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Blogs

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The Gotchas of OTEL collector processors for effective observability in K8s

Sanket Rajgiri

SRE @One2N

Spandan Ghosh

Content @One2N

Struggling to make sense of OpenTelemetry Collector processors for real-world projects? This blog breaks down what each OTEL processor actually does, where it matters, and shares real lessons from messy SRE problems like taming noisy data, surviving crashes, and staying under cost limits in Kubernetes.

January 26, 2026 | 6 min read

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Karpenter vs Cluster Autoscaler: a comprehensive guide to Kubernetes Node Scaling

Rahul H Bhatia

SRE

Learn what the key differences are between Karpenter and Cluster Autoscaler for Kubernetes node scaling, how each tool handles dynamic workloads, node provisioning, and cost optimization, to choose the best solution for your cloud-native environment.

November 8, 2024 | 7 min read

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Horizontal Pod Autoscaling in Kubernetes based on external metrics, using Prometheus adapter

Akshay Srivastava

SRE @One2N

Learn how to leverage Kubernetes for efficient auto-scaling in micro-service architectures. This blog explores Horizontal Pod Autoscaling (HPA) using internal and external metrics, including the setup of Prometheus Adapter for monitoring external Redis servers.

October 14, 2022 | 6 min read

Case Studies

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Pull Request Based Ephemeral Preview Environments on Kubernetes

Problem Statement. Set up Heroku PaaS style, ephemeral Preview environments for all frontend and backend services on Kubernetes Allow a Preview service to use another service in Preview mode as a dependency Configure the latest migrations and seed data in Preview environments, reducing the time to test a Pull Request Allow automated deletion of the Preview environment if it’s not actively used

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Pull Request Based Ephemeral Preview Environments on Kubernetes

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Pull Request Based Ephemeral Preview Environments on Kubernetes

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

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Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

Read Case Study

Journey of moving VM-based workloads to Kubernetes

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

Read Case Study

Journey of moving VM-based workloads to Kubernetes

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

Read Case Study

AutoScaling eKYC Machine Learning workloads to 2M+ req/day

Cloud monthly costs were reduced to a maximum of $10k per month (90% savings over predicted costs without the Autoscaler). The solution has been live and running successfully on production for 2+ years. SLA for uptime and response time are met during this period.