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SAShrijal Acharyafreecodecamp.org3d ago · 20 min read

How to Build a Durable, Autoscaling AI Agent with Temporal, Composio, KEDA, and Kubernetes

Most AI agents are great at quick tasks. Send a message, the agent calls a few tools, and you get a response back in seconds. That works perfectly when you're asking it to summarize a document or do s

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AAAyobami Agboolaayobamidevops.hashnode.devJun 1 · 11 min read

Building an Observable, Self‑Healing Cluster

There's a gap in how most teams operate Kubernetes. Dashboards get built, alerts get configured, and runbooks get written. But when something goes wrong at 2am, someone still has to wake up, read the

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ARAshwaq Rehemanashwaq.hashnode.devMay 23 · 5 min read

2,300+ idle pods, one fix: KEDA HTTP scale-to-zero

Every time a user opens our platform and hits Create, we spin up a pod. That pod is their workspace, the coding agent runs in it, their project files live on the storage attached to it, every message

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Hhareshv0711haresh-v.hashnode.devMay 10 · 10 min read

Containerized self-hosted ADO agents using AKS and KEDA

About the project Modern CI/CD workloads are dynamic. During peak deployment hours, build queues increase rapidly, while during idle periods, infrastructure often remains underutilized. Managing self-

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SASaeed Anwar Ansarisaeed.hashnode.devMar 4 · 8 min read

Mastering KEDA on GKE: A Deep Dive into Event-Driven Autoscaling

If you are running workloads on Google Kubernetes Engine (GKE), you are likely familiar with the Horizontal Pod Autoscaler (HPA). HPA is great for scaling based on standard CPU or memory metrics. But

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SKSaurav Karkisauravkarki.hashnode.devFeb 12 · 13 min read

from-devops-to-mlops-building-production-ready-ml-pipelines

Introduction As a DevOps engineer, I’ve practiced developing CI/CD pipelines, managing Kubernetes clusters, and ensuring reliable deployments. But when machine learning entered the picture, I realized

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Ttaruntarungampa.hashnode.devJan 13 · 3 min read

Simplifying Kubernetes: Scale Your Applications Easily

If you’re just starting out with Kubernetes, you’ve probably realized that "scaling" is one of its biggest selling points. But then you look at the documentation and see HPA, VPA, and KEDA. Which one should you use? 1. Horizontal Pod Autoscaler (HPA)...

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JLjoseph lamadevopsthread.hashnode.devNov 1, 2025 · 3 min read

Demystifying KEDA in terms of Helm

KEDA: Kubernetes Event Driven Auto-Scaling KEDA enables Kubernetes workloads to scale not only based on CPU or memory usage, but also on external events such as messages in RabbitMQ or Kafka, Azure Queue length, Prometheus metrics, and more. In ess...

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AKAniket Kharpatilaniket-on-cloud9.hashnode.devSep 14, 2025 · 9 min read

Scaling Kubernetes Like a Pro: Hands-On with HPA, KEDA & Karpenter

In modern cloud-native environments, scalability isn’t a luxury — it’s a survival skill. Kubernetes makes scaling workloads and infrastructure seamless, but the real magic happens when you combine pod-level and node-level autoscaling strategies. In t...

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AMAntonin Marxerantoninmarxer-fr.hashnode.devSep 12, 2025 · 15 min read

Des datacenters carbon-aware — Décarbonner les entraînements de modèles IA

Selon le dernier rapport de l'Agence Internationale de l'Énergie (AIE), les datacenters représentaient 1,5 % de la demande mondiale d'électricité en 2024, avec 10 % de cette demande liée à l'intelligence artificielle. L'AIE prévoit que les datacenter...

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Search Hashnode

#keda

Explore Hashnode

Trending tags this week

How to Build a Durable, Autoscaling AI Agent with Temporal, Composio, KEDA, and Kubernetes

Building an Observable, Self‑Healing Cluster

2,300+ idle pods, one fix: KEDA HTTP scale-to-zero

Containerized self-hosted ADO agents using AKS and KEDA

Mastering KEDA on GKE: A Deep Dive into Event-Driven Autoscaling

from-devops-to-mlops-building-production-ready-ml-pipelines

Simplifying Kubernetes: Scale Your Applications Easily

Demystifying KEDA in terms of Helm

Scaling Kubernetes Like a Pro: Hands-On with HPA, KEDA & Karpenter

Des datacenters carbon-aware — Décarbonner les entraînements de modèles IA