KEDA
Why CPU-Based Autoscaling Fails for Rails — and What We Used Instead
Kubernetes autoscaling works best when teams match the metric to the workload: queue latency for synchronous web traffic, queue depth for background jobs ...
Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems
KEDA can scale Kubernetes AI workloads on real demand signals such as queue depth, helping model-serving and agent workloads respond faster while reducing idle compute costs ...
Stop Treating GPUs Like Web Pods
Kubernetes schedules accelerators as opaque integers, and your bill pays for it. Share the silicon, scale on the right signal and keep weights out of the image ...
Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA
The rush to deploy Large Language Models (LLMs) and generative AI has created a massive infrastructure bottleneck. Platform engineering teams are spinning up expensive GPU node pools on Kubernetes, but they are ...
Deploying Docker AI Agents on OCI and OKE
This guide details the architectural transition of AI agents from experimental scripts to "first-class production workloads" using Oracle Cloud Infrastructure (OCI) and Oracle Kubernetes Engine (OKE). It emphasizes a zero-trust, scalable approach ...
