Discover your savings with Astronomer.

Astro IDE

Ship Airflow pipelines in minutes, not days. The only AI-native IDE for data engineers auto-generates, tests, and debugs DAGs using context from your code, configs, and observability metadata.

Astro CLI

Streamline local pipeline development. Developers run and debug Airflow DAGs locally previewing changes instantly without consuming shared infrastructure or waiting on deploys.

Branch-Based Deploys

Accelerate testing and improve code quality with lower overhead. Spin up isolated dev environments to run, test, and deploy pipeline branches safely without affecting production or maintaining long-lived staging infrastructure.

CI/CD

Cut manual release effort and risk. Automate reliable code deployment, enforce reviews, promote code across environments, and ensure testing. Integrated with your favorite tools.

Central Connection Management

Reduce setup time and eliminate licensing costs. A centralized secrets backend manages all credentials across deployments, avoiding duplication and cutting reliance on external vaults.

Hypervisor Autoscaling Infrastructure

Maximize throughput while lowering compute costs. Purpose-built engine dynamically right-sizes resources and doubles Airflow task execution, delivering more performance per core without manual tuning.

Scale-to-Zero Deployments

Eliminate idle infrastructure costs. Non-production environments automatically hibernate when inactive, reducing spend to zero during off-hours.

Dynamic Workers

Match capacity to demand: no overprovisioning. Worker nodes scale up and down automatically, ensuring you only pay for the compute you use.

Task-Optimized Worker Queues

Run tasks efficiently and eliminate waste. Separate queues for heavy and light workloads prevent over-provisioning, optimizing resource usage and reducing cloud CPU and memory costs.

Event-Driven Scheduling

Cut compute costs and enable real-time data workflows. Event-driven pipelines eliminate constant polling and rigid schedules, triggering instantly on data arrival to save resources.

Remote Execution Agents

Reduce cloud spend with flexible workload placement. Run compute-heavy tasks or sensitive data pipelines on lower-cost infrastructure whether on-prem, in GPUs or in cheaper cloud regions. Minimize egress fees and avoid premium pricing.

Multi-Zone High Availability

Prevent zone‑level outages and missed SLAs. Replicated schedulers, DAG processors, and web servers run across multiple nodes and zones, keeping pipelines active even if one zone fails.

In-Place Upgrades + Deployment Rollbacks

Reduce downtime and engineering overhead during upgrades. In-place Airflow upgrades and instant rollbacks let teams update environments without rebuilds and recover from failed deployments. Minimize outages, maintenance windows, and effort.

dbt Orchestration with Cosmos

Eliminate redundant tooling and lower platform costs. Runs dbt projects natively in Airflow with 10 lines of code. Gain full control over transformations in a single, scalable orchestration layer and saving hundreds of thousands of dollars.

DataOps Consolidation

Cut tooling costs and simplify your data stack. Replace fragmented observability, lineage, quality, and cost tools with a single platform. Eliminate redundant licenses, integration effort, and brittle custom glue code.

Unified Asset Catalog + Popularity Scores

Optimize spend by focusing on high-value data. Table popularity scores highlight the most-used assets so teams can prioritize storage, caching, and quality efforts where they deliver the greatest return.

SLA Monitoring & Proactive Alerting

Avoid SLA breaches and costly emergency reruns. Custom alerts flag freshness and latency risks early so teams can intervene before issues escalate and disrupt downstream data products.

End-to-End Lineage & Pipeline Visibility

Accelerate root cause analysis and avoid rework. Task-level lineage shows exactly how data flows across pipelines, making it easy to trace issues, assess impact, and prevent unnecessary recomputations.

AI Log Summaries

Faster incident resolution. Generates instant, plain-language root-cause reports, compressing MTTR and the costly engineer hours that go with it.

Data Quality Monitoring

Catch bad data early and prevent downstream failures. Inline checks for volume, schema, and completeness run with pipeline context, making it easy to trace issues and avoid expensive reprocessing.

Data Warehouse Cost Management

Cut platform costs with pipeline-level attribution. Usage and spend are mapped directly to DAGs, tasks, and assets so teams can detect anomalies, uncover trends, and eliminate waste tied to specific workflows.