Legacy Scheduler Migration
Your scheduler wasn't
built for AI.
AI is only as reliable as the pipelines beneath it.
Your legacy scheduler is the weak layer.
Tools like Control-M, AutoSys, and Tidal were built for batch, not the event-driven, GPU-aware, agentic workflows production AI now demands. Astronomer moves you to Apache Airflow on your timeline, with AI migration tooling and expert professional services to de-risk the move.
The orchestration gap is now an AI problem.
Every AI and analytics initiative depends on pipelines that are reliable, observable, and governed. Legacy schedulers can't deliver any of the three.
Built for batch, not AI.
Legacy schedulers run time-based, linear job chains. They have no concept of event-driven triggers, GPU-aware scheduling, dynamic branching, or agent workflows — the patterns production AI requires. Fewer than 5% of AI projects reach production, and brittle orchestration is a core reason why.
Rising cost, fragmented tooling.
$500K–$2M+ annual licenses with 15–20% escalators, intensified by the Broadcom and Fortra acquisitions. 80% of enterprises run three or more automation tools across clouds, each with its own IAM model, cost structure, and visibility gap.
No observability.
Legacy tools produce job logs, not lineage. Failures surface when users complain, take hours to trace, and offer no cross-pipeline visibility. 98% of enterprises report SLA breaches driven by fragmented automation.
The destination is Apache Airflow.
The partner is Astronomer.
Airflow is the unified orchestration layer for data and AI workloads. It's the standard the leading AI foundation labs like OpenAI and Anthropic, AI-native companies like Notion and together.ai, and the Fortune 500 run on. It has the largest ecosystem, the deepest community, and ranks among the most active open source projects anywhere.
Astro is managed Airflow, so you get that standard without the burden of running it yourself. Airflow is also a bet on flexibility: open, pipelines-as-code, and vendor-agnostic, so the pain of getting off a legacy scheduler is one you never repeat.
GitHub stars
unique contributors
monthly downloads
pre-built modules
An AI-ready platform for the enterprise.
Astro gives your team a foundation built for data and AI, with the security and governance regulated industries require.
An AI-ready foundation
Reliable, observable, event-driven orchestration your AI and analytics initiatives can actually build on.
Lower, predictable cost
Eliminate escalating license and agent fees. Consolidate fragmented tools onto one consumption-based platform.
Faster delivery
New pipelines ship in hours, not weeks. Engineers build instead of babysitting jobs, with 10× faster pipeline creation.
End-to-end observability
Built-in lineage, an asset catalog, and AI-powered remediation cut troubleshooting time ~80% and reduce SLA breaches.
Security & governance
Outbound-only connectivity, fine-grained RBAC down to the individual pipeline, and credentials encrypted at rest. Deployment models that keep data and IP in your environment.
Astro
Fully managed across AWS, Azure, and GCP. Astronomer handles infrastructure, upgrades, and operations.
Astro with Remote Execution
Orchestration managed by Astronomer; execution inside your VPC. Outbound-only, zero inbound firewall rules, so data and IP never leave your environment.
Astro Private Cloud
Self-hosted entirely within your infrastructure, ideal for air-gapped or full on-premises requirements.
Migrating to Airflow is the right call. How you get there decides whether it lands in a quarter or drags on for years.
83% of migration projects fail on scope, disruption, or skills gaps. Only Astronomer delivers an AI agent that does the translation and expert services from the team behind Airflow.
AI-assisted translation
Otto, Astronomer's data engineering agent
Otto converts Control-M, AutoSys, Automic, Tidal, and other scheduler definitions into production-ready Dags. It maps every job dependency as it goes, eliminating the #1 source of migration surprises.
It carries Airflow best practices and your team's conventions, and produces deterministic output that traces back to each source job for validation before production.
Talk to Us About OttoExpert-led delivery
Astronomer Professional Services
We've guided hundreds of Airflow migrations and know where the complexity hides. A guided, phased program runs your legacy scheduler in parallel, so there's no cutover risk, and sequences waves that prevent scope creep. Embedded training means your team owns the platform when the engagement ends.
Explore Professional ServicesHow Astronomer Compares
vs. DIY open-source Airflow
Self-managing Airflow trades one operational burden for another. You're back to babysitting infrastructure within a year. Astro is managed Airflow, run by the experts that maintain the project.
vs. Amazon MWAA & Google Managed Service
Hyperscaler's Airflow services are slow to upgrade, opaque on failures, and locked to one cloud. Astro ships first access to new Airflow versions, native observability, and runs wherever you need it.
vs. rip-and-replace consultants
A systems integrator hands back converted jobs no one on your team can own. Otto plus Astronomer Professional Services build your conventions into the migration and leave your team self-sufficient.
Proven success across regulated industries.
Enterprises in financial services, healthcare, life sciences, and retail have moved off legacy schedulers onto Astro.
65K+ jobs migrated by Otto · 908 Dags consolidated · 2× ROI projected, 25% lower compute.
Standardized Airflow across eight previously independent teams, with full data sovereignty on Azure.
Bank-grade Airflow-as-a-service: 500+ deployments in production, 1,000+ engineers writing Dags, 4 legacy orchestrators consolidated.
Provisioning went from days to on-demand. Runs on Astro Private Cloud inside the bank.
Read the Case Study
95% faster MTTR with Otto-powered self-healing pipelines. 20% TCO reduction.
Quant and AI/ML pipelines ready at market open, with production-ready autonomous remediation stood up in eight weeks.
Read the Case Study
Replaced Control-M with Python-native, observable orchestration.
Dependable, on-time data across Snowflake and Databricks, powering analytics for 300,000+ policyholders.