Airbyte vs. Airflow

Features

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Connectors

Pre-built connectors are the primary way to differentiate ETL / ELT solutions, enabling data teams to focus only on the insights to build.

‍Airbyte

Airbyte’s approach to its connectors is unique in three ways: 

1. Airbyte is the only platform supporting structured and unstructured sources and vector database destinations for your AI use cases.

2. Airbyte offers Airbyte-official connectors on which it provides an SLA, and a marketplace of connectors powered by the community and built from Airbyte’s Connector Builder (low-code, no-code, or AI-powered). Marketplace connectors have quality and usage indicators. This approach enables Airbyte to offer the largest and fastest-growing catalog of over 550 connectors.

3. All Airbyte connectors are open-sourced, giving users the ability to edit them at will. However, all connectors built with the Connector Builder can be customized. Adding a new stream only takes minutes, as does building a new connector from scratch. 

This open approach empowers Airbyte users to address the growing list of custom connectors they need, while those same users would have to build connectors in-house with a closed-source solution. 

Airbyte will also start offering reverse-ETL connectors in 2025. 

Airflow

You can use one of the 60 available Airflow transfer operators to move data between one system to another like the PostgresToGCSOperator. Sources and destinations are tightly coupled. Because of this, you need a different transfer operator for each pair of source and destination. This makes it hard for Airflow to cover the long tail of integrations.

Transformation

Airbyte

Airbyte offers two options to get your data out of the box: a serialized JSON object and the normalized version of the record as tables. Airbyte also offers custom transformations via SQL and through deep integration with dbt, allowing their users and customers to trigger their own dbt packages at the destination level right after the EL. To help with this, Airbyte open-sourced a few dbt models to have analytics-ready data at your destination. 

Airbyte also supports RAG-specific transformations, including chunking powered by LangChain and embeddings enabled by OpenAI, Cohere, and other providers. This allows you to load, transform, and store data in a single operation.

Finally, Airbyte is offering some mapping features, enabling its users to perform column selection or hashing, handle PII, filtering, and more. 

Airflow

You can transform data locally with the PythonOperator, remotely with operators like the SparkSubmitOperator and in the database with operators like the BigQueryInsertJobOperator. You can also integrate Airflow with dbt for transformations.

Customizability

Every company has custom data architectures and, therefore, unique data integration needs. A lot of tools don’t enable teams to address those, which results in a lot of investment in building and maintaining additional in-house scripts. 

Airbyte

Airbyte’s architecture modularity implies that you can leverage any part of Airbyte. For instance, you can: 

• use Airflow’s, Dagster’s, Prefect’s, or Kestra’s orchestrator to trigger Airbyte’s ELT jobs. 
• leverage Langchain or LlamaIndex for all your AI-related jobs. 
• deploy Airbyte in self-hosted, cloud-hosted, or hybrid.

It also means you can edit any pre-built connectors to your own specific needs or even leverage the no-code / low-code / AI-powered Connector Builder to build your own custom connectors in minutes (instead of days) and share their maintenance with the community and the Airbyte team. 

Airbyte’s promise is to address all your data movement needs.

Airflow

Airflow operators are split into the built-in operators and provider packages. You can modify existing operators and also create new operators on top of existing Airflow hooks.

You can scale Airflow deployments with operators and executors. For example, you can use the CeleryExecutor or the KubernetesExecutor to scale your Airflow workers. 

You can also use AIrflow to schedule ELT tasks and integrate it with Airbyte for the EL steps and dbt for the T step.

Support & docs

Data integration tools can be complex, so customers need to have great support channels. This includes online documentation as well as tutorials, email and chat support. More complicated tools may also offer training services.

Airbyte

Airbyte Cloud provides in-app support with an average response time of less than 1 hour. 

Its documentation is comprehensive and complete with engaging tutorials and quickstarts. Airbyte also has a Slack, GitHub, and Discourse community where help is available from the Airbyte team, other users, or contributors. 

Airbyte does not yet provide training services, but it offers its Airbyte Cloud and Enterprise customers a premium support option with SLAs. 

Airflow

Astronomer.io is the only service to provide premium support.

Airflow documentation is comprehensive but split over different supports. Astronomer.io also provides high quality documentation and guides.

There is a popular Airflow Slack community.

You can get Airflow training from Astronomer.io and get the Apache Airflow certification.

Pricing

Airbyte

Airbyte Open Source is free to use. 

Airbyte Cloud provides a 14-day free trial (which starts after the 1st sync). After the trial, prices are available depending on the volume of data you wish to replicate. It doesn’t charge for failed syncs or normalization. The Enterprise and Team editions, on the other hand, offer capacity-based pricing which depends on the number of pipelines used to sync data simultaneously. Capacity-based model ensures predictable and scalable costs and is suitable for organizations with changing data needs. Learn more about Airbyte's transparent pricing plans here.

Airflow

Cloud Composer pricing is consumption based, so you pay for what you use, based on your CPU, storage and data transfer costs.

Amazon Managed Workflows for Apache Airflow pricing is based on CPU usage from the scheduler, worker and web server. You also pay for the meta database storage.

Astronomer.io pricing is not publicly available, but they provide standard, premium and custom plans. 

More information

If you are interested in more information about Airflow vs. Airbyte, you may wish to read our blog article: The difference between Airbyte and Airflow

About the services

About Airbyte

Airbyte is the leading open data movement platform, created in July 2020. Airbyte offers more than 550 data connectors in its marketplace, with over 7,000 companies using it to sync data daily. In an AI world with an ever-growing list of data sources, Airbyte positions itself as the only futureproof solution. It offers extensibility through Connector Builder and a marketplace, supports unstructured sources and vector database destinations, and allows both self-hosted and cloud-hosted options.

About Airflow

Apache AIrflow is an open-source workflow management tool. Airflow is not an ETL tool but you can use Airflow operators to extract, transform and load data between different systems. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.