Cloudchipr Delivers 99.99% Uptime and 60% Cost Reduction with Kubernetes, AWS, GitOps, and FinOps Automation
Challenge
Cloud costs spiked unpredictably on legacy PaaS hosting while services crashed during peak usage, threatening strict enterprise SLAs. The team needed cloud-agnostic infrastructure with cost visibility and automated scaling, without downtime or disrupting developer workflows.
Solution
Cloudchipr migrated the platform to Kubernetes on AWS EKS with GitOps automation (Argo CD, Helm), intelligent autoscaling (KEDA, Karpenter), and comprehensive FinOps governance, delivering 99.99% uptime and 60% cost reduction.
Impact
Cloudchipr partnered with an enterprise AI-powered learning platform to stabilize operations and optimize spend. By moving to Kubernetes on AWS with GitOps workflows and FinOps automation, the team achieved 99.99% uptime and reduced cloud costs by ~60%.
Projects used
By the numbers
60% cloud cost reduction
through intelligent autoscaling, FinOps automation, and resource optimization.
99.99%
uptime
200+ Kubernetes nodes
successfully scaled and tested to handle peak
How an Enterprise AI Learning Platform Scaled Without Outages or Budget Surprises
Cloudchipr’s client – an enterprise AI-powered learning platform – operated its stack on a Platform as a Service (PaaS) managed hosting provider, when growth started causing problems. As usage and system load climbed, the team ran into several critical challenges: cloud costs kept spiking unpredictably, services crashed during peak periods, and executives were demanding strict reliability SLAs.
To make matters worse, cost attribution and unit economics calculations became difficult due to inconsistent resource tagging and idle cloud resources, making it nearly impossible to answer basic questions like “what does it cost per active learner?”
The customer needed to rebuild their entire cloud infrastructure, swap providers, and implement cost governance, all without any downtime or disrupting their engineers’ daily workflow.
Peak traffic exposed scaling gaps and slow rollbacks, while cloud spend fluctuated without clear attribution. We needed a standards-based way to stabilize reliability and costs.
— Norayr, SRE Engineer, Cloudchipr
Building Cloud Native Resilience with Kubernetes and GitOps
Cloudchipr’s team orchestrated a phased workload migration from the customer’s existing PaaS hosting to Kubernetes on AWS EKS, implementing a fully standardized GitOps workflow. This new architecture gave the platform cloud-agnostic infrastructure with intelligent autoscaling and streamlined GitOps release processes. The team built comprehensive observability and alerting systems, while the Cloudchipr FinOps platform enforced cost hygiene through consistent tagging policies and automated resource cleanup across all cloud environments.
Solution Approach
The team deployed AWS (EKS) with multi-AZ worker nodes as the foundation. Stateless services run behind an ingress controller with horizontal pod autoscaling, while asynchronous workers process queues in the background. For stateful workloads, the architecture uses managed data services alongside Kubernetes StatefulSets when needed. The platform achieves resilience through rolling updates, PodDisruptionBudgets that protect capacity during node rotations, pod anti-affinity rules to distribute workloads, automated backups, and documented and clear RPO/RTO targets.
Platform and Resilience
- EKS with private networking and multi-AZ worker nodes, per-environment isolation.
- Workloads: Stateless deployments, async workers/cron jobs, and selective stateful components; managed DB/storage.
- Resilience: rolling/blue-green updates, PDBs/anti-affinity, backups, and restore/runbooks aligned to SLOs
Orchestration and Autoscaling: How the Platform Scales Automatically Without Waste
Kubernetes provides the core orchestrator layer with declarative manifests, self-healing, rolling/blue-green updates, and isolation via namespaces/RBAC/network policies. The platform isolates workloads using namespaces, RBAC policies, and network policies. The Horizontal Pod Autoscaler scales applications based on CPU, memory, or custom metrics, while PodDisruptionBudgets protect capacity during maintenance windows and node rotations.
KEDA enables event-driven autoscaling by watching external signals like queue depth, stream lag, and HTTP throughput. This lets the platform scale out workloads during traffic bursts and scale idle workers down to zero, eliminating over-provisioning costs.
Karpenter automatically provisions nodes on demand and consolidates underutilized capacity to reduce waste. It supports both on-demand and Spot instances while respecting PodDisruptionBudgets and topology constraints to maintain reliability.
GitOps and Releases
Argo CD treats Git as the single source of truth for all deployments. The platform automatically detects configuration drift, runs health checks on applications, and can sync changes automatically or on demand. The team uses an app-of-apps pattern to promote releases across environments, and fast rollbacks protect against bad deployments.
Helm packages and templates applications for repeatable releases across environments. The team maintains shared Helm charts with per-environment values and overlays. Helm’s release history enables quick, auditable rollbacks when needed.
Observability and SLOs
Prometheus · Loki · Grafana on EKS. Prometheus scrapes application and cluster metrics and writes them to durable object storage on Amazon S3 for long-term retention via the remote-write path; Loki stores logs in S3. This reduces local volume management and makes retention predictable and cost-efficient.
Grafana serves as the central observability layer, consolidating dashboards and alerting rules across performance, reliability, and business KPIs.
Cost Hygiene and Policy
The Cloudchipr SaaS platform gave the company full visibility into cloud costs and usage across multiple providers in one consolidated view. With timely and accurate data, they could finally allocate costs to specific projects and services, establishing clear unit economics for cloud spend.
The team started with data reporting and visualization, which helped identify the next FinOps capabilities to tackle. The team prioritized cost allocation first; once the team had reliable allocation in place, they could move on to workload optimization, targeting underutilized resources in development and sandbox accounts.
This foundation enabled rate optimization, since the team now had a clear view of monthly spend. They further strengthened optimization efforts with improved forecasting techniques and internal benchmarking.
The Results: 60% Cost Savings and 99.99% Uptime
- 60% cloud cost reduction in cloud spending through the implementation of the solution.
- 99.99% uptime maintained, meeting strict reliability SLAs.
- Cloud cost control and automated FinOps practices and policy enforcement gave the team complete control over cloud spending.
- Standardized CI/CD and GitOps workflows streamlined release processes across all environments.
- Granular observability and monitoring through Prometheus, Loki, and Grafana provided complete visibility into platform performance.
- Scalable cloud infrastructure successfully handled testing with over 200 Kubernetes nodes.
Contact
Questions about the approach? Cloudchipr — FinOps Platform & DevOps Services https://cloudchipr.com