KEDA
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 ...
Veera Ravindra Divi | | AI infrastructure, AI serving, autoscaling, cloud costs, cloud native AI, DCGM exporter, DRA, Dynamic Resource Allocation, GPU costs, GPU scheduling, GPU sharing, GPU utilization, GPUs, inference workloads, KEDA, kubernetes, Kubernetes GPU scheduling, LLM Inference, MIG, model weights, MPS, NVIDIA GPUs, NVIDIA MIG, Prometheus, scale-to-zero, time-slicing
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 ...
Pavan Madduri | | Agentic Architecture, AI, AI agents, containerization, Data Minimization, docker, Event-Driven Autoscaling, GitOps, infrastructure as code, kagent, KEDA, Kubernetes CRD, Kyverno, LLM Inference, MCP server, Model Context Protocol, oci, OCI Generative AI, OCI Vault, OCIR, OKE, OpenTelemetry, Oracle Kubernetes Engine, Production Workloads., Terraform, Virtual Nodes, Zero-Trust Security

