24/7 Apache Airflow Support with a 15-Minute Emergency SLA

Our Airflow support covers 24/7 emergency incident response with a 15-minute SLA, root cause analysis on every P1, scheduler and executor tuning, upgrade and compatibility advisory, quarterly capacity reviews, and migration or architecture support. Every ticket is handled by a named senior engineer — there is no tier-1 triage layer and no per-incident charge.

Yes — this is one of the most common Airflow incidents we handle. It's almost always one of three things: scheduler heartbeat failure, max_active_runs saturation on a specific DAG, or executor slots consumed by zombie tasks that never released after a worker crash. We check all three in the first diagnostic pass.

Yes, alongside self-managed Airflow on Kubernetes or bare infrastructure. Managed variants remove some operational surface but not the ones that cause most incidents — DAG design, executor slot exhaustion, and metadata DB connection pressure are still yours to manage, and still where we spend most diagnostic time.

Yes. As an extension of the support contract, AceMQ runs managed Airflow on your own infrastructure: Airflow on Kubernetes with the official Helm chart, on virtual machines or in Docker stays in your account, and we take on the day-2 operations. That means monitoring the scheduler, executors, workers and metadata database, holding the pager, applying patches, planning and running upgrades, and reviewing capacity as DAG volume grows. Your team keeps writing DAGs; keeping the platform under them healthy becomes our job.

Astronomer's Astro, Amazon MWAA and Google Cloud Composer are managed Airflow services: the provider runs the Airflow environment on its platform, offers the Airflow versions it supports, and bills for the environment. They are a good fit for a new deployment or a team already standardised on that cloud. Moving an existing deployment onto one is a migration, and DAG design, dependency conflicts and executor tuning still sit with you. AceMQ's managed operations keep Airflow where it already runs, on versions and configuration you choose, and cover that DAG-level layer too. If you already use one of those services, our support covers it as it is.

P1 emergencies — the scheduler is down or a business-critical DAG chain is stuck — get a 15-minute first response with a senior engineer joining a live bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual, not best-effort targets.

Yes. Heavy top-level code in a single DAG file — API calls or expensive imports executed at parse time rather than inside a task — runs on every scheduler heartbeat and delays parsing for every other DAG in the instance. We identify the offending DAG and refactor the parse-time logic out of it.

Yes. Airflow 2.x's DAG serialization requirements break certain dynamic patterns that worked under 1.x, and provider package version pins are a frequent source of breakage after an upgrade. We validate and fix custom operators as part of upgrade support.

Apache Airflow is the open source platform created by the community to programmatically author, schedule and monitor workflows as Python DAGs. The Airflow community supports current versions with bug fixes and offers help on GitHub and Slack, with no SLA; Airflow 2 reached end of life in April 2026. AceMQ's support service covers self-managed Airflow on Kubernetes with the official Helm chart, on virtual machines, and in Docker, plus Amazon MWAA, Google Cloud Composer and Astronomer where the provider runs the platform and nobody runs your DAGs. In scope are the scheduler, executors, workers and metadata database, task instances stuck in queued, slow DAG parsing, dependency conflicts between providers and your own Python packages, versioning and upgrade planning across Airflow versions, and security and compliance reviews of connections, secrets and access.

Yes. Airflow 3 is an architectural release: tasks no longer talk to the metadata database directly, the REST API changed, SubDAGs and other deprecated features were removed, datasets became assets, and a newer Python version is required. The project recommends moving to the last 2.x release first and clearing deprecation warnings there, and ships a linter that flags incompatible DAG code, which turns the migration into a measurable backlog. Estates still on 1.10 go through 2.x on the way. We run the migration with your team: environment build, DAG code review, provider version alignment, a parallel run against production schedules, and a cutover with a tested rollback.

Yes. AceMQ works on Apache Airflow in two ways: ad hoc engagements for a defined piece of work, such as a health check, an upgrade or an incident, or an annual partner engagement that covers 24/7 support, proactive reviews and named engineers. Many teams start ad hoc and move to an annual partnership once the first engagement is done.

Every AceMQ proposal is tailored to your requirements: your Apache Airflow environment, the SLA you need, the hours of cover and the scope of work. Most contracts run $24,000–72,000 a year, depending on the SLA, the scope and how Apache Airflow is deployed, with aggressive discounts when several technologies share one contract. Request a quote and AceMQ replies within 1 hour.