Case Study

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Client - DevOps Case Study Datami

About the Client

It all started with a simple question: How can we build the ultimate mobile service?

When existing MVNE and telco solutions couldn’t deliver, we took matters into our own hands. We built the tech stack we needed—powerful, flexible, and future-ready. Today, that same innovation powers our global SaaS platform, designed for ease, scalability, and limitless potential. And it’s ready for innovators like you.

As a start-up, we embraced the opportunity—and responsibility—to build a culture we’re truly proud of. The Reach 'Core Four' values empower our team to do right by our clients and every end user that interacts with our platform. These guiding principles also spark the kind of bold thinking needed to disrupt an industry — keeping us focused, inspired, and just a little daring

Customer Challenge

The client utilized AWS to effectively manage both their development and production workloads, maintaining separate environments for each client platform across non-production and production accounts. This segregation was a key element of their operational strategy, enabling them to thoroughly test applications in non-production environments before launching them for end users.

However, despite their efforts, the client faced challenges in consistently following AWS best practices, resulting in inefficiencies and potential risks. These challenges highlighted the need for an experienced partner to guide them in managing their DevOps pipeline. The client sought expertise to improve deployment efficiency, a critical factor in meeting their clients' expectations and deadlines.

Partner Solution

To address the client’s challenges with a modern, scalable, and cost-effective serverless architecture, CloudStok proposed a comprehensive DevOps solution leveraging AWS managed services. The updated architecture ensures high availability, fault tolerance, and cost-efficiency, built around Lambda and DynamoDB. The solution includes the following components:

  • Environment Segmentation - We implemented a clear separation between production and non-production environments using distinct VPCs and IAM roles. This segmentation ensures that development activities do not interfere with production workloads and simplifies environment-specific governance and access control.
  • Serverless Infrastructure Setup - Each client platform is hosted in a VPC with a mix of public and private subnets.
  • The application backend logic is implemented using AWS Lambda, offering on-demand compute with automatic scaling and zero idle costs.
  • Data is stored in Amazon DynamoDB, a serverless NoSQL database, ensuring low-latency access, seamless scaling, and high availability.
  • CI/CD Pipeline - CI/CD workflows are configured in Bitbucket Pipelines, automating code build, test, and deployment for Lambda functions and syncing frontend assets to Firebase Hosting. This ensures fast, reliable, and consistent delivery across environments.

AWS Services Used

  • AWS Fargate
  • AWS RDS
  • AWS ECR
  • AWS EC2
  • AWS Lambda
  • AWS CloudWatch
  • AWS WAF
  • AWS CloudTrail
  • AWS Step Functions
  • AWS SNS
  • AWS Auto-Scaling
  • AWS Load Balancer
  • AWS SQS
  • AWS Systems Manager
  • AWS SES
  • AWS Config
  • AWS Secrets Manager
  • AWS Security Hub

Results and Benefits:

The DevOps team at CloudStok effectively designed and implemented the proposed AWS solution, delivering substantial improvements to the client’s application development and deployment processes. Key outcomes included:

  • Improved Deployment Efficiency: Optimized CI/CD pipelines reduced deployment time by 40%, accelerating release cycles.
  • Faster Customer Onboarding: Automated provisioning cut onboarding time by over 50%, enabling quicker client engagement.
  • Cost Optimization: Right-sized AWS resources led to a 25% reduction in unnecessary compute and storage costs.
  • Enhanced Reliability: Strengthened infrastructure reduced production outages by over 60%, ensuring higher uptime.
  • Reduced AWS Expenditures: Optimized resource allocation lowered monthly AWS costs by 20-30%.
  • Increased ROI: Infrastructure optimization and automation improvements resulted in a 15% increase in return on investment (ROI).

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