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AWS EKS and Kubernetes scalability
FROMCLOUD helped move the platform toward a multi-cluster Kubernetes architecture using Amazon EKS, improving scalability, reliability, and operational flexibility.
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Real client case study
FROMCLOUD helped Cocoding AI transition toward a scalable, cloud-native AI SaaS architecture using Amazon EKS, Kubernetes, AIOps, FinOps, and MLOps practices.
Client overview
Cocoding AI is an agentic coding platform. During a critical transition phase, the platform needed stronger infrastructure foundations for deployment reliability, monitoring, cost visibility, and AI model lifecycle management.
FROMCLOUD supported the team with architecture and implementation direction for a scalable cloud-native platform on AWS. The work centered on Amazon EKS, Kubernetes scalability, production deployment workflows, observability, AIOps concepts, FinOps visibility, and MLOps support.
The challenge
Cocoding AI needed an infrastructure path that could support increased usage while keeping deployment workflows reliable and operations visible.
Scaling an AI SaaS platform for increased usage while preserving reliability.
Moving toward a more resilient Kubernetes-based infrastructure.
Designing AWS and Kubernetes architecture aligned with production best practices.
Improving deployment workflows for a production AI platform.
Adding observability, AIOps, and cost optimization capabilities.
Supporting MLOps and LLMOps workflows for fine-tuning, evaluation, and continuous improvement.
FROMCLOUD solution
FROMCLOUD helped move the platform toward a multi-cluster Kubernetes architecture using Amazon EKS, improving scalability, reliability, and operational flexibility.
The architecture focused on reliability, security, scalability, observability, and maintainability so the platform could support future growth.
AIOps and agentic AI concepts were applied to improve infrastructure monitoring, operational response, and automation patterns.
FROMCLOUD strengthened cost monitoring and optimization practices so infrastructure efficiency could be tracked alongside platform growth.
The engagement supported SageMaker-based MLOps workflows, model fine-tuning, evaluation automation, and continuous model improvement.
Architecture highlights
The architecture direction separated infrastructure scalability, production deployment reliability, observability, cost visibility, and model lifecycle concerns into explicit operating layers.
Multi-cluster Kubernetes direction using Amazon EKS.
Production deployment approach for a cloud-native AI SaaS platform.
Infrastructure structure designed for reliability, security, and observability.
Operational automation patterns informed by AWS DevOps Agent and FinOps Agent concepts.
Model lifecycle support using SageMaker-oriented MLOps workflows.
AI operations, MLOps, and FinOps
Operational monitoring patterns were shaped around AIOps and agentic AI concepts, including Bedrock AgentCore concepts and AWS DevOps Agent patterns.
Model workflows were designed to support fine-tuning with new data, evaluation loops, and continuous improvement of the AI platform.
Cost visibility and optimization practices helped make AWS spending easier to monitor as infrastructure needs evolved.
Business impact
The engagement improved the technical foundation qualitatively. Exact performance, cost, or uptime claims are intentionally not stated here.
Improved scalability foundation for the AI SaaS platform.
Stronger deployment architecture for production workloads.
Better operational visibility across cloud-native infrastructure.
Improved cost monitoring and optimization practices.
A stronger foundation for MLOps and LLMOps workflows.
More reliable infrastructure direction for an agentic coding platform.
Better readiness for future platform growth.
Technologies used
AI SaaS infrastructure
FROMCLOUD helps AI startups design, migrate, and operate scalable cloud-native infrastructure on AWS and Kubernetes.
Based in Krakow ยท Remote worldwide