--- title: Apache Airflow Reviews meta_title: 'Apache Airflow Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 128 reviews by the users' company size, role or industry to find out how Apache Airflow works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 128 scale: '5' date_modified: '2026-06-30' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # Apache Airflow Reviews **Vendor:** The Apache Software Foundation **Category:** [AI Orchestration Software](https://www.g2.com/categories/ai-orchestration) **Average Rating:** 4.4/5.0 **Total Reviews:** 128 ## About Apache Airflow Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs. Key Features: - Pure Python: Workflows are defined using standard Python code, allowing for dynamic pipeline generation and easy integration with existing Python libraries. - User-Friendly Web Interface: A robust web application enables users to monitor, schedule, and manage workflows without the need for command-line interfaces. - Extensibility: Users can define custom operators and extend libraries to fit their specific environment, enhancing the platform's flexibility. - Scalability: Airflow's modular architecture and use of message queues allow it to orchestrate an arbitrary number of workers, making it ready to scale as needed. - Robust Integrations: The platform offers numerous plug-and-play operators for executing tasks across various cloud platforms and third-party services, facilitating easy integration with existing infrastructure. Primary Value and Problem Solving: Apache Airflow addresses the challenges of managing complex data workflows by providing a scalable and dynamic platform for workflow orchestration. By defining workflows as code, it ensures reproducibility, version control, and collaboration among teams. The platform's extensibility and robust integrations allow organizations to adapt it to their specific needs, reducing operational overhead and improving efficiency in data processing tasks. Its user-friendly interface and monitoring capabilities enhance transparency and control over workflows, leading to improved data quality and reliability. ## Apache Airflow Pros & Cons **What users like:** - Users appreciate the **ease of use** of Apache Airflow, facilitating workflow design and monitoring with intuitive features. (35 reviews) - Users appreciate the **intuitive web UI** of Apache Airflow, enabling efficient monitoring and debugging of workflows. (18 reviews) - Users value the **flexibility** of Apache Airflow, allowing customized workflows and seamless integration with various services. (13 reviews) - Users appreciate the **automation capabilities** of Apache Airflow, praising its simplicity and effectiveness in scheduling tasks. (10 reviews) - Users highlight the **easy integrations** of Apache Airflow, enhancing flexibility for diverse workflows and data sources. (10 reviews) - Users appreciate the **extensive integrations** of Apache Airflow, enabling seamless connections with various applications and data sources. (10 reviews) - Users love the **intuitive Python interface** of Apache Airflow, making it easy to set up and manage workflows. (9 reviews) - Efficiency (6 reviews) - Scalability (6 reviews) - Development Ease (4 reviews) **What users dislike:** - Users face a **difficult setup** with Apache Airflow, learning nuances that can complicate initial configuration and usage. (13 reviews) - Users find the **learning curve challenging** , requiring significant time to master operators and the scheduling system. (9 reviews) - Users find Apache Airflow has a **steep learning curve** , making initial setup and configuration quite challenging for newcomers. (8 reviews) - Users find Apache Airflow has a **steep learning curve** that complicates job setup and debugging, making it challenging. (6 reviews) - Users find the **outdated user interface** of Apache Airflow contributes to a less efficient and seamless experience. (6 reviews) - Users find the **UI clumsy and daunting** , impacting usability and the overall experience with Apache Airflow. (6 reviews) - Complexity (5 reviews) - Users find the **interface complexity** of Apache Airflow challenging, requiring significant technical knowledge for effective use. (5 reviews) - Missing Features (5 reviews) - Performance Issues (5 reviews) ## Apache Airflow Reviews ### 1. Scalable Workflows with Apache Airflow, Best data Engineering tool for Orchestrator,Easy Deployment **Rating:** 4.5/5.0 stars **Reviewed by:** Rajesh K. | Senior Cloud Software Engineer , Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2026 **What do you like best about Apache Airflow?** It is easiy to deploy with docker. Provide secure authentication. A better UI in airlfow3.x. There. is many method, operator, hooks are added. easily to add dependecy. A better workflow monitoring tool. Easily schedule , activate and deactivate jobs. Good performance when used in any cloud platform like aws in serverless mode. easily scaleable. Easy to integrate third part library. Apache airflow is free of cost but you need to pay compute cost if you are using any cloud. A wide support community **What do you dislike about Apache Airflow?** When I create a task in expansion mode, it doesn’t display the graphical flow properly. I also noticed there’s too much logging. And when setting it up on AWS MWAA in a private VPC, it requires additional configuration. **What problems is Apache Airflow solving and how is that benefiting you?** I have thousands of jobs that I need to monitor daily. In the past, we created a custom Python script, but later we found Airflow and started using it. Now, in one place, we can monitor all running jobs along with their status, and we can easily notify the developer or the respective person to check any alerts. It also helps me provide a better orchestration flow. ### 2. Powerful for complex ML pipelines, but comes with a steep infrastructure learning curve **Rating:** 5.0/5.0 stars **Reviewed by:** Sachin G. | Machine Learning Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 08, 2026 **What do you like best about Apache Airflow?** We use Apache Airflow as the central orchestrator for our entire machine learning and data engineering lifecycle. Specifically, it manages the schedules and dependencies for pulling raw data from our production databases, orchestrating daily feature engineering jobs, and kicking off automated model retraining pipelines. Once the models are evaluated, Airflow also triggers the deployment scripts to push updated model artifacts to our staging environment. It basically acts as the glue holding our data workflows together, ensuring that everything runs in the exact sequence required.What I appreciate most about Airflow is its "configuration as code" philosophy. Because every workflow is defined as a Python script, it fits perfectly into our existing development practices. We can version control our DAGs in Git, run code reviews on them, and easily write custom operators when the built-in ones don't quite fit our needs. The UI is also incredibly detailed; when a complex pipeline fails at 3:00 AM, the tree view and the ability to dig directly into the logs of a specific failed task save us an immense amount of troubleshooting time. The open-source community is massive, so if you are trying to connect Airflow to a database or a cloud service, chances are a robust provider package already exists. **What do you dislike about Apache Airflow?** The biggest headache with Airflow is definitely the operational overhead and the steep learning curve required to keep it running smoothly. Managing the scheduler, web server, and workers—especially when handling resource-intensive machine learning tasks—requires a lot of infrastructure tuning. We struggled initially with the scheduler getting bogged down and tasks getting stuck in a queued state. Additionally, local development can be painful to set up realistically, and the fact that the scheduler constantly parses Python files means you have to be extremely careful about how you write your code to avoid severe performance degradation. It is not a tool you can just turn on and forget about; it requires dedicated DevOps attention. **What problems is Apache Airflow solving and how is that benefiting you?** Before implementing Airflow, our data syncing and model training tasks were managed by a fragmented mix of cron jobs and custom shell scripts. If a single step failed, the subsequent scripts would either run with corrupt data or the whole system would silently halt without anyone knowing. Airflow completely solved this visibility and dependency issue. For example, we automated a massive customer churn prediction pipeline that requires seamless execution across data extraction, transformation, and model inference. Now, if the initial data extraction step fails due to a network glitch, Airflow automatically retries it a few times before alerting us on Slack, ensuring our downstream models always train on fresh, accurate data without manual intervention. ### 3. Incredible flexibility with Python, but requires dedicated maintenance **Rating:** 5.0/5.0 stars **Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Apache Airflow?** Our data pipelines, and all machine learning workflows are orchestrated on top of Apache Airflow. We have a variety of ETL jobs running on a schedule and some more complicated multi-step ML training loops. Airflow is in the middle, consuming data from a number of production databases, running our data preprocessing scripts, feeding our modelling training pipelines, and sending the results to our data analytics dashboards and cloud storage. It's like the clock and the nervous system within our whole data architecture design.That's definitely the selling point of Airflow that everyone can't outrace from - configuration as code. Being completely in Python it gives us a lot of flexibility. I can work on real data workflows using common software engineering techniques such as git version control, code reviews, and unit-testing my code. The UI is also great for debugging: I'm sure someone is running a complex pipeline somewhere, at 3:00 AM some of their critical tasks are failing, and using the tree view and direct access to the task logs, I can be almost certain which task failed and why. Oh, and, because they have such a big community, we have an operator or a provider for almost any cloud service or tool we want to connect to. **What do you dislike about Apache Airflow?** This is extremely powerful, but by no means a “set it and forget” it tool. The overhead of the infrastructure is definitely high, and the need to fine-tune and keep the scheduler up and running without a single point of failure can be very demanding on a medium sized team. It is also a rather steep learning curve for novices. Creating a DAG correctly is quite complex, because one has to grasp the concepts such as idempotence, execution dates, etc., which are not intuitive for a junior engineer and can lead to misunderstandings and Double Runs. Finally, the local development machine may be somewhat cumbersome and complicated to set up. **What problems is Apache Airflow solving and how is that benefiting you?** Prior to Airflow we were dependent on a very fragile web of custom shell scripts and cron jobs for our data pipelines. Imagine if one script went wrong in the middle of the night, it would silently fail to complete the downstream workflows and we would only learn about the failures down the road by a business stakeholder saying that the dashboards were broken the following morning. Airflow took care of this nightmare problem. For example, we have a daily retraining pipeline that fails when there is a temporary network glitch and Airflow sends it out again a few times. If it continues to fail, it slows down the various steps downstream and gives us a clear notification on the instant in Slack! This reliability has reduced our manual firefighting and manual data recovery time for us to countless hours. ### 4. Apache Airflow : Flexible and reliable orchestration tool with a learning curve **Rating:** 4.0/5.0 stars **Reviewed by:** Salman K. | Subordinate Consultant, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** March 17, 2026 **What do you like best about Apache Airflow?** What I like most about Airflow is its flexibility and number of features for building workflows using DAGs. It is very useful for managing complex pipelines with dependencies. Integration with different systems is also strong. Once setup is done, it works reliably and is used frequently in day-to-day operations. **What do you dislike about Apache Airflow?** Ease of use is one area where it can improve, especially for new users. Initial setup and implementation take effort if you are managing it yourself. The UI is not very user-friendly and sometimes slow. Also, debugging failed workflows can take time. **What problems is Apache Airflow solving and how is that benefiting you?** Airflow is helping us automate and manage data pipelines in a structured way. Earlier, tasks were manual and not properly scheduled, but now everything runs through defined workflows. It has improved reliability, reduced manual effort, and made it easier to monitor processes. Integration with multiple systems also helps in handling end-to-end data flow efficiently. ### 5. Flexibility and Power for Complex Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Akash M. | Senior Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** May 03, 2026 **What do you like best about Apache Airflow?** I use Apache Airflow to schedule and manage data pipelines, and I appreciate how it automates ETL pipelines easily and enables monitoring of tasks. I love its flexibility to build and manage complex workflows effortlessly using code, along with features like Operators and sensors. The UI is very helpful for tracking pipelines and quickly debugging failures. It helps me create, automate, and schedule complex workflows by reducing manual efforts, making pipeline monitoring and error handling much easier. I love most of the features that Airflow provides, and I rate it 9/10 as I love this platform. **What do you dislike about Apache Airflow?** For beginners, Apache Airflow can be complex to setup, especially with Docker. At times when a DAG is updated, the UI can feel slow. **What problems is Apache Airflow solving and how is that benefiting you?** I use Apache Airflow to automate ETL pipelines, reducing manual efforts and making pipeline monitoring and error handling easier. ### 6. Organised, Flexible Workflow Management with a Great Monitoring UI **Rating:** 5.0/5.0 stars **Reviewed by:** Brian K. | Technical Lead, Mid-Market (51-1000 emp.) **Reviewed Date:** March 22, 2026 **What do you like best about Apache Airflow?** What I like most about Apache Airflow is how it makes managing workflows feel organised and predictable. You can clearly define tasks, set dependencies, and see everything laid out in one place, which makes complex pipelines much easier to understand. I also like how flexible it is. You can build and customise workflows to fit pretty much any data pipeline, and the scheduling just works once it is set up. The UI is another big plus, it makes it easy to monitor runs, debug issues, and quickly see where something has failed without digging through logs for ages. **What do you dislike about Apache Airflow?** One thing I don’t like about Apache Airflow is that it can feel quite heavy and complex, especially at the start. Setting it up properly takes time, and there are quite a few moving parts to understand before everything runs smoothly. Debugging can also be frustrating at times. When something fails, the logs are not always the easiest to follow, so it can take longer than expected to figure out what went wrong. It can also be a bit resource intensive, which is not ideal if you are running smaller projects or just need something lightweight. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves the problem of managing and scheduling complex workflows in a clear and structured way. Instead of running scripts manually or relying on fragile cron jobs, it lets you define everything as a pipeline with dependencies, retries, and proper monitoring. For me, that means my data pipelines run automatically and reliably without constant oversight. It also makes it much easier to track what is happening, catch failures early, and fix issues quickly. Overall, it saves time and gives me confidence that my workflows are running as expected. ### 7. Powerful Orchestration for Complex Data Pipelines with Great Community Support **Rating:** 4.5/5.0 stars **Reviewed by:** Aindrila R. | Assistant System Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 03, 2026 **What do you like best about Apache Airflow?** For me, the standout feature is definitely the Web UI. As a data engineer, I often find myself troubleshooting, and the Grid view in Airflow makes it remarkably simple to identify exactly where a pipeline has failed. I can quickly access the logs for any specific task and determine what went wrong within seconds. This level of transparency is something that traditional cron jobs or basic scripts simply don't offer. Having a central dashboard for all your workflows truly provides peace of mind. **What do you dislike about Apache Airflow?** The main challenge is the ease of implementation for beginners. Setting up the infrastructure (like the webserver, scheduler, and database) requires a good bit of DevOps knowledge, which can be a hurdle for a small team. Because it’s open-source, you don’t have traditional customer support, so you rely heavily on the community. While the community is active, the documentation can sometimes be a bit overwhelming when you are trying to troubleshoot a very specific configuration issue. It’s a powerful tool, but the ease of use from a setup perspective could definitely be improved. **What problems is Apache Airflow solving and how is that benefiting you?** Before adopting Apache Airflow, our team faced significant challenges managing the complex dependencies among our various data scripts. We relied on simple cron jobs and manual triggers, which meant we often wouldn't discover a failed transformation until long after the fact. Airflow has addressed these issues by serving as our central 'source of truth' for automation. It orchestrates the sequencing of our tasks seamlessly, ensuring that Step B only begins once Step A has completed successfully. Personally, I've found that Airflow has greatly reduced the amount of time I spend on manual monitoring and troubleshooting. Rather than sifting through server logs to track down errors, I can simply consult the Airflow dashboard to pinpoint exactly where a task has failed. This shift has allowed me to dedicate more time to developing new data pipelines instead of just maintaining existing ones, resulting in a workflow that is both more reliable and scalable. ### 8. Flexible Workflow Management with Apache Airflow **Rating:** 5.0/5.0 stars **Reviewed by:** Atharva P. | Cloud BI Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is its flexibility in building and managing complex workflows using code. The DAG-based approach makes it easy to define dependencies, schedule jobs, and monitor pipeline execution in a centralized way. **What do you dislike about Apache Airflow?** Debugging failed workflows and managing dependencies across multiple tags can become challenging over time. Also, initial setup and maintenance can be complex, especially for large-scale deployments if it is not a managed service. Basic local setup is manageable, but production-grade deployment with scaling, monitoring, and high availability requires significant expertise. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves our workflow orchestration and scheduling challenges, automating workflows, triggering dependent jobs, managing retries, and coordinating data movement. This reduces manual intervention and improves pipeline reliability. ### 9. Streamlined ETL with Powerful DAG Visualization **Rating:** 5.0/5.0 stars **Reviewed by:** Dhanush R. | Senior Technical Customer Success Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Apache Airflow?** I love using Apache Airflow for creating ETL pipelines, especially with its UI-driven DAG visualization that makes understanding the workflow and dependencies so much easier. The graph and stage view are amazing, and identifying errors is straightforward. The task monitoring feature is really useful, letting me see which jobs are running, and the retry and failure handling are essential for smooth operations. Setup was straightforward, particularly on AWS, and it's incredibly helpful when integrated with Acceldata for pipeline observability. **What do you dislike about Apache Airflow?** The only thing I noticed, when there are more DAGs, is that the UI can sometimes feel slow when dealing with many tasks. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow helps us create a robust ETL pipeline, automates Spark job flows, and simplifies error identification with its UI. The DAG visualization aids in understanding workflows and dependencies. ### 10. industry standard for cron jobs **Rating:** 4.5/5.0 stars **Reviewed by:** Mitul C. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 27, 2026 **What do you like best about Apache Airflow?** the whole process is very extensible, since there are so many users, we have a lot of built in operators/plugins. the UI is very clean and intuitive. **What do you dislike about Apache Airflow?** i dont think there is a feature where we can watch the logs of all the jobs in one place, lets say we have 20-30 steps, so we have to go to each step and check the logs, so i would all logs to be consolidated **What problems is Apache Airflow solving and how is that benefiting you?** solves the problem of cron jobs and removes manual dependency. it is very reliable and any flaws also show up quite easily in the process if they do exist ## Apache Airflow Discussions - [What is airflow technology?](https://www.g2.com/discussions/what-is-airflow-technology) - 1 comment - [Is airflow a framework?](https://www.g2.com/discussions/is-airflow-a-framework) - 1 comment - [Is Apache airflow an ETL tool?](https://www.g2.com/discussions/is-apache-airflow-an-etl-tool) - 1 comment - [Who is using Apache airflow?](https://www.g2.com/discussions/who-is-using-apache-airflow) - 1 comment - [View Apache Airflow pricing details and edition comparison](https://www.g2.com/products/apache-airflow/reviews/apache-airflow-review-4826961?section=pricing&secure%5Bexpires_at%5D=2026-07-03+03%3A45%3A55+-0500&secure%5Bsession_id%5D=f36c92a1-38fb-4868-abea-044c8dda346c&secure%5Btoken%5D=f94f0fb30ba2b85b9890f4ad32b139f53b49435cd59de47b9a52e125287f37e3&format=llm_user) ## Apache Airflow Integrations - [Amazon EMR](https://www.g2.com/products/amazon-emr/reviews) - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [Amazon Simple Storage Service (S3)](https://www.g2.com/products/amazon-simple-storage-service-s3/reviews) - [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) - [AWS Bedrock](https://www.g2.com/products/aws-bedrock/reviews) - [AWS CloudFormation](https://www.g2.com/products/aws-aws-cloudformation/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Erisna](https://www.g2.com/products/erisna/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Google Cloud Data Fusion](https://www.g2.com/products/google-cloud-data-fusion/reviews) - [Google Cloud Storage](https://www.g2.com/products/google-cloud-storage/reviews) - [Kubernetes](https://www.g2.com/products/kubernetes/reviews) - [Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [OpenVAS](https://www.g2.com/products/openvas/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Python](https://www.g2.com/products/python/reviews) - [Slack Connector for Jira](https://www.g2.com/products/slack-connector-for-jira/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Tenable Nessus](https://www.g2.com/products/tenable-nessus/reviews) ## Apache Airflow Features **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Workflow Design & Integration - AI Orchestration** - Dependency Management - Workflow Coordination - Multi-Provider API Connectivity - Multi-Step Workflow Creation - Enterprise System Integration - Real-Time Data Pipelines **Management** - Cataloging - Monitoring - Governing - Model Registry **Operations** - Metrics - Infrastructure management - Collaboration **Performance Optimization & Analytics - AI Orchestration** - Workflow Performance Dashboards - Workflow Reporting - Resource Utilization Monitoring - Computational Resource Management - Dynamic Scaling - Component Monitoring **Management** - Cataloging - Monitoring - Governing **Governance & Compliance Controls - AI Orchestration** - Regulatory Compliance - Governance Policy Enforcement - Role-Based Access Control - Audit Trail Management - Security Protocols **Generative AI** - AI Text Generation - AI Text Summarization ## Top Apache Airflow Alternatives - [UiPath Agentic Automation](https://www.g2.com/products/uipath-agentic-automation/reviews) - 4.6/5.0 (6,110 reviews) - [Camunda](https://www.g2.com/products/camunda/reviews) - 4.5/5.0 (317 reviews) - [MuleSoft Anypoint Platform](https://www.g2.com/products/mulesoft-anypoint-platform/reviews) - 4.5/5.0 (646 reviews)