Introduction:
A prominent healthcare company faced the challenge of modernizing its infrastructure to meet the demands of a dynamic market while ensuring cost efficiency. A hybrid structure spanned throughout AWS cloud and on-premises environments, coupled with the imperative to integrate AI-driven services into their applications.
The Challenge:
Timelines:
Understanding the infra was a big challenge as the landing zones are to be created.
The Solution:
Our team devised a comprehensive strategy to address the client’s requirements, focusing on efficient migration, AWS service deployments, including AI services, all under strict cost optimization measures.
Before Migration (Hybrid Infrastructure):
On-Premises Infrastructure:
AWS Infrastructure:
After Migration to AWS:
AWS Infrastructure:
After migrating to AWS their infrastructure costs were around a million dollars per month.
Cost-Optimized Migration to the Cloud:
Landing zones, where workloads are migrated, were planned wisely to optimize costs, if not configured up to the mark, may lead to an increase in compliance and security expenses. The setup of the landing zone included integration with identity directories, formulation of account structures, establishment of virtual private cloud (VPC) networking, establishment of infrastructure for security, monitoring, and configuration management.
Various options with reference to sizes and types were considered.
We made use of tools and monitoring to modify resource allocation based on authentic usage patterns.
Seamlessly migrated the company’s on-premises infrastructure to the cloud, optimizing resource utilization and minimizing downtime. Through rigorous assessment and planning, we achieved cost savings of up to $40-45K/month during the migration phase alone.
Cost-optimization of the Cloud Deployment:
Eliminating the need for manual deployments and saving an estimated 100-120 development hours per month. This translates directly to $30-40K/month saved in developmental costs.
This granular data was then visualized in Grafana, allowing us to identify pods consistently underutilizing their allocated resources. Leveraging Horizontal Pod Autoscalers (HPAs), we implemented a dynamic scaling strategy.
HPAs automatically adjust the number of pod replicas based on pre-defined CPU or memory utilization thresholds. This ensured the worker nodes were populated with only the necessary number of pods, maximizing resource utilization while maintaining application performance.
By skillfully right-sizing worker node capacity based on the optimized pod density, we achieved a 20% reduction in EKS costs (approximately $30,000 per month) applied on 3 various env (dev, uat, prod) with 4 EKS clusters, 200+ worker nodes and 1000+ pods running 50+ microservices in them.
By understanding the architecture in and out and thus coming up with these personalized cost optimized solutions our team helped in saving more than $90-100K per month.
Cost Optimization of AI Services:
Through Parquet’s columnar storage design and efficient compression algorithms, we achieved storage reductions averaging around 40-50% compared to uncompressed data storage formats. This substantial decrease in storage footprint directly translated to cost savings.
By implementing these cost optimization strategies, we maximized the value of AI services while minimizing operational expenses up to $20-25K per month.
Outcomes:
Through our expertise, the healthcare company successfully transitioned to a cloud-native infrastructure, realizing substantial cost savings and operational efficiencies.
After applying all the above strategies and cost optimization techniques their million-dollar infrastructure cost came down to roundabout $800K per month.
The optimized deployment of AWS services enabled seamless scalability and enhanced performance, while the integration of AI services empowered them to deliver innovative healthcare solutions, positioning them as a leader in the digital healthcare landscape.
Conclusion:
Our expertise in cloud services, coupled with intense focus on cost optimization and AWS AI service implementation, enabled our healthcare client to navigate the complexities of digital transformation with confidence and saved up to 20% ($150K-200K per month) of the total cost. By leveraging the power of the cloud and AI, we empowered the client to enhance service delivery, drive innovation, and achieve sustainable growth in an ever-evolving healthcare ecosystem.