Apache Airflow alternatives: top competitors compared
Named alongside Apache Airflow
Google Vertex AI85% of Apache Airflow answersMLflowBest forTeams of all sizes building LLM applications, agents and ML models who want open source tooling without vendor lock-in.69% of Apache Airflow answersAWS69% of Apache Airflow answersKubeflow54% of Apache Airflow answers
Data from SlateIndex weekly runs
Apache Airflow has a visibility score of 5.2 in MLOps Platforms, #18 of 61. AI answers that name Apache Airflow most often also name these 4.
Quick reference
| Alternative | Best for | Starting price | Visibility | Key differentiator |
|---|---|---|---|---|
| Google Vertex AI | — | — | 33.1% | Tight Google Cloud integration with no infrastructure to manage, according to reviewers. |
| MLflow | Teams of all sizes building LLM applications, agents and ML models who want open source tooling without vendor lock-in. | — | 50.9% | Fully open source under Apache 2.0, with tracing built on OpenTelemetry and no vendor lock-in. |
| AWS | — | — | 35.7% | Syncs Figma components into generated code and provisions AWS backend services through a visual interface. |
| Kubeflow | — | — | 20.1% | A Kubernetes-native, modular stack for data and AI workloads that runs the same code on a laptop, on-premises or in any cloud. |
Alternative 1
Google Vertex AI vs Apache Airflow →Google Vertex AI
Google Vertex AI is ranked #5 in MLOps Platforms.
Quick facts
- Pricing not available
- 33.1% visibility score · #5 in MLOps Platforms
Key features
- Vertex AI Pipelines — Serverless orchestrator that runs KFP or TFX pipelines.
- Feature Store — Managed feature store for ML workflows.
- Model Registry — Registers models before they are deployed to endpoints.
- Model Monitoring — Detects skew and drift in deployed models.
Google Vertex AI vs Apache Airflow
| Dimension | Google Vertex AI | Apache Airflow |
|---|---|---|
| Visibility score | 33.1% | 5.2% |
| Pricing model | — | — |
| Entry price | — | — |
| Free plan | — | — |
| Trial | — | — |
Pros
- unified ML platform across the lifecycle
- strong Google Cloud integration
- strong support for generative and foundation models
- robust MLOps and agentic workflow support
- $300 starter credits
Cons
- less developer-centric for Python, Kubernetes and Git-based teams
- usage-based costs depend on compute, storage and API usage
Alternative 2
MLflow vs Apache Airflow →MLflow
MLflow suits teams of all sizes building LLM applications, agents and ML models who want open source tooling without vendor lock-in..
Quick facts
- Pricing not available
- 50.9% visibility score · #1 in MLOps Platforms
- 100+ native integrations
Key features
- MLflow Tracing and Observability — Captures traces of LLM applications and agents, built on OpenTelemetry, to monitor quality, cost and safety.
- MLflow Evaluation — Runs evaluations with 50+ built-in metrics and LLM judges, and tracks quality metrics over time.
- Prompts and Optimization — Versions, tests and deploys prompts with lineage tracking, and optimizes prompts automatically.
- AI Gateway — Unified OpenAI-compatible gateway for LLM providers, with rate limits, fallbacks and cost control.
MLflow vs Apache Airflow
| Dimension | MLflow | Apache Airflow |
|---|---|---|
| Visibility score | 50.9% | 5.2% |
| Pricing model | — | — |
| Entry price | — | — |
| Free plan | — | — |
| Trial | — | — |
Pros
- free open-source software with no license fee
- widely adopted MLOps standard and baseline
- experiment tracking and model registry
- portability and avoiding cloud or vendor lock-in
- strong free starting point for small teams
Cons
- requires DevOps capability and significant infrastructure and integration work
- lower rating of about 4.3/5
Alternative 3
AWS vs Apache Airflow →AWS
AWS is ranked #4 in MLOps Platforms.
Quick facts
- Pricing not available
- 35.7% visibility score · #4 in MLOps Platforms
Key features
- Amplify Studio — Visual low-code tool that syncs Figma components, generates JavaScript or TypeScript UI code and provisions AWS backend services.
AWS vs Apache Airflow
| Dimension | AWS | Apache Airflow |
|---|---|---|
| Visibility score | 35.7% | 5.2% |
| Pricing model | — | — |
| Entry price | — | — |
| Free plan | — | — |
| Trial | — | — |
Pros
- Core federation and multi-account management included at no extra charge
- Enterprise-grade reliability and uptime on AWS infrastructure
- Pay-as-you-go, usage-based pricing
- Deep integration with the AWS ecosystem and AI tools
- Recognised by analysts such as Gartner and ISG
Cons
- Requires strong AWS engineering or technical expertise
Alternative 4
Kubeflow vs Apache Airflow →Kubeflow
Kubeflow is ranked #9 in MLOps Platforms.
Quick facts
- Pricing not available
- 20.1% visibility score · #9 in MLOps Platforms
Key features
- Kubeflow Spark Operator — Makes specifying and running Spark applications as easy as running other workloads on Kubernetes.
- Kubeflow Notebooks — Runs interactive development environments for AI, ML, and Data workloads on Kubernetes.
- Kubeflow Trainer — Kubernetes-native distributed training and LLM fine-tuning across frameworks such as PyTorch, JAX, HuggingFace and XGBoost.
- Kubeflow Katib — Kubernetes-native AutoML with hyperparameter tuning, early stopping and neural architecture search.
Kubeflow vs Apache Airflow
| Dimension | Kubeflow | Apache Airflow |
|---|---|---|
| Visibility score | 20.1% | 5.2% |
| Pricing model | — | — |
| Entry price | — | — |
| Free plan | — | — |
| Trial | — | — |
Pros
- free and open source
- strong choice for Kubernetes-native, cloud-native ML platforms
- portability and deep infrastructure control
- modular, open-source infrastructure
Cons
- operationally complex, especially for small teams
- requires Kubernetes expertise and significant infrastructure ownership
- Kubernetes operations add infrastructure costs
How to choose
Match the pricing model to your cost driver: headcount, usage or features.
| Platform | Pricing model | Starting price |
|---|---|---|
| Apache Airflow | — | — |
| Google Vertex AI | — | — |
| MLflow | — | — |
| AWS | — | — |
| Kubeflow | — | — |
More alternatives
Ranked by how often AI answers name them alongside Apache Airflow in the latest weekly run.