Kubernetes

Running video transcoding on Kubernetes maps the workload's natural shape — variable-throughput, queue-driven, periodic-spike — onto K8s + KEDA primitives. These articles and reference architectures cover the four-pattern climb (Job per encode → worker Deployment + queue → KEDA queue-depth autoscaling → multi-tenant operator), pool-isolation strategies, leader election, drain semantics for upgrades without dropped encodes, and where each pattern stops scaling. For SREs and platform engineers running video on K8s.

Reference architectures · 2

Cost-aware spot-instance encoder pool
Production architecture for running video transcoding on AWS Spot, GCP Preemptible, and Azure Spot instances. Interruption-tolerant queue topology, fleet diversification, atomic upload semantics, and the cost math that makes self-hosted video pipelines beat per-minute pricing at scale.
May 9, 2026
Kubernetes deployment with KEDA autoscaling
Production K8s topology for MpegFlow — API tier, shared workers via Helm, dedicated workers via Operator, KEDA queue-depth autoscaling, leader election, pool pause for cost savings.
May 5, 2026

Engineering blog · 1

FFmpeg in Kubernetes: the pod, queue, and operator pattern
How to run FFmpeg in Kubernetes at production scale — the four patterns we've watched teams climb, where each breaks, and why a video transcoder Kubernetes operator earns its keep above ~50K jobs/day.
May 8, 2026

Adjacent topics

Autoscaling· 2
Operator· 2
Spot instances· 1
cost optimization· 2
FFmpeg· 3