Apache Airflow vs Argo Workflows: when each one fits
Choose Apache Airflow when the people writing pipelines are comfortable in Python and want a scheduler, a metadata database and a web UI for pipeline runs as the center of the platform. Airflow models work as DAGs defined in Python code, and its scheduler, metadata database and executors manage task state. Choose Argo Workflows when each pipeline step is already a container and your team prefers to describe work as Kubernetes custom resources that sit alongside the rest of the cluster's configuration. Argo treats every step as a container and models workflows as a sequence of steps or a directed acyclic graph. Both projects are Apache-2.0 licensed, so license alone will not separate them. Argo Workflows is also a CNCF graduated project, which matters if your governance rules favor that status.
Existing estates matter as much as greenfield choices. Airflow 2 reached end of life on April 22, 2026, so it gets no fixes, and any team still running Airflow 2 must plan an upgrade to the Airflow 3 line or a move to another engine. That upgrade may change the decision, because the work of porting DAGs to Airflow 3 is a cost that Argo does not impose on the same codebase. If you have no Airflow 2 estate, the question is simpler. Confirm with your platform team which Kubernetes version your clusters run, since Airflow 3.3.2 lists support for Kubernetes 1.30 to 1.35, and check that version against the Kubernetes release cycle page.
The decision turns on three questions. First, who writes the pipelines: Python-first data engineers will be more productive with Airflow, while platform or application engineers who already build container images may prefer Argo. Second, how much Kubernetes operating skill the team has, because Argo's behavior is defined through cluster resources and Airflow adds a scheduler, a metadata database and executors that someone must run. Third, how history and backfills must be kept. Write down your retention rules for run history, logs and reruns before you compare features, because those rules determine how much of the archiving and database work you will own under either engine.
Which platform fits which scenario
This table maps common team situations to the platform that usually fits better, with the reason and the condition that would reverse the choice. Read each row against your own skills, existing estate and operating model, not as a ranking of the two tools.
| Scenario | Better fit | Why, and when the other platform wins |
|---|---|---|
| Data engineers writing Python transformations and SQL tasks | Apache Airflow | Airflow DAGs are plain Python with a large operator ecosystem, so data teams can author and review pipelines without writing Kubernetes manifests. Argo wins if the team already treats every job as a container and wants the manifest as the source of truth. |
| Platform teams running container jobs as a shared service | Argo Workflows | Each step is a container defined as a Kubernetes custom resource, so platform engineers can use existing RBAC, namespaces, and GitOps tooling. Airflow fits better if the platform team must also serve analysts who expect a Python DAG interface and a metadata database. |
| ML pipelines with containerized training and evaluation steps | Argo Workflows | Argo's container-per-step model and its Python client Hera suit GPU and batch jobs on Kubernetes, and projects such as Kubeflow Pipelines build on it. Airflow becomes the better choice if the pipeline is mostly data preparation scheduled alongside warehouse loads. |
| CI-style workflows triggered by commits or events | Argo Workflows | Argo's DAG templates, loops, conditionals, timeouts and artifact support map well onto build and test steps running in cluster pods. Neither tool replaces a dedicated CI system with pull request integration, so confirm trigger and reporting needs before choosing. |
| Team with an existing Airflow 2 estate | Apache Airflow (upgrade path) | Airflow 2 reached end of life on April 22, 2026 (per the Airflow version support table), so the estate must move to Airflow 3 whichever platform is chosen. Argo is the better fit only if the DAGs can be rewritten as container steps and the team accepts that rewrite effort. |
| Kubernetes-only operating model with strong GitOps practice | Argo Workflows | Workflows, templates and archived runs are Kubernetes resources, which fits teams that manage everything through manifests. Airflow still works on Kubernetes, but it adds its own scheduler, metadata database and executor configuration to operate. |
| Teams with no Kubernetes skills and a mostly Python workforce | Apache Airflow | Airflow can run on hosts through a PyPI install or other deployment methods, so the Kubernetes requirement is not forced on the team. Argo requires a working Kubernetes cluster, which the team must provision, secure and upgrade. |
| Workloads that must run in many tools and languages inside one pipeline | Argo Workflows | Argo runs any container image, so language and tooling choices stay with each step. Airflow reaches the same result through operators and custom images, but its scheduler-centered model is more natural for orchestrating data tasks than arbitrary containers. |
How Airflow and Argo are built and what that means for operations
Airflow keeps its scheduling state in a database it runs itself, while Argo keeps its state in the Kubernetes API and runs each step as a container. Both choices shape who gets paged, what must be backed up and which skills the platform team needs.
Airflow scheduler and DAG processing
The scheduler decides when tasks run, and Airflow 3 separates DAG parsing into its own component. Airflow supports running more than one scheduler at once, coordinated through database row-level locks, so resiliency depends on the database as much as on the scheduler itself. Check how your team will size and monitor scheduler capacity, and who owns the upgrade path when a DAG parser misbehaves.
Airflow metadata database
Every DAG run, task instance and connection lives in the metadata database, and Airflow's production guidance recommends PostgreSQL or MySQL. The default SQLite backend is for testing only, and running it in production can lead to data loss. MariaDB is not a supported metadata database. Your team must back up, restore and tune this database, which makes it a standing operational responsibility.
Airflow executors and API server
Airflow runs tasks through executors such as KubernetesExecutor, CeleryExecutor and LocalExecutor, and since Airflow 2.10.0 several can run in one deployment. The hybrid CeleryKubernetesExecutor and LocalKubernetesExecutor are no longer supported as of Airflow 3.0.0. The API server and web UI add more components to secure and scale. Confirm which executor model your platform team can support before committing to DAG-level tooling.
Argo workflow controller and Workflow resource
In Argo Workflows, a workflow is a Kubernetes custom resource, and its controller reconciles that resource into running pods. Scheduling, retries and status come from the Kubernetes control loop rather than from a separate scheduler service. Your platform team must already be comfortable with CRDs, RBAC and kubectl troubleshooting, because a broken controller or CRD version affects every workflow on the cluster.
Argo per-step containers
Each Argo step runs as its own container, so every task gets its own image, resource requests and pod lifecycle. This isolates dependencies well but multiplies pod churn, image pulls and cluster-level quota pressure for large fan-out jobs. Check how your cluster autoscaling, image registry and resource quotas will handle peak parallel workflows before you commit to this model.
Argo state and archive option
Argo's run state lives in the Kubernetes API, so a deleted or pruned Workflow object disappears unless you configure archiving. The archive option stores completed workflow history in a database for later queries, which becomes a second data store to back up. Ask your team who owns retention policy, how archived history is audited and whether etcd backups already cover the state you need.
How scheduling and backfills differ in practice
Airflow and Argo Workflows both start runs on a timetable, but they keep run history in different places, and that difference shapes how backfills are done. In Airflow, the scheduler reads DAG definitions and creates a DAG run for each scheduled interval, and every run and task instance is recorded in the metadata database. The Airflow scheduler documentation states that you can run more than one scheduler at the same time for performance and resiliency, using database row-level locks to coordinate them. Because the metadata database holds the full record of which logical dates have run, most backfill work in Airflow starts from that record. Confirm the backfill behavior yourself before committing: the facts available for this comparison do not establish how either platform handles a backfill of historical dates, so run a test over a short window of past dates in an environment that mirrors production.
Argo Workflows takes a different approach. A CronWorkflow runs on a cron schedule, and step and workflow timeouts limit how long a run or step may take. Its run history is not held in a separate scheduler database. It depends on the Kubernetes objects that represent each workflow, and on whether you have configured workflow archiving, which stores completed workflows so they can be inspected after the cluster objects are removed. Plan the retention of those objects and the archive destination before go-live, because a history you did not configure may not exist when an auditor or an engineer asks for it. Ask the Argo project and your own platform team how a backfill over historical dates would be expressed, whether by generating workflows from a loop or by a script that submits runs, and who would own that script.
Test three things in your own environment: how each platform recovers when a scheduled run is missed during a control plane or scheduler outage, how many runs a backfill of your largest historical window creates at once, and how long the resulting run history stays queryable. Record the commands, configuration keys and Kubernetes resources used so the test can be repeated during an upgrade. The Apache Airflow scheduler documentation is the starting point for the Airflow side of this test.
Side-by-side capabilities from supplied facts
This table compares Apache Airflow and Argo Workflows on license, authoring, scheduling, executors, metadata storage, UI, artifacts, archiving, client libraries, Kubernetes dependency and paid editions. Where the project documentation does not establish a capability for one platform, the cell tells you what to confirm before you decide. The main takeaway is that Airflow is a Python scheduler whose Kubernetes role is optional, while Argo is a Kubernetes-native engine that needs a cluster.
| Capability | Apache Airflow (3.3.2) | Argo Workflows (v4.1.5) |
|---|---|---|
| License and governance | Apache-2.0, an Apache Software Foundation project. The project itself offers no paid edition. | Apache-2.0, a CNCF graduated project. Confirm any commercial support options with the vendor or distributor. |
| Authoring model | Python DAGs that define tasks and dependencies in code. | Each step is a container. Sequences or directed acyclic graphs are defined as Kubernetes custom resources. |
| Scheduling and multiple schedulers | Scheduler runs DAGs. Several schedulers can run at once, coordinated by database row-level locks. | Cron scheduling is supported. Confirm how controller replicas are run and coordinated in your cluster. |
| Executors and compute | Multiple executors, including KubernetesExecutor and CeleryExecutor, can run in one deployment since 2.10.0. | Steps run as containers on the cluster. Confirm the execution options your version supports. |
| Metadata store | PostgreSQL 14 to 18 or MySQL 8.0 and 8.4. SQLite is not for production and MariaDB is not supported. | Not established in the supplied facts. Confirm the persistence store your deployment requires. |
| Workflow UI and API | Confirm the UI and API scope for your version in the auth manager documentation. | Includes a UI and a REST and gRPC API. |
| Artifacts and archiving | Confirm artifact handling and run-history retention for your setup. | Artifact support for S3, Artifactory, Azure Blob Storage, GCS, Git and HTTP. Workflow archiving is listed. |
| Client libraries | Python DAG authoring is native. No separate client library is listed in the supplied facts. | Java, Go, Python (Hera) and TypeScript (Juno) client libraries. |
| Kubernetes dependency | Not inherently required. Kubernetes 1.30 to 1.35 is listed in prerequisites, and your executor choice sets how much you depend on it. | Requires a Kubernetes cluster, because Argo is implemented as a Kubernetes Custom Resource Definition. |
What drives the cost and effort of running each platform
Neither project charges a license fee, so the cost of either option is what you pay for infrastructure, people and support. Apache Airflow is Apache-2.0 licensed and the Airflow project offers no paid edition of its own. Argo Workflows is also Apache-2.0 licensed and is a CNCF graduated project. Any paid spend therefore goes to a cloud or Kubernetes platform, to staff or contractors, or to a commercial support contract you choose to buy. Airflow adds a metadata database to the infrastructure bill: the prerequisites recommend PostgreSQL or MySQL in production, warn against SQLite, and recommend at least 4 GB of RAM. Argo runs on a Kubernetes cluster you already need to operate, so its infrastructure cost is mostly the cluster capacity your workflow pods consume plus the artifact store you point it at, such as S3 or GCS. Confirm which of these you will carry and which a cluster team already provides.
Kubernetes upkeep applies to Argo in full and to Airflow's KubernetesExecutor path. Kubernetes publishes minor releases about every 14 weeks and supports only the three most recent minor versions, so you need a recurring upgrade cycle, and the version skew policy requires the API server to move one minor version at a time before the controller manager, scheduler and kubelets. If you run a stacked control plane, plan for etcd backups with etcdctl snapshot save and test restores with etcdutl, because Kubernetes Secrets sit unencrypted in etcd by default. Airflow 3.3.2 supports Kubernetes 1.30 to 1.35, so the Airflow and cluster upgrade calendars must be aligned. The one-time migration cost is the bigger issue for existing Airflow estates. Airflow 2 reached end of life on April 22, 2026 and gets no fixes, so DAGs must move to Airflow 3 before the platform is supported again, and the upgrade guide should be costed as project work before any Argo rewrite is considered.
Ask for these items in any quote, whichever platform you choose. Request a line-item split between one-time work (discovery, DAG conversion or container rewrites, identity and secret integration, artifact store setup, cluster hardening, and runbooks) and recurring work (Kubernetes and Airflow upgrades, etcd or database backups, on-call rotation, and patch testing). Ask who holds the on-call pager, whether the scope includes a tested restore of the metadata database or workflow archive, and which Kubernetes and Airflow versions the quote assumes. Compare staff skills honestly: Airflow engineers need Python and scheduler operations knowledge, while Argo engineers need Kubernetes manifest and RBAC skills. Confirm with each vendor which support tiers exist and what they cover. DeployOpen fixes scope and deliverables in writing before work starts, and its quote follows from that written scope.
Moving from one platform to the other
This sequence gives a team a controlled path from Airflow DAGs to Argo Workflows (or the reverse), keeping the business logic and data stores that carry over and rebuilding the orchestration layer that does not. The order depends on the estate, so treat the steps as a starting checklist to adjust.
Inventory every DAG, workflow and dependency
Someone who owns the pipelines lists each Airflow DAG or Argo workflow template, its schedule, the images or Python packages it runs, the connections and secrets it uses, the data stores it reads and writes, and any upstream or downstream systems that trigger it. Include sensors, custom operators and cron triggers, since these are often missed. The step is done when every pipeline has an owner and a row in a shared inventory that the migration team has reviewed.
Decide which run history must be kept
Compliance, audit and data-lineage owners agree which historical run records must survive the move. In Airflow, that history lives in the metadata database; in Argo Workflows, completed workflows are kept only if workflow archiving is configured. Confirm the retention requirement with your auditors and the archive destination with your platform team before you retire anything. The step is done when a written retention decision exists for each pipeline class.
Separate transferable assets from rebuilt definitions
Engineers sort the estate into three groups. Container images and Python business logic usually carry over, because the transformation code can often run unchanged inside a container. Data stores, such as warehouses, lakes and object storage buckets, stay where they are. DAG definitions, scheduling configuration, Airflow connections and the run history model must be rebuilt in the target platform's own terms. The step is done when each inventory item is tagged as carried over or rebuilt.
Rebuild each pipeline in the target model
Engineers translate each DAG into an Argo workflow, with steps or a directed acyclic graph of container tasks, or they translate Argo workflows into Airflow DAGs. Argo Workflows offers Python authoring through Hera, which can reduce rewrites for Python-heavy teams. Connections move into Kubernetes secrets or the target's configuration store, and cron schedules are re-expressed. The step is done when each pipeline has a reviewed definition in version control and a successful test run in a non-production namespace.
Run both platforms in parallel
Operators keep the existing system active for the agreed period while the new definitions run on a shadow schedule or against copied inputs. Watch for duplicate writes to shared stores, and disable outbound notifications or external side effects in the new copy until they are validated. Confirm that the scheduler behavior, backfills and retry rules match the expectations of the downstream teams. The step is done when every pipeline has completed the parallel run period without unexplained differences.
Validate outputs against the old system
Data owners compare the outputs of old and new runs: row counts, checksums, aggregate values, file manifests and downstream dashboard results. They also check timing, failure handling and alerting, since a pipeline that produces the right data late can still break a consumer. Record each difference and its cause, and get sign-off from the data owner for each pipeline. The step is done when every pipeline has a signed validation record.
Retire the old system and preserve records
Once validation is signed off, the team disables the old schedules, exports the retained run history to the archive chosen in step two, and removes credentials that are no longer needed. For Airflow, that means the metadata database and its backups are handled under the retention decision; for Argo Workflows, it means archived workflows are verified as readable. Keep the old definitions in version control for the agreed period. The step is done when the old scheduler is stopped, the archive has been tested for restore, and the decommission record is filed.
Questions buyers ask before choosing or hiring help
How much does an Airflow or Argo Workflows implementation cost?
Neither project charges a license fee, so the cost is the work around the software: infrastructure, staff time, and any outside help. Apache Airflow is Apache-2.0 licensed with no paid edition from the project itself, and Argo Workflows is also Apache-2.0 licensed. Your spend therefore falls into four buckets: the Kubernetes cluster and its database, storage for logs and artifacts, engineer time to build and operate pipelines, and any fixed-scope engagement you buy. The main cost drivers are the number of pipelines, the number of teams sharing the platform, how many external systems each pipeline touches, and how much security hardening your environment requires. Ask any provider for a written scope that lists deliverables, environments, handover material and what happens after go-live, and ask whether ongoing support is a separate retainer.
How long does a migration from Airflow 2 take?
The duration depends on the estate, not on a standard number. Airflow 2 reached end of life on April 22, 2026, so it gets no fixes, and any migration is now a compliance and risk item as well as a feature decision. Airflow 2 entered limited maintenance on October 22, 2025, and Airflow 3 was first released on April 22, 2025. Before you estimate, inventory the DAGs, custom operators, plugins, connections, executor configuration and the metadata database version. Count how many DAGs depend on hybrid executors, since CeleryKubernetesExecutor and LocalKubernetesExecutor are no longer supported as of Airflow 3.0.0. Argo Workflows is a different authoring model, so a move to it means rewriting pipelines rather than upgrading them. Ask for a dependency inventory as the first deliverable in any written scope.
What skills does a team need to run Airflow compared with Argo?
Airflow needs strong Python skills for authoring DAGs and solid database and scheduler operations knowledge, because the scheduler and metadata database are central. Argo Workflows needs Kubernetes skills, since every step is a container defined as a custom resource, and the team must understand pod scheduling, resource limits and RBAC. Airflow's prerequisites list Python 3.10 to 3.14, PostgreSQL 14 to 18 or MySQL 8.0, 8.4 and Innovation releases, and Kubernetes 1.30 to 1.35 for Airflow 3.3.2. Argo teams who write Python can use Hera, while teams that prefer other languages can use the Java, Go or TypeScript clients. Map your existing skills against these requirements before choosing, and ask any partner which of them the engagement will transfer to your staff.
Can a team operate Argo Workflows without deep Kubernetes knowledge?
Not safely in most production settings. Argo Workflows is implemented as a Kubernetes Custom Resource Definition, so it inherits the cluster's failure modes, upgrade rules and security model. Kubernetes maintains only the three most recent minor releases, and the version skew policy requires you to upgrade the API server one minor version at a time. Secrets are stored unencrypted in etcd by default, so encryption at rest and least-privilege RBAC need an owner. A team without this depth can still run Argo with a platform team in place, but the platform team carries the cluster work. If your staff lacks that depth, ask a provider to name who owns cluster upgrades, etcd backups and access control after handover.
How are workflow history and archiving kept in each platform?
Airflow keeps run history in its metadata database, so the database backup, retention settings and version upgrades determine how long history survives. Airflow's production guide warns that SQLite is for testing only and can lead to data loss, so PostgreSQL or MySQL is the expected choice. Argo Workflows includes workflow archiving, but it is a feature you configure, and the archive location, retention rules and access controls need to be decided explicitly. Argo stores step artifacts in S3, Artifactory, Azure Blob Storage, GCS, Git or HTTP. Confirm in writing which systems hold audit-relevant history, how long each keeps it, and who can read or delete it.
When is it worth bringing in an outside partner?
An outside partner is worth considering when the decision carries deadlines you cannot absorb internally, such as an Airflow 2 estate past end of life, or when your team lacks the Kubernetes or Python depth to own the platform after go-live. DeployOpen plans, implements, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own infrastructure, and most engagements are fixed-scope with scope, deliverables and timelines agreed in writing before work starts. DeployOpen does not publish a price list, so a quote follows from the written scope. Ongoing support and upgrades are available as retainers. Ask for a named list of deliverables, the environments covered, the handover plan and the boundary between your staff and the partner's staff.
What should a written scope for an Airflow or Argo project include?
A written scope should state the platform version, the target infrastructure, the number of pipelines in scope, the integrations and the acceptance criteria. For Airflow, it should name the Airflow 3 release line, the metadata database, the executors and the migration approach for existing DAGs. For Argo, it should name the Kubernetes version, the artifact store, the archive configuration, the RBAC model and the Secrets handling. Include backup and restore tests, upgrade procedures and documentation as explicit deliverables. Exclusions matter as much as inclusions, so ask the provider to list what is out of scope, such as pipeline rewrites or third-party system changes.
How DeployOpen helps choose, implement or migrate
DeployOpen plans, implements, integrates, hardens, backs up, upgrades and documents self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Apache Airflow vs Argo Workflows, that means starting from your scheduling needs, cluster ownership and team skills before any installation begins.
How we scope
- Written scope covers which pipelines move, which engine owns each, and the target cluster.
- Scope records Airflow version, metadata database choice and Kubernetes versions in use.
- Engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts.
How we build
- Build covers the scheduler, metadata database, executors or Argo controller and workflow namespaces.
- Artifact storage, secrets handling and RBAC are configured to your identity and security standards.
- Pipelines are tested against backfill, retry and archiving behavior before go-live.
How we hand over
- Handover includes runbooks for upgrades, etcd or database backups and restore steps.
- Documentation explains which components your team operates and which need vendor confirmation.
- Your engineers receive working knowledge of authoring, scheduling and day-two operations.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers after handover.
- Retainers can cover moving from Airflow 2 once it is past end of life, with scope agreed in writing.
- Security testing of pipelines and their APIs can be scoped separately from platform support.
