--- title: Apache Airflow Reviews meta_title: 'Apache Airflow Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 130 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: 130 scale: '5' date_modified: '2026-08-07' 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:** 130 ## 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** in Apache Airflow, facilitating efficient workflow creation and monitoring. (35 reviews) - Users appreciate the **intuitive dashboard** of Apache Airflow for effortlessly monitoring workflows and task statuses. (18 reviews) - Users highly value the **flexibility** of Apache Airflow, allowing for customized workflows via Python code. (13 reviews) - Users appreciate the **workflow automation** capabilities of Apache Airflow, simplifying complex data pipeline management. (10 reviews) - Users appreciate the **easy integrations** in Apache Airflow, making it flexible for connecting various systems and tools. (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** when installing Apache Airflow, especially on Windows systems, complicating the onboarding process. (13 reviews) - Users find the **learning curve challenging** , requiring time to grasp operators and manage workflows effectively. (9 reviews) - Users find the **steep learning curve** of Airflow challenging, especially with concepts and setup complexities. (8 reviews) - Users find the **learning difficulty** of Apache Airflow to be a barrier, particularly with Jinja and job setups. (6 reviews) - Users find the **outdated user interface** of Apache Airflow detracts from an otherwise reliable 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. Powerful workflow orchestration tool with great flexibility **Rating:** 5.0/5.0 stars **Reviewed by:** Aditya R. | Sofware Development Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2025 **What do you like best about Apache Airflow?** Apache Airflow makes it incredibly easy to design, schedule, and monitor complex workflows using Python. I like how it allows building DAGs in a very readable and modular way, which helps in managing large-scale data pipelines. The UI is intuitive and gives full visibility into task execution, retries, and logs. Its ability to integrate seamlessly with databases, cloud providers, and external services makes it very flexible for real-world use cases. The community support and available plugins also make it easy to extend functionality as needed. Customer Support is also good. **What do you dislike about Apache Airflow?** Airflow can be a bit challenging to set up and configure initially, especially when deploying in production with multiple workers and schedulers. Resource management and scaling sometimes require additional tuning, and debugging can be tricky for new users. The learning curve is steeper compared to some other orchestration tools, and the UI, while useful, could be more modern and responsive. However, once set up, it becomes stable and very reliable. **What problems is Apache Airflow solving and how is that benefiting you?** At my company, Apache Airflow has become the backbone of our automation and data workflows. Some examples of how we use it: Integration Management DAG: Automates the client ID and secret token lifecycle. Since tokens expire every three months, Airflow ensures timely reminders via email (daily during the last week before expiry) without any manual follow-up. Data Purging DAG: Handles automatic purging of client data after a configured time, which varies across clients. This helps us meet compliance requirements and optimize storage without manual monitoring. Data Migration DAG: Manages migration between hot and cold storage, ensuring cost-efficient and optimized data management. By orchestrating these processes in Airflow, we’ve significantly reduced manual overhead, ensured reliability with retries and alerts, and built a scalable workflow system that adapts to multiple client needs. ### 2. Pipeline and user management at the most **Rating:** 5.0/5.0 stars **Reviewed by:** Pedro P. | Visiting Professor, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** August 04, 2025 **What do you like best about Apache Airflow?** Airflow enhances pipeline observability - both process and data - to the highest level. It enables the distribution of pipeline execution among a team of stakeholders with varying technical backgrounds in a safe and user-friendly environment. **What do you dislike about Apache Airflow?** The installation, setup, and running are not straightforward, and some fine-tuning is necessary. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow provides comprehensive pipeline transparency (both code and data) while enabling secure distribution of pipeline execution and monitoring among stakeholders with diverse technical backgrounds. ### 3. Streamlining Supply Chain Workflows with Apache Airflow **Rating:** 5.0/5.0 stars **Reviewed by:** Abhishek K. | Senior Analyst, Retail, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2025 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is how it lets me orchestrate complex data pipelines in a very structured way. In supply chain demand planning, we deal with multiple data sources – sales, inventory, production, even external signals like holidays or weather. Airflow makes it easier to schedule, monitor and re-run these workflows without too much manual hassle. I also like the visibility it gives through the UI, it helps to quickly catch when a task is failing and why. For me, this saves a lot of time compared to writing adhoc scripts and cron jobs. **What do you dislike about Apache Airflow?** Sometimes Airflow can feel a bit heavy, specially when setting it up the first time. For smaller workflows it almost feels like an overkill, but in larger supply chain planning projects it pays off. The UI is good but can be slow when you have too many DAGs running. Also, learning curve is not trivial – it takes some time to get comfortable with operators, connections and handling backfills. I also wish the documentation had more real-world supply chain use cases instead of just generic ETL examples. **What problems is Apache Airflow solving and how is that benefiting you?** In my case, Airflow is solving the problem of reliably moving models from experimentation into production. In supply chain demand planning, I often need to retrain forecasting models on new sales and inventory data every few days. Airflow lets me automate this entire pipeline – from data extraction, cleaning, model training, evaluation, and final deployment into dashboards or APIs. This reduces manual steps and chances of mistakes. The biggest benefit is consistency – my models are always refreshed with the latest data without me babysiting the process. It also helps my team track failures quickly and re-run only the failed steps instead of the whole workflow, which saves us a lot of time. ### 4. Amazing for daily tasks **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.) **Reviewed Date:** October 12, 2023 **What do you like best about Apache Airflow?** Airflow is the most intuitive interface for setting up daily workflow jobs that I've come across. The API's are mostly easy to learn/use and it's all I love that it's all in Python. There are a few people on my team who are not trained programmers but they have figured out how to create simple daily jobs. The web interface is can be a bit obtuse but it gets the job done. Using the workflow visualizer makes debugging complex jobs much easier. **What do you dislike about Apache Airflow?** I wish it were easier to set up jobs that can be manually triggered. It can technically be done but the interface is clunky and lacks some basic quality-of-life features. The only complaint I have with the actual coding is that Jinja is hard to learn and debugging it can be a nightmare. That being said, if you stay within the straight-forward use cases, you shouldn't have any issues. **What problems is Apache Airflow solving and how is that benefiting you?** Apache handles all of our daily jobs in a much better way than Jenkins used to. It's easier to chain jobs together and see where the failure points are when edge cases arise. ### 5. Efficient and Reliable Workflow Orchestration with Apache Airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Anurag J. | Associate software Architect , Enterprise (> 1000 emp.) **Reviewed Date:** May 31, 2023 **What do you like best about Apache Airflow?** 1) Workflow Orchestration: Apache Airflow provides a powerful framework for defining, scheduling, and executing complex workflows. 2) Workflow Orchestration: Apache Airflow provides a powerful framework for defining, scheduling, and executing complex workflows. 3)Monitoring and Alerting: Airflow provides a user-friendly web interface that allows users to monitor the status and progress of their workflows. 4)Active Community and Ecosystem: Apache Airflow has a vibrant and active open-source community. 5)Mature and Production-Ready **What do you dislike about Apache Airflow?** 1) Learning Curve: Apache Airflow has a steep learning curve, especially for users who are new to workflow orchestration concepts or Python programming. 2) Complexity for Simple Use Cases: Airflow's feature-richness and flexibility can sometimes feel overwhelming for simple use cases. **What problems is Apache Airflow solving and how is that benefiting you?** 1) Complex Workflow Management: Airflow helps manage and coordinate complex workflows by providing a centralized platform to define, schedule, and monitor tasks and dependencies 2) Dependency Management: Airflow handles dependency management between tasks, ensuring that each task is executed only when its dependencies are met. 3)Scheduling and Retry Mechanisms: Airflow offers robust scheduling capabilities, allowing users to define precise schedules for task execution, including interval-based schedules, cron-based schedules, or specific trigger-based schedules. 4)Monitoring and Alerting 5) Parallel Execution and Scalability ### 6. Best tool for data flow **Rating:** 4.5/5.0 stars **Reviewed by:** Farhan K. | Big Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 10, 2023 **What do you like best about Apache Airflow?** The flexibility and customizability when it comes to creating and scheduling data pipelines. Uses python, which is the most popular programming language in the world. **What do you dislike about Apache Airflow?** Not really meant for streaming applications but it can be set up for those. Has a bit of a learning curve compared to other solutions. It only supports python for creating dags **What problems is Apache Airflow solving and how is that benefiting you?** It's very flexible as a data flow creator and there are a lot of callback mechanisms available for alerting, and fallback mechanisms. It's great for data engineers which is my field. ### 7. Airflow: Needs a refresher for the modern data stack **Rating:** 4.0/5.0 stars **Reviewed by:** Anup J. | Enterprise (> 1000 emp.) **Reviewed Date:** March 30, 2023 **What do you like best about Apache Airflow?** The best thing about Airflow is how versatile of a tool it is. Airflow can be used to build workflow on just about every database and tool and the sheer wealth of integrations it has is brilliant and just all-round useful **What do you dislike about Apache Airflow?** Learning Airflow is a seriously complicated task. And even that is not often enough to become truly good at it. The scheduling system is hardly intuitive. Versioning for the Dags and reverting them a very simple task in a competitor Prefect is not a part of Airflow at all **What problems is Apache Airflow solving and how is that benefiting you?** Batch orchestration and scheduling is the fundamental problem that Airflow solves. We use the Airflow operators to build DAGs(Directed Acyclic Graphs) which have parts of our processes, including preprocessing, training and monitoring. ### 8. What you should know about airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Adekunle M. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 06, 2023 **What do you like best about Apache Airflow?** It is a very great tool for job orchestration in the data processing pipelines. It helps me sleep well at night as i can scheduled my job prior before the time needed. **What do you dislike about Apache Airflow?** It requires a little complexity and technical background for optimal use . someone without the knowledge of programming cannot be confidently use this tool **What problems is Apache Airflow solving and how is that benefiting you?** Job Orchestration Task Scheduling ## 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?filters%5Bsentiment_snippet%5D=2214616&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+10%3A08%3A55+-0500&secure%5Bsession_id%5D=e05c79f0-499a-4f16-96a0-64c700899712&secure%5Btoken%5D=153c276752c87b046a89628973f81f9ea2cfcf703d5b561c37a12e602ec7ad64&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) - [AWS Bedrock](https://www.g2.com/products/aws-bedrock/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/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) - [Slack Connector for Jira](https://www.g2.com/products/slack-connector-for-jira/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Tenable Nessus](https://www.g2.com/products/tenable-nessus/reviews) ## Apache Airflow Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **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 (6,136 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 (657 reviews)