In 2024 GoVolt experienced growth beyond expectations. Therefore, the technical team decided to focus on developing new features for the product, with the goal of launching an innovative system based on data analysis and predictive capabilities. To support this expansion, a serverless architecture was implemented fully on cloud in a serverless way. After the development and consolidation phase, in collaboration with the technical direction of GoVolt, we worked with the team to optimize the existing infrastructure in order to improve operational efficiency and reduce costs.
Performed an in-depth evaluation of existing AWS services, configurations, and costs. This included analysis of compute utilization, storage patterns, network throughput, and overall architecture. The audit identified resource underutilization, high operational overhead, and architectural bottlenecks impacting both cost and performance.
Designed and deployed a robust CI/CD workflow to streamline code delivery. Leveraged GitHub Actions for version control integration, and AWS CodePipeline for automated testing, building, and deployment. This eliminated manual steps, reduced deployment time from hours to minutes, and significantly improved code reliability in production environments.
Migrated core services from containerized and EC2-based workloads to a fully serverless architecture using AWS Lambda, API Gateway, and DynamoDB. This drastically reduced infrastructure management overhead and introduced auto-scaling, resulting in improved latency, better resource utilization, and substantial cost savings.
Integrated comprehensive observability tooling using Amazon CloudWatch and third-party monitoring solutions. Custom dashboards were created to track latency, error rates, and throughput. Real-time alerts were configured to notify development teams of anomalies, ensuring faster resolution of incidents and continuous system reliability.
The project began with an exhaustive audit of GoVolt’s AWS infrastructure to assess architectural design, service utilization, and cost distribution. This phase uncovered several inefficiencies, including underutilized resources and high-latency communication between services. Following the audit, we initiated a serverless-first refactoring approach, transitioning key workloads to AWS Lambda, API Gateway, and DynamoDB to reduce operational complexity and improve scalability. Then established a modern CI/CD pipeline using AWS CodePipeline and GitHub Actions to automate testing, integration, and deployment, drastically reducing release times and minimizing human error. To ensure long-term stability, we implemented centralized monitoring with Amazon CloudWatch and integrated alerting mechanisms, enabling proactive incident response and performance optimization.
Cloud Cost Reduction
Deployment Speed
Downtime
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