Apache Airflow vs MLflow
No clear leader: Apache Airflow (70.9) and MLflow (70.7) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Features
The capabilities each product documents. These tools do not share a category taxonomy, so this aligns what each vendor states rather than a normalised feature set.
| Feature | Apache Airflow | MLflow |
|---|---|---|
| Architecture model | - | open_source_proxy |
| Budget spend controls | - | ✓ |
| Connector count | 100+ | - |
| Data quality | ✓ | - |
| Dbt native | ✓ | - |
| Deployment model | - | self-hosted |
| Distributed executor | ✓ | - |
| Evaluation | - | ✓ |
| Experiment tracking | - | ✓ |
| Framework agnostic | - | ✓ |
| Guardrails moderation | - | built-in |
| Incremental runs | ✓ | - |
| Lineage | ✓ | - |
| Llm observability | - | ✓ |
| Managed cloud | ✓ | ✓ |
| Mit or apache permissive oss license | - | ✓ |
| Model serving | - | ✓ |
| Multi provider | - | ✓ |
| Open source | - | ✓ |
| Openai compatible api | - | ✓ |
| Organization wide cost tracking and chargebacks | - | ✓ |
| Otel compatible | - | ✓ |
| Paradigm | batch | - |
| Pricing model | - | free_open_source |
| Prompt management | - | ✓ |
| Python first | ✓ | - |
| Rate limiting | - | ✓ |
| Request cost observability | - | ✓ |
| Role | orchestrator | platform |
| Self hostable | - | ✓ |
| Self hosted oss | ✓ | - |
| Streaming support | - | ✓ |
| Vpc deployment | - | ✓ |
What each one is
The product in its own terms, so the numbers below have context.
Apache Airflow
An open-source platform where you define workflows in Python, schedule their execution based on dependencies and timing, and monitor progress through a web interface. Widely used for data orchestration, ML operations, and infrastructure automation.
MLflow
A comprehensive, open-source platform that provides experiment tracking, model registry, LLM tracing, prompt management, and deployment capabilities across the complete machine learning and AI lifecycle
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- Databricks
- PostgreSQL
- MySQL
- Kubernetes
- OpenAI
- Anthropic
Apache Airflow
- Amazon Web Services
- Microsoft Azure
- Google Cloud Platform
- Snowflake
- dbt Cloud
- Slack
- Salesforce
- Tableau
- MongoDB
- Redis
- Docker
- Apache Spark
- Apache Kafka
- Apache Cassandra
- Apache Flink
- Apache Druid
- Elasticsearch
- Datadog
- Jenkins
- GitHub
- Cohere
- Pinecone
- Weaviate
- Qdrant
- +26 more
MLflow
- AWS S3
- Google Cloud Storage
- Azure Storage
- AzureML
- LangChain
- Pydantic AI
- Anthropic Claude
- Google Gemini
- SAP AI Core
- JFrog
- Aliyun
- MSSQL
- Claude Code
- OpenAI Codex
- Gemini
- Ollama
- OpenClaw
- Qwen Code
- LiteLLM
- Amazon S3
- OpenTelemetry/OTLP
- OpenAI-compatible endpoints
Apache Airflow or MLflow: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.