Decision comparison
Apache Airflow vs Meltano
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.
Used together. These are normally used together rather than chosen between. The comparison explains what each one does in the stack.
These are different kinds of product — Workflow Orchestrator and ELT Platform.
Quick Comparison
| Decision factor | Apache Airflow | Meltano |
|---|---|---|
| Primary Focus | General-purpose workflow orchestration using Python-based DAGs for scheduling and monitoring data pipelines | Declarative ELT data integration platform built specifically for extract-and-load workloads with 600+ connectors |
| Architecture | Modular architecture with scheduler, web server, metadata database, and executor components communicating via message queue | CLI-first architecture with declarative YAML configuration, Singer-based taps and targets, and built-in dbt integration |
| Connector Ecosystem | Hundreds of pre-built operators for GCP, AWS, Azure, and third-party services; community-maintained provider packages | 600+ pre-built connectors via Meltano Hub covering SaaS APIs, databases, REST endpoints, and semi-structured data sources |
| Pricing Model | Free and open-source under the Apache License 2.0 | Meltano Open is self-hosted. Managed Starter, Growth, Scale, and Enterprise plans are priced by compute capacity; Enterprise is custom. |
| Learning Curve | Steep learning curve requiring Python proficiency and DevOps knowledge for setup, DAG authoring, and infrastructure management | Lower barrier to entry with CLI-driven setup, YAML configuration, and pre-built connectors requiring less custom code |
| Deployment Model | Self-hosted on-premise or cloud VMs; managed options via Astronomer, AWS MWAA, and Google Cloud Composer | Self-hosted open-source deployment or Meltano Cloud managed service with built-in orchestration |
| Configuration Style | Workflows defined as Python code using DAG scripts stored in a designated folder | Pipelines configured declaratively in meltano.yml with environment-specific overrides and Git-based version control |
| Community Size | 46,000+ GitHub stars with a large, active open-source community and extensive third-party documentation | 2,500+ GitHub stars with a growing community of 5,500+ members on Slack and active contributor ecosystem |
Apache Airflow
- Primary Focus:
- General-purpose workflow orchestration using Python-based DAGs for scheduling and monitoring data pipelines
- Architecture:
- Modular architecture with scheduler, web server, metadata database, and executor components communicating via message queue
- Connector Ecosystem:
- Hundreds of pre-built operators for GCP, AWS, Azure, and third-party services; community-maintained provider packages
- Pricing Model:
- Free and open-source under the Apache License 2.0
- Learning Curve:
- Steep learning curve requiring Python proficiency and DevOps knowledge for setup, DAG authoring, and infrastructure management
- Deployment Model:
- Self-hosted on-premise or cloud VMs; managed options via Astronomer, AWS MWAA, and Google Cloud Composer
- Configuration Style:
- Workflows defined as Python code using DAG scripts stored in a designated folder
- Community Size:
- 46,000+ GitHub stars with a large, active open-source community and extensive third-party documentation
Meltano
- Primary Focus:
- Declarative ELT data integration platform built specifically for extract-and-load workloads with 600+ connectors
- Architecture:
- CLI-first architecture with declarative YAML configuration, Singer-based taps and targets, and built-in dbt integration
- Connector Ecosystem:
- 600+ pre-built connectors via Meltano Hub covering SaaS APIs, databases, REST endpoints, and semi-structured data sources
- Pricing Model:
- Meltano Open is self-hosted. Managed Starter, Growth, Scale, and Enterprise plans are priced by compute capacity; Enterprise is custom.
- Learning Curve:
- Lower barrier to entry with CLI-driven setup, YAML configuration, and pre-built connectors requiring less custom code
- Deployment Model:
- Self-hosted open-source deployment or Meltano Cloud managed service with built-in orchestration
- Configuration Style:
- Pipelines configured declaratively in meltano.yml with environment-specific overrides and Git-based version control
- Community Size:
- 2,500+ GitHub stars with a growing community of 5,500+ members on Slack and active contributor ecosystem
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Apache Airflow | Meltano |
|---|---|---|
| Docker Hub pulls(Product adoption) | 1.6B | 2.6M |
| GitHub commits, 90d(Product adoption) | 2.0k | 181 |
| GitHub stars(Product adoption) | 46,000+ | 2,500+ |
| Search interest(Market interest) | 2 | 0 |
| Hacker News mentions, 90d(Community interest) | 1 | 0 |
| PyPI weekly downloads(Product adoption) | 1.8M | 45.9k |
| Stack Overflow questions(Community interest) | 10.6k | 22 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Apache Airflow
September 21, 2026Package vulnerabilities
PyPI · apache-airflow@3.3.2
0 vulnerabilities
across 1 package
Repository security score
github.com/apache/airflow
7.3/10
Meltano
September 21, 2026Package vulnerabilities
PyPI · meltano@4.2.2
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
Apache Airflow
Feature Comparison
| Feature | Apache Airflow | Meltano |
|---|---|---|
| Pipeline Orchestration | ||
| Workflow Scheduling | Cron-based and interval-based scheduler that reads DAG files, triggers task runs according to dependencies, and distributes work across executors including CeleryExecutor and KubernetesExecutor | Built-in schedule definitions in meltano.yml using interval syntax; Meltano Cloud provides managed orchestration for dozens of pipelines; larger deployments integrate with Airflow or Dagster |
| Dependency Management | DAG-based dependency resolution with upstream/downstream task relationships, BranchPythonOperator for conditional paths, fan-out parallelism, and automatic retry with configurable backoff | Sequential task execution within named jobs defined in YAML; tasks run in declared order with built-in idempotency ensuring consistent pipeline state across reruns |
| Error Handling and Recovery | Task-level retry policies, SLA monitoring, email alerting on failure, and the ability to clear and rerun individual tasks or entire DAG runs from the web UI | Detailed pipeline logs and alerting with diagnostics for troubleshooting; supports full or partial re-syncs to correct data inconsistencies without rebuilding entire pipelines |
| Data Integration | ||
| Connector Library | Hundreds of pre-built operators and provider packages for databases, cloud platforms, and SaaS services; custom operators built by extending BaseOperator in Python | 600+ pre-built connectors on Meltano Hub covering SaaS applications, REST APIs, relational databases, and semi-structured data; custom connectors built using the Meltano SDK |
| ELT and Transformation Support | Orchestrates transformation tools like dbt and Databricks as downstream tasks within DAGs; does not perform transformations natively but coordinates the execution pipeline | Native dbt integration configured and version-controlled inside the Meltano project; Elementary integration for data validation; in-flight filtering and PII hashing before warehouse load |
| Replication Strategies | Relies on external operators and custom code to implement replication logic; batch-oriented processing with configurable scheduling intervals for periodic data loads | Built-in support for full, incremental, and log-based replication strategies; batch or near real-time modes configurable per connector in YAML |
| Developer Experience | ||
| Configuration Approach | Python-code-first approach where every workflow is a Python script defining DAG structure, operators, and parameters using Jinja templating for runtime values | Declarative YAML-first configuration in meltano.yml with environment-specific overrides; CLI commands for adding extractors, loaders, and running pipelines |
| User Interface | Full-featured web UI for monitoring DAG runs, viewing task logs, triggering manual runs, and inspecting task dependencies with tree and graph visualizations | Web UI for connector configuration and pipeline monitoring; primary interaction through CLI and YAML files; Meltano Cloud adds a managed dashboard |
| Version Control Integration | DAG files stored as Python scripts in Git repositories; no built-in release management but compatible with standard CI/CD workflows for deployment | Built-in release management using Git and GitHub workflows with named environments; CI/CD pipelines for developing, testing, and promoting pipeline changes with full traceability |
| Operations and Scalability | ||
| Scalability Architecture | Message-queue-based architecture supporting SequentialExecutor, LocalExecutor, CeleryExecutor for distributed workers, and KubernetesExecutor for dynamic pod-based scaling | Lightweight process model suited for ELT workloads; scales horizontally by adding connector instances; large deployments offload orchestration to Airflow or Dagster |
| Monitoring and Observability | Built-in web UI with DAG-level and task-level status tracking, execution logs, SLA miss alerts, and integration with external monitoring via StatsD and Prometheus metrics | Integrated monitoring, logging, and alerting for pipeline health and ingestion job status; pipeline performance metrics visible in Meltano Cloud dashboard |
| Security and Governance | Role-based access control in the web UI, configurable authentication backends, and connection credential management through the metadata database or environment variables | Isolated customer environments with secure credential storage and encrypted data transfer; in-flight PII filtering and hashing; governance enforced through Git-based version control |
| Ecosystem and Community | ||
| Open Source Ecosystem | Apache Software Foundation project with 45,100 GitHub stars, Apache-2.0 license, and a large contributor base; latest release is version 3.2.0 (April 2026) | MIT-licensed open-source project with 2,469 GitHub stars and active development; latest release is version 4.2.0 (April 2026); 5,500+ community members on Slack |
| Third-Party Integrations | Extensive provider ecosystem covering GCP, AWS, Azure, Snowflake, Databricks, dbt, and hundreds of SaaS tools; plugin architecture for custom extensions | Direct integration with dbt for transformation, Elementary for data validation, and BI tools like Omni, Power BI, Looker, and Sigma; SDK for building custom connectors |
| Managed Service Options | Astronomer provides managed Airflow hosting; cloud providers offer AWS MWAA, Google Cloud Composer, and Azure-managed Airflow instances with pay-as-you-go pricing | Meltano Cloud provides managed orchestration and deployment; open-source version runs self-hosted on any infrastructure with no vendor lock-in |
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
How they fit together
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.
What each one handles
Use Apache Airflow for:
Data engineering teams that need a general-purpose orchestrator for complex, multi-step workflows spanning ETL pipelines, ML model training, infrastructure automation, and cross-system dependencies. Airflow delivers the scheduling flexibility and operator ecosystem required when pipelines involve custom Python logic, conditional branching, and coordination across dozens of heterogeneous systems. Teams with strong Python skills and DevOps capacity benefit most from Airflow's programmable DAG model and the mature ecosystem of managed hosting options from Astronomer, AWS MWAA, and Google Cloud Composer.
Use Meltano for:
Data teams that need a focused, cost-effective solution for extracting data from SaaS APIs, databases, and REST endpoints and loading it into a warehouse. Meltano's 600+ pre-built connectors, declarative YAML configuration, and built-in dbt integration reduce the custom code required to stand up production ELT pipelines. Teams looking for faster time-to-value on data ingestion, Git-native version control, and lower costs compared to point-and-click ELT tools benefit from Meltano's open-source core and CLI-first workflow. Smaller teams without dedicated DevOps resources find Meltano Cloud's managed orchestration simplifies operations significantly.
These roles reflect the available product evidence. Most teams run both; which one owns a given job depends on your stack and team.
Frequently Asked Questions
Can Apache Airflow and Meltano be used together in the same data stack?
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.
How do the infrastructure requirements and operational overhead compare between Airflow and Meltano?
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.
Which tool provides better support for building custom data connectors?
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 extensive Meltano Hub ecosystem and work consistently across all Meltano deployments, making it quick to build production-quality connectors for data extraction use cases.
What are the main cost differences between running Airflow and Meltano in production?
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.