Apache Airflow and Meltano address different layers of the data pipeline stack. Airflow provides general-purpose workflow orchestration for scheduling and monitoring any type of batch data pipeline using Python-based DAGs. Meltano focuses specifically on the extract-and-load layer, offering a declarative, CLI-first ELT platform with 600+ pre-built connectors and native dbt integration. Many teams use both together, with Meltano handling data ingestion and Airflow orchestrating the broader workflow.
| Feature | Apache Airflow | Meltano |
|---|---|---|
| Primary Focus | — | — |
| Architecture | — | — |
| Connector Ecosystem | — | — |
| Pricing Model | Free and open-source under the Apache License 2.0 | Free tier (1 user), Meltano Pro $25/mo, Enterprise custom |
| Learning Curve | — | — |
| Deployment Model | — | — |
| Configuration Style | — | — |
| Community Size | — | — |
| Metric | Apache Airflow | Meltano |
|---|---|---|
| GitHub stars | 45.3k | 2.5k |
| TrustRadius rating | 8.7/10 (58 reviews) | 9.0/10 (1 reviews) |
| PyPI weekly downloads | 4.3M | 61.9k |
| Docker Hub pulls | 1.6B | 2.5M |
| Search interest | 3 | 0 |
As of 2026-05-04 — updated weekly.
| Feature | Apache Airflow | Meltano |
|---|---|---|
| Pipeline Orchestration | ||
| Workflow Scheduling | — | — |
| Dependency Management | — | — |
| Error Handling and Recovery | — | — |
| Data Integration | ||
| Connector Library | — | — |
| ELT and Transformation Support | — | — |
| Replication Strategies | — | — |
| Developer Experience | ||
| Configuration Approach | — | — |
| User Interface | — | — |
| Version Control Integration | — | — |
| Operations and Scalability | ||
| Scalability Architecture | — | — |
| Monitoring and Observability | — | — |
| Security and Governance | — | — |
| Ecosystem and Community | ||
| Open Source Ecosystem | — | — |
| Third-Party Integrations | — | — |
| Managed Service Options | — | — |
Workflow Scheduling
Dependency Management
Error Handling and Recovery
Connector Library
ELT and Transformation Support
Replication Strategies
Configuration Approach
User Interface
Version Control Integration
Scalability Architecture
Monitoring and Observability
Security and Governance
Open Source Ecosystem
Third-Party Integrations
Managed Service Options
Apache Airflow and Meltano address different layers of the data pipeline stack. Airflow provides general-purpose workflow orchestration for scheduling and monitoring any type of batch data pipeline using Python-based DAGs. Meltano focuses specifically on the extract-and-load layer, offering a declarative, CLI-first ELT platform with 600+ pre-built connectors and native dbt integration. Many teams use both together, with Meltano handling data ingestion and Airflow orchestrating the broader workflow.
Choose Apache Airflow if:
Choose Meltano if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Apache Airflow and Meltano are frequently deployed together in production data stacks because they address different layers. Meltano handles the extract-and-load phase using its 600+ pre-built connectors and Singer-based taps and targets, while Airflow orchestrates the broader workflow including scheduling Meltano jobs, triggering dbt transformations, coordinating ML training pipelines, and managing cross-system dependencies. Meltano's own documentation recommends this pattern for teams running hundreds of pipelines, using Airflow, Dagster, or Orchestra as the orchestration layer above Meltano's ELT engine. The integration works by having Airflow's BashOperator or PythonOperator invoke Meltano CLI commands within DAG tasks.
Apache Airflow requires a metadata database (PostgreSQL or MySQL for production), a scheduler process, a web server, and executor workers, which means teams must provision and maintain multiple components. Managed services like Astronomer, AWS MWAA, and Google Cloud Composer reduce this burden but add cost. Meltano's open-source version runs as a single CLI process with a meltano.yml configuration file, requiring significantly less infrastructure for straightforward ELT workloads. Meltano Cloud further reduces operational overhead by providing managed orchestration, scheduling, and monitoring. Teams running complex multi-step workflows still need a dedicated orchestrator like Airflow alongside Meltano for the data movement layer.
Both tools support custom connector development but take different approaches. Apache Airflow uses Python-based operators that inherit from BaseOperator, giving developers full programmatic control but requiring them to implement connection logic, error handling, and data transfer code from scratch. Meltano provides the Meltano SDK, a purpose-built framework for creating Singer-compatible taps (extractors) and targets (loaders) with built-in schema discovery, state management, incremental replication, and standardized output formats. The Meltano SDK approach produces connectors that integrate automatically with the broader Meltano Hub ecosystem and work consistently across all Meltano deployments, making it faster to build production-quality connectors for data extraction use cases.
Apache Airflow's open-source version is free under the Apache License 2.0, but production deployments incur infrastructure costs for the scheduler, web server, metadata database, and workers. Managed Airflow services from Astronomer, AWS MWAA, and Google Cloud Composer charge based on environment size and usage. Meltano's open-source core is free under the MIT license, with Meltano Pro available at $25/mo and Enterprise at custom pricing. Meltano's website states their connectors cost 30-40% less than competitors, citing specific connector-level pricing comparisons on their pricing page. For data ingestion workloads, Meltano's pre-built connectors eliminate the engineering time required to build equivalent custom operators in Airflow, reducing total cost of ownership for extract-and-load use cases.