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.
Focus
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Data movement (including AI support), governance.
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Workflow Management.
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Sources
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550+ pre-built customizable connectors for both structured and unstructured sources.
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More than 30 sources with the transfer operators. Sources are tightly coupled with destinations.
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Destinations
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Data warehouses, lakes, databases, 10+ vector databases, LLMs, RAG and more.
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All major data warehouses, lakes and databases. Destinations are tightly coupled with sources.
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Customizability of connectors
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Edit any connectors and build new ones within minutes through Airbyte’s Connector Builder (low-code, no-code, AI-powered).
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Users can edit any pre-built operator and build their own ones.
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Database replication
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Full table and incremental via change data capture.
Pricing adapted for this use case.
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Full table replication. Incremental replication requires coding your own logic in your Airflow DAGs and SQL files to only extract new data.
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Integration with data stack
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Kubernetes, Airflow, Prefect, Dagster, dbt, LangChain, LlamaIndex, OpenAI, Cohere.
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Kubernetes, dbt, Airbyte and more.
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Support SLAs
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Available
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N/A
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Security certifications
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SOC 2, ISO 27001, GDPR, HIPAA Conduit
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N/A
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Vendor lock-in
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Airbyte Core (ELv2) and Connectors (MIT) are open source.
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Airflow Core and Operators are open source.
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Purchase process
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Self-service or sales for Airbyte Cloud.
Open-source edition deployable in minutes.
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Self-service for Managed services with Google Cloud Composer and Amazon Managed Workflows for Apache Airflow (MWAA). Sales for Astronomer.io.
Open-source edition deployable in minutes.
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Pricing
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Volume-based for Airbyte Cloud and capacity-based for Enterprise and Team editions.
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Pricing for Cloud Composer is based on CPU, storage and egress cost. Pricing for MWAA is based on storage and compute cost. Astronomer.io’s pricing is not public.
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API
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Available.
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Available.
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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
