☰

Astronomer.io is an American data operations technology company that specializes in AI and offers a unified DataOps platform primarily powered by Apache Airflow. Its flagship product is Astro, a fully managed platform designed to simplify the deployment and management of Apache Airflow at scale for enterprises.

What Astronomer.io and Astro Do

Astronomer’s core offering, Astro, simplifies the complexities of building, running, and monitoring data pipelines by providing a managed service for Apache Airflow. This allows data engineers, data scientists, and MLOps teams to focus on building robust, reliable workflows without the operational overhead of managing infrastructure.

Apache Airflow, the open-source foundation of Astro, is a powerful workflow orchestration tool developed by Airbnb in 2014, open-sourced in 2015, and later became a top-level project under the Apache Software Foundation. It enables users to programmatically author, schedule, and monitor workflows as Directed Acyclic Graphs (DAGs) written in Python.

Key features of Apache Airflow include:

  • Workflows as Code (DAGs): Define tasks and their dependencies in Python, making workflows modular, dynamic, and version-controllable.
  • Pipeline Scheduling: Automates workflow execution based on defined schedules, including complex timetables or data-driven triggers.
  • Dependency Management: Ensures tasks run in the correct order, handling failures and retries automatically.
  • Real-time Monitoring: Provides visibility into workflow health and performance through a web-based user interface.
  • Scalability: Can handle workflows at any scale, supporting multiple executors (like Celery and Kubernetes) for task distribution.
  • Extensibility: Allows users to extend functionality with custom operators, sensors, and hooks, integrating with various external APIs, cloud services, and databases.
  • Cloud-Native Integration: Seamlessly integrates with major cloud platforms like AWS, Google Cloud, and Azure, with over 1,500 pre-built modules.

Astro’s Advantages and Enterprise Features

Astro enhances standard Apache Airflow by offering a fully managed platform with enterprise-grade features. This means Astro automates the configuration, scaling, and maintenance of Airflow components, reducing the operational burden on internal teams.

Specific benefits and features of Astro include:

  • Simplified Management: Eliminates the need for manual configuration and scaling of Airflow components, such as the Scheduler, Workers, and Executors, by handling all infrastructure management automatically via Astro Runtime.
  • Dynamic Scaling: Automatically adjusts resource allocation based on workload demands, ensuring efficient task execution without manual configuration or over-provisioning. Astro runs on Kubernetes clusters, enabling dynamic scaling.
  • Enhanced Observability and Monitoring: Offers advanced monitoring tools, real-time alerts, and dashboards for insights into pipeline performance and system health. Astro Observe is also available for unified data pipeline health, lineage, and cost.
  • Security and Governance: Provides robust built-in security features, including role-based access control (RBAC), audit logging, SAML-based SSO, and network isolation, which are crucial for compliance in regulated industries.
  • Multi-Environment Support: Facilitates the management of isolated environments (e.g., development, staging, production) within a single platform, unlike self-hosted Airflow, which typically operates in siloed environments.
  • Cost Optimization: Features like hibernating development deployments reduce compute costs when not needed, and a pay-as-you-go option is available. Worker queues allow for task-optimized compute, enabling resource-intensive tasks to use higher-performing workers only when necessary, optimizing costs.
  • Developer Experience: Improves on Airflow’s native experience with an intuitive user interface, improved DAG management system, and a streamlined command-line interface (CLI). It also provides a Registry of certified integrations for various tools in the data and ML stack.

Pricing

Astronomer’s pricing is usage-based, determined by your Airflow cluster type, deployment sizing, and worker compute. Networking costs from the cloud provider are passed through.

Astro offers several pricing tiers tailored for different team sizes and needs:

  • Developer: Starts at $0.35/hour for deployments, with pay-as-you-go billing and features like flexible, scale-to-zero compute and hibernating deployments. A free one-month trial with $300 in credits is available, where a small instance running continuously might consume around $272.84.
  • Team: Starts at $0.42/hour for deployments, including all Developer features plus network isolation, dedicated clusters, audit logging, high availability deployments, and 24×5 support.
  • Business & Enterprise: Custom pricing with annual agreements for mission-critical pipelines requiring 24×7 support, SSO/CI/CD enforcement, custom RBAC, and organization dashboards.

A specific example indicates that the smallest deployment (up to 50 DAGs) on GCP in the EU region would cost around $300. Pricing includes scheduler cost, but worker uptime is charged per hour and typically forms a larger part of the overall cost. Worker sizes range from A5 (1 vCPU, 2 GiB memory) at $0.13/hour to A160 (32 vCPU, 64 GiB memory) at $4.16/hour. Costs for Kubernetes Executor and Kubernetes Pod Operator are billed based on the equivalent A5 workers allocated.

Use Cases

Astronomer.io’s Astro platform is used for automating a wide range of complex workflows across various industries. It is particularly valuable for:

  • Data Pipelines (ETL/ELT): Extracting, transforming, and loading data.
  • Machine Learning Workflows (MLOps): Automating training, evaluation, and deployment of models.
  • Generative AI Applications: Orchestrating workflows for assistance and support automation (e.g., LLM-powered chatbots, like Astronomer’s own Ask Astro), content generation (e.g., automating timekeeping and billing, summarizing support cases), reasoning and analysis (e.g., re-routing support tickets, categorizing and assigning engineering tasks), and app development (e.g., code generation).
  • Cloud Services Orchestration: Provisioning and scaling infrastructure, scheduling API calls, and integrating with tools like dbt, Snowflake, Databricks, and various ML/AI frameworks.

Notable customers and use cases include:

  • The Texas Rangers baseball team, who use Astro to accelerate their live game analytics pipeline, reducing completion time by over 80% (from 20 minutes to 3 minutes) through worker queues and data-aware scheduling, enabling immediate post-game analytics for players and coaching staff.
  • Laurel, an AI company, utilizes Astro for managing massive data for its machine learning models, automating timekeeping and billing, and creating text summaries.
  • Dosu, an “AI teammate” for engineers, relies on Airflow via Astro to ingest and index vast amounts of information for its AI-based platform, ensuring its reasoning is useful and accurate.
  • Major companies like Northern Trust, Adobe, Ford, Activision, and Marriott are listed as customers.

Company Background and Funding

Astronomer was founded in 2015 by Paola Peraza Calderon, Viraj Parekh, and Pete DeJoy, with its headquarters in New York City. The company has secured over $375 million in funding across multiple rounds, including a $93 million Series D in 2025, reaching a valuation of around $1 billion. Prominent investors include Bain Capital Ventures, Insight Partners, Salesforce Ventures, and Meritech Capital. In July 2025, CEO Andy Byron resigned following a public scandal.

Comparisons and Criticisms

Astronomer’s managed Airflow solution is often compared to:

  • MWAA (AWS Managed Workflows for Apache Airflow): While MWAA is a competitor, some users find Astronomer to be miles better than AWS or GCP’s offerings due to easier deployments, better support, and earlier upgrades. MWAA has been criticized for being expensive for its size, lacking observability on worker usage and costs, and being slow to update Airflow versions.
  • Google Cloud Composer: Similar to MWAA, it’s suitable for GCP users but can be expensive, slow to update Airflow versions, and has been cited for “horrendous” support.
  • Self-hosting Apache Airflow: This option offers full control and is free to use but requires significant operational overhead, deep technical expertise in infrastructure, and continuous maintenance. For smaller teams, the development time and maintenance cost of self-hosting can outweigh the savings compared to a managed service.
  • Other Data Orchestration Tools: The broader market includes tools like Apache Airflow (open source), Luigi, Prefect, Dagster, ActiveBatch, RunMyJobs, Keboola, Rivery, and Zapier. Competitors like Orchestra offer a unified control plane for data ops with a different approach, being less Airflow-centric and more focused on GUI-driven or declarative analytics workflows, believing the future of data ops is AI-native.

Despite its benefits, Astronomer faces some criticisms:

  • Cost: Many users perceive Astronomer as expensive, especially at scale, for a service built on open-source software. Some suggest that resources would be better spent hiring someone to maintain a Kubernetes cluster with Airflow.
  • Value Proposition: Some in the data engineering community question its unique value beyond being a “managed Airflow” provider, arguing it offers “features you didn’t know you need (and probably don’t)” or that its offerings are “buzzword-heavy”.
  • “Shiny Toy Syndrome”: Some companies, particularly C-suite executives, are seen as adopting Astronomer due to persuasive sales pitches or a desire to pivot to “shiny new things” like “Generative AI” buzzwords, rather than a genuine technical need.
  • Vendor Lock-in/Priorities: There are concerns that as the company receives more VC funding, priorities might shift towards developing products that lock users into their platform, moving away from improving the core managed Airflow experience.

Overall, Astronomer.io provides a robust, managed solution for Apache Airflow, offering significant advantages in scalability, security, and ease of management for enterprises and teams that prefer to offload infrastructure complexities. However, potential users should carefully weigh its costs against their specific operational needs and consider alternatives like self-hosting or other managed services, depending on their technical resources and budget.

Think of Astronomer.io as a luxury cruise ship for your data pipelines. While you could build your own, perhaps more efficient, sailboat (self-hosting Airflow) or take a public ferry (AWS MWAA/GCP Composer), Astronomer offers a fully managed, premium experience with all the amenities (security, scalability, support, monitoring) taken care of, allowing you to simply enjoy the journey of data orchestration without worrying about steering or maintenance. However, this convenience comes with a higher ticket price, which might not be justifiable for every traveler.