Apache Airflow vs Kubeflow Pipelines
A side-by-side comparison of Apache Airflow and Kubeflow Pipelines across 26 workflow orchestration capabilities. Apache Airflow fully supports 13 of them and Kubeflow Pipelines fully supports 14; of the 12 mandatory features, Apache Airflow covers 11 and Kubeflow Pipelines covers 10.
Apache Airflow
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It allows you to define workflows as code, making them more maintainable, versionable, testable, and collaborative.
Kubeflow Pipelines
Kubeflow Pipelines (KFP) is a platform for building and deploying portable, scalable machine learning workflows on Kubernetes, using a Python SDK to define pipelines as containerized components.
Feature comparison
6 of 26 capabilities differ between Apache Airflow and Kubeflow Pipelines.
| Capability | Importance* | Apache Airflow | Kubeflow Pipelines |
|---|---|---|---|
DAG-based workflow definition Define dependencies and execution order Mandatory | Mandatory | Yes | Yes |
Code-first workflows Versionable, testable workflows Mandatory | Mandatory | Yes | Yes |
Declarative workflow model Predictability, reproducibility Mandatory | Mandatory | Yes | Yes |
Task dependency resolution Correct execution order Mandatory | Mandatory | Yes | Yes |
Persistent workflow state Crash recovery, observability Mandatory | Mandatory | Yes | Yes |
Task retries Handle transient failures Mandatory | Mandatory | Yes | Yes |
Retry policies (backoff, limits) Avoid cascading failures Recommended | Recommended | Partial | Yes |
Scheduling (cron / time-based) Automated recurring workflows Depends | Depends | Yes | Yes |
Event-driven execution React to external systems Depends | Depends | Partial | Partial |
Backfills / replays Reprocess historical data Depends | Depends | Yes | Partial |
Idempotency support Safe retries & replays Mandatory | Mandatory | Yes | Partial |
Exactly-once semantics Correctness for business workflows Optional | Optional | No | No |
Long-running workflows (days/months) Business processes, approvals Depends | Depends | No | Partial |
Human-in-the-loop steps Approvals, manual actions Optional | Optional | No | No |
Dynamic workflows at runtime Conditional execution Depends | Depends | Partial | Partial |
Workflow versioning Safe evolution over time Recommended | Recommended | No | Yes |
Distributed execution Scalability Mandatory | Mandatory | Yes | Yes |
High availability scheduler Production reliability Recommended | Recommended | Partial | Partial |
Fault-tolerant execution Crash safety Mandatory | Mandatory | Partial | Partial |
Observability (logs, states, UI) Debugging & ops Mandatory | Mandatory | Yes | Yes |
Workflow-level visibility Understand end-to-end runs Mandatory | Mandatory | Yes | Yes |
Task isolation Failure containment Mandatory | Mandatory | Yes | Yes |
Native Kubernetes support Modern infra compatibility Depends | Depends | Partial | Yes |
Multi-language SDKs Polyglot systems Optional | Optional | No | No |
Deterministic replay Safe recovery & correctness Optional | Optional | No | No |
Pluggable execution backends Flexibility Recommended | Recommended | Partial | Partial |
* To be considered a complete workflow orchestration tool, a tool must implement the features marked as mandatory according to our assessment.
Spotted an inaccuracy? contact@workflows.guru
Learn more
Read the full profiles, including pros and cons, installation and alternatives.
Related comparisons
- Apache Airflow vs Prefect
- Kubeflow Pipelines vs Prefect
- Apache Airflow vs Dagster
- Kubeflow Pipelines vs Dagster
- Apache Airflow vs Argo Workflows
- Kubeflow Pipelines vs Argo Workflows
- Apache Airflow vs Temporal
- Kubeflow Pipelines vs Temporal
- Apache Airflow vs n8n
- Kubeflow Pipelines vs n8n
- Apache Airflow vs Luigi
- Kubeflow Pipelines vs Luigi
- Apache Airflow vs Flyte
- Kubeflow Pipelines vs Flyte
- Apache Airflow vs CrewAI
- Kubeflow Pipelines vs CrewAI
- Apache Airflow vs Kestra
- Kubeflow Pipelines vs Kestra
- Apache Airflow vs LangGraph
- Kubeflow Pipelines vs LangGraph
- Apache Airflow vs Pipedream
- Kubeflow Pipelines vs Pipedream
- Apache Airflow vs Workato
- Kubeflow Pipelines vs Workato
- Apache Airflow vs Cadence
- Kubeflow Pipelines vs Cadence
- Apache Airflow vs Elsa Workflows
- Kubeflow Pipelines vs Elsa Workflows
- Apache Airflow vs Camunda
- Kubeflow Pipelines vs Camunda
- Apache Airflow vs Metaflow
- Kubeflow Pipelines vs Metaflow
- Apache Airflow vs ZenML
- Kubeflow Pipelines vs ZenML
- Apache Airflow vs Apache NiFi
- Kubeflow Pipelines vs Apache NiFi
- Apache Airflow vs Mage
- Kubeflow Pipelines vs Mage
- Apache Airflow vs Tekton
- Kubeflow Pipelines vs Tekton
- Apache Airflow vs Conductor
- Kubeflow Pipelines vs Conductor
- Apache Airflow vs AWS Step Functions
- Kubeflow Pipelines vs AWS Step Functions
- Apache Airflow vs Windmill
- Kubeflow Pipelines vs Windmill
- Apache Airflow vs Activepieces
- Kubeflow Pipelines vs Activepieces
- Apache Airflow vs Concourse
- Kubeflow Pipelines vs Concourse
- Apache Airflow vs Apache DolphinScheduler
- Kubeflow Pipelines vs Apache DolphinScheduler
- Apache Airflow vs Inngest
- Kubeflow Pipelines vs Inngest
- Apache Airflow vs Restate
- Kubeflow Pipelines vs Restate
- Apache Airflow vs Hatchet
- Kubeflow Pipelines vs Hatchet
- Apache Airflow vs Trigger.dev
- Kubeflow Pipelines vs Trigger.dev