Stop rebuilding the same
Spark control plane
Teams running Spark on EKS eventually build the same control-plane layer and carry its maintenance forever. SparkPilot exists so they don't have to — and so the changes that layer governs come with proof instead of hope.
The gap nobody was filling
When a Spark workload moves — a runtime upgrade, a platform migration, a dependency change — the tools around it can say the job ran. None of them can say the results are still right. Runtime optimizers make jobs cheaper, FinOps platforms show you what you spent, observability tools diagnose failures after the fact — and the cutover still gets signed off on release notes and optimism.
That is the gap SparkPilot fills: it runs the same workload on both sides of a change and proves the outputs match before you cut over — with a comparator that is proven able to fail.
The same control plane governs day-to-day execution on the runtimes you already run on AWS: every submission is checked against governance and cost rules before dispatch — before IAM misconfigurations waste startup cost, before a team blows its monthly budget at 2 AM, before a bad release label causes a silent failure.
How we work
We keep product claims grounded in what customers can use now. We would rather be clear and reliable than broad and vague.
Observability tools tell you what went wrong after a job ran. We prevent the bad run from starting. The value is in the gate, not the dashboard.
SparkPilot runs in your AWS account. Your Spark job artifacts, S3 buckets, and VPC stay in your perimeter. BYOC is the foundation.
Data engineers are the end users. Platform engineers are the buyers. We build for the person who has to set up IRSA bindings at midnight, not just the person who submits jobs.
How we got here
First preflight engine shipped with IAM, OIDC, and quota checks before job dispatch.
First production Spark job ran through SparkPilot on a live EKS cluster.
Extracted the Python SDK and the Airflow and Dagster providers into installable packages — preview, source-available to pilot customers, not yet published to a package index.
Ran the reference migration end to end on EMR Serverless: the same TPC-H workload across two Spark runtimes, byte-identical outputs, and a seeded negative control that correctly returns DO_NOT_CUT_OVER.
Want to scope your pilot architecture?
We can walk through architecture details, scope your pilot, and confirm technical fit in one call.