumers. The step is done when every consumed output has a named asset in the plan and each intermediate task has a stated reason for not being one. This mapping is what makes Dagster's asset model useful, so it deserves more time than the code translation.
Build the Dagster project and reuse business logic
Create a Dagster code location and move the Python business logic and SQL from Airflow into it, keeping the transformations themselves unchanged where possible. Rebuild the scheduling, sensors, retries and connections using Dagster's own constructs, since Airflow operators and hooks do not carry over as code. Set up the Dagster deployment in the client's infrastructure with the same secrets and network access the Airflow workers had. The step is done when the Dagster project loads without errors, every planned asset is defined, and the team can materialize a development asset against a non-production source.
Rebuild the highest-value assets first
Start with the assets that feed the most consumers or carry the highest business or compliance risk, and migrate those before low-traffic pipelines. Each migrated asset should produce output that is compared with the Airflow version over the same input window. Record any differences in row counts, null handling, timezone behavior or ordering and resolve them before moving on. The step is done when the first set of assets has passed reconciliation and the owners have accepted the results in writing. Expect the first migrations to surface hidden assumptions in the old DAGs, so keep the first batch small.
Run both systems in parallel and reconcile outputs
Keep Airflow 2 producing the production outputs while Dagster runs the same assets against the same sources, writing to a shadow schema or staging location. Compare the two outputs on a schedule that covers at least one full business cycle for each dataset, including month-end or other peak periods where the team has them. Log every mismatch with its cause and an owner. The step is done when every migrated asset has a documented reconciliation result, with any accepted differences signed off by the data owner. Do not treat an empty mismatch log as proof of parity until the reconciliation covers the edge cases listed in the inventory.
Move schedules, alerts and access controls
Recreate the schedules, failure alerts and on-call routing in Dagster, then switch the alert destinations for each migrated asset so that failures reach the same people as before. Confirm which access controls the team needs in the Dagster deployment. Role-based access control, SAML SSO, audit logs and alerts are listed on Dagster's pricing page as Dagster+ features, not part of open-source Dagster, so the team should confirm with Dagster and its own security function whether those controls are required before production. The step is done when a deliberate test failure triggers the right alert, and the access model has been reviewed against the client's identity policy.
Retire Airflow 2 DAGs after sign-off
After the data owner, the platform team and the security reviewer have signed off on a migrated asset, disable its Airflow 2 DAG and keep the code and metadata archived for the agreed retention period. Remove connections and secrets from Airflow only after the archive is confirmed. Update runbooks, data catalog entries and consumer documentation to point at the Dagster assets. The step is done when no active DAG remains for the retired asset, the archive can be restored, and the change record links the sign-off, reconciliation results and the new asset definitions. Confirm with compliance whether the archive satisfies any evidence requirements in the client's assessments.
Where Airflow upgrades and Dagster rollouts usually run long
The longest part of an Airflow move is rarely the version change itself. On the Airflow side, the work is moving from Airflow 2 to the current line, which the Apache Airflow supported versions page shows is past end of life, and reviewing every custom operator, plugin and provider package the estate depends on. Teams should inventory each DAG, operator import and plugin, then confirm which provider packages each one needs under the new line. Work that looks small in a list often hides pinned dependencies and home-grown hooks that were written against older internals. Expect the inventory itself to take longer than the upgrade, and confirm the scope with the vendor or your own test environment before committing to dates.
On the Dagster side, the work is different in kind. Teams model their pipelines as assets, set up code locations, and decide how those code locations are deployed and how they are promoted between environments. Each of these is a design decision, and the most common overrun is training. Engineers used to thinking in tasks and schedules need time to reason about assets, their dependencies and materialization. Projects tend to slip when that retraining is treated as a one-day session instead of an ongoing part of the rollout. The Dagster concepts documentation is the right starting point for the team's model.
Across both platforms, the same five areas cause most overruns. Undocumented DAG dependencies surface only when a job fails after a change elsewhere. Secrets and connections must be re-mapped to the new runtime without copying credentials into code. Data quality checks that lived in ad hoc tasks need explicit owners and thresholds. Access control design, including who can trigger, edit and view runs, must be settled before the old system is retired. Parallel-run reconciliation, where both systems produce the same outputs for a fixed period, is where discrepancies appear, so plan for it explicitly. DeployOpen plans, implements and documents this work under a written, fixed scope where agreed, so the reader can ask for each of these items to be named as a deliverable in the quote.
Questions buyers ask before choosing or migrating an orchestrator
Do we have to upgrade from Airflow 2 now?
Airflow 2 reached end of life on April 22, 2026, so the project no longer releases fixes for it, and the current release line is Airflow 3.3.2. Staying on Airflow 2 means running an unsupported scheduler and web server in production, which most security and audit teams will flag as a finding. The urgency depends on your risk acceptance process, your exposure, and whether a managed provider covers Airflow 2 for you. Check the support terms on the Airflow supported versions page and confirm any provider commitments in writing before you set a deadline.
What drives the cost of migrating from Airflow to Dagster?
The main cost is engineering time to rewrite pipelines around assets, which depends on how many DAGs you run and how much logic depends on Airflow-specific features such as XComs, sensors, custom operators and plugins. Other drivers include the number of external connections and secrets, the depth of your data quality checks and alerting, CI/CD changes, the number of environments, and the length of the period when both systems run in parallel. Some costs are one-time, such as rewrite, testing and cutover. Others recur, such as platform operations and any Dagster+ subscription you choose. Ask any quote to list the DAG inventory, the custom code in scope, the environments covered, the parallel run period and the acceptance tests.
How long does an Airflow to Dagster migration usually take?
Duration depends less on the framework itself and more on the size of your DAG inventory, how much of your estate you want to redesign rather than port one-to-one, and how many teams must sign off on each cutover. Most estates move in phases grouped by business domain or downstream consumer, so the timeline is a sequence of cutovers rather than one event. Design decisions about the asset model usually take longer than the code changes. No supplied fact gives a general duration, so ask for a phased plan with a named cutover order, the decisions that must be made before build starts, and the acceptance criteria for each phase.
Can our own team migrate Airflow to Dagster without outside help?
A team with strong Python skills, time allocated to the work, and a clear asset model can migrate its own pipelines. The hard parts are usually modeling dependencies as assets, proving that outputs match the old system during a parallel run, and rebuilding alerting and secrets handling without gaps. Teams that lack this experience often underestimate the parity testing. An outside partner can take on a defined part of the work, such as the asset design, the migration of one pipeline group, or the hardening of the new platform. DeployOpen plans and implements self-hosted platforms inside your own infrastructure, and most engagements run on a fixed scope agreed in writing before work starts.
What skills do engineers and platform staff need to run Dagster?
Data engineers need working Python, automated testing, and the ability to model data as assets, including their upstream and downstream dependencies and the tables or models they produce. Platform engineers need to run the service reliably, which means container or VM hosting, secrets management, identity integration, backups of the metadata database, a patching and upgrade routine, and monitoring for failed runs and stalled jobs. Both groups should agree on ownership of each asset, on-call responsibilities, and the release process for pipeline code. Before committing, confirm that your staff have time for this skill shift, and ask any implementation partner to name the training and handover deliverables in the written scope.
Is open-source Dagster enough for SSO and audit logging?
Dagster's pricing page lists role-based access control, SAML SSO, catalog search, column lineage, branch deployments, alerts and audit logs as Dagster+ features, not as part of the free open-source Dagster. If your identity or audit policy requires SSO into the orchestrator or audit logs of user actions inside it, open source alone may not meet that requirement. You can address parts of this outside the product, for example through a reverse proxy with an identity provider and centralized logging of the web server and deployment events, but those controls need design, testing and evidence. Confirm the exact control requirements with your compliance team before deciding, and check the Dagster pricing page for current feature placement.
Do we need Dagster+ to run Dagster in production?
No. Dagster is Apache-2.0 licensed and can be self-hosted, so a team can run production pipelines on the open-source edition. Dagster+ becomes relevant when the features listed on the pricing page matter to your controls or daily operations, such as SAML SSO, role-based access, audit logs or branch deployments. The decision compares the subscription cost against the engineering and operating effort of building and maintaining equivalent controls yourself. Ask for the subscription terms, the features included at your tier, what is billed per year or per seat, and how upgrades and support are handled before you treat either option as the cheaper one.
Can Airflow and Dagster run side by side during migration?
Yes, many teams run both during a phased migration, as long as each pipeline has one owning orchestrator at any given time. The main risks are double runs that write the same tables, duplicated connections and secrets that drift apart, and split monitoring that hides failures. To control these, assign ownership by pipeline group, disable the old schedule when the new one takes over, keep one secrets source for both systems, and route alerts from both into the same on-call channel. Confirm these controls in the written scope, including how parity is checked and when the Airflow environment is retired.
How DeployOpen helps choose, implement or migrate orchestration
DeployOpen starts with the question of which orchestrator fits your data platform, then plans and carries out the work inside your own infrastructure. We cover Airflow, Dagster, or a move between them, and we document the result so your team can run it after handover.
How we scope
- Inventory existing Airflow 2 DAGs, operators, connections and schedules before estimating any migration work.
- Map which pipelines should become asset-based in Dagster and which can remain task-based for now.
- Agree deliverables, timelines and exclusions in writing before any engineering work starts, since engagements are fixed-scope.
- Confirm which Dagster capabilities your team needs and whether they fall within open-source Dagster or Dagster+ features.
How we build
- Build or port pipelines in your own cloud accounts, data centers or air-gapped networks, under your identity and secrets controls.
- Integrate the orchestrator with your warehouses, storage, source systems and alerting channels as agreed in scope.
- Run security testing on the orchestrator, its APIs and connected credentials before production cutover.
- Run old and new pipelines in parallel for a period agreed in scope to compare outputs before retiring Airflow DAGs.
How we hand over
- Document deployment, configuration, DAG or asset conventions, and runbooks so your engineers can maintain them.
- Provide a migration record that maps each retired Airflow DAG to its replacement and notes any behavior changes.
- Walk your platform and security teams through access control, backup and upgrade procedures before sign-off.
- Prepare evidence and configuration records that your compliance team can use in assessments, without implying certification.
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after handover, scoped in writing.
- Plan version upgrades around the Airflow and Dagster release lines your team chooses to run.
- Confirm with your team which open-source or paid Dagster features are needed before each upgrade decision.
- Review access, backups and alerting periodically, with findings and changes recorded in the same documentation set.
