Cloud Infrastructure Optimization and Cost Reduction for EdTech Platform

As a cloud-first company specializing in digital educational solutions, the client operates with over 20 AWS services across multiple cloud environments. Rapid organic growth and acquisitions have led to a heterogeneous and complex technology stack, causing operational inefficiencies, increased costs, and difficulty in maintaining visibility into resource utilization. The absence of a comprehensive architecture diagram and high-cost elements in services such as container orchestration, database hosting, and messaging infrastructure have hampered performance and scalability initiatives.

A mid-sized educational technology company providing digital enrollment solutions and virtual engagement tools to higher education institutions, utilizing a complex cloud infrastructure from multiple acquisitions.

• Reduce cloud infrastructure costs by identifying and optimizing high-expense services, targeting a minimum 30% cost reduction.
• Improve visibility into cloud resource utilization through comprehensive architecture analysis and monitoring dashboards.
• Resolve performance bottlenecks, such as autoscaler stability issues and inefficient resource provisioning, to enhance system reliability.
• Implement targeted microservices architecture improvements, including migration of suitable services to serverless platforms for scalability and cost-efficiency.
• Optimize data management and backup strategies to streamline storage costs and data retrieval efficiency.
• Enhance messaging infrastructure cost-effectiveness by refining message size, content, and delivery practices.

• Automated identification of high-cost cloud services including EKS (Kubernetes) clusters, RDS instances, and messaging services.
• Implementation of resource usage dashboards providing real-time monitoring and historical logs analysis.
• Tools for detailed analysis of container usage, including deployment metrics and efficiency insights, utilizing marketplace assessment tools.
• Automated detection and resolution of autoscaler crashes and performance issues within Kubernetes environments.
• Dynamic adjustment of data storage strategies, including backup configuration and data retention policies.
• Evaluation and recommendation module for migrating microservices to serverless platforms like AWS Lambda, based on usage patterns.
• Mechanisms for optimizing messaging cost by enforcing message size limits, link shortening, and content management.

• Monitoring and logging systems (e.g., CloudWatch, third-party dashboards)
• Container management tools
• Messaging infrastructure platforms (e.g., Twilio or equivalent), with optimized billing practices
• Data storage and backup systems

• System should support scalability to accommodate increasing user and service load, with minimal performance degradation.
• Cost optimization measures to achieve at least a 30% reduction in cloud expenditure within the first year.
• High system reliability with automated recovery from performance issues, such as autoscaler crashes.
• Security compliance and data privacy adherence in storage, backup, and messaging components.
• Real-time monitoring and alerting capabilities for resource utilization and cost anomalies.

By implementing comprehensive cloud infrastructure analysis, performance tuning, and cost optimization strategies, the client is projected to achieve at least 30% reduction in monthly AWS expenses, translating to annual savings of over a quarter million dollars. System reliability and scalability will be significantly improved, enabling the client to better support increasing user activity and extend their market reach while maintaining operational efficiency.