Overview
Observability demands in healthcare are growing rapidly—but many platforms are still overpaying for data they don’t use.
As telemetry volumes increase across life-critical systems, inefficient data retention, idle infrastructure, and static architectures are driving unnecessary costs—without improving reliability or performance.
In this session, we take a strategic look at how to reduce observability costs without compromising visibility, compliance, or system performance. You’ll learn how optimized Index Lifecycle Management (ILM) and Observability as Code enable smarter data management, scalable operations, and long-term cost control.
What You’ll Learn
- How to identify and eliminate wasted observability spend
- How optimized ILM reduces storage costs through efficient data lifecycle management
- How to align data retention with compliance and operational needs
- How Observability as Code improves automation, consistency, and governance
- What healthcare leaders must rethink to scale observability without overspending
Real World Application
Managing large-scale healthcare data with efficient retention strategies; reducing storage and infrastructure costs without losing visibility; improving operational efficiency across observability platforms; enabling scalable monitoring across distributed systems; and supporting compliance-driven data management at scale.
Practical Insights
- Designing ILM strategies for high-ingestion environments (logs, metrics, traces)
- Eliminating idle data and optimizing retention policies
- Automating observability workflows using version-controlled configurations
- Reducing operational overhead for platform and infrastructure teams
- Scaling observability architectures without unnecessary infrastructure growth
RSVP
Sarvagya is an Elastic Engineer at Hyperflex, and an Elastic Certified Observability Engineer with a strong track record in designing scalable data pipelines, building impactful Kibana dashboards, and improving system observability for enterprises. He brings deep expertise in Elasticsearch, Logstash, and Kibana, along with hands-on experience leading anomaly detection, Elastic ML jobs, optimized clusters for performance and cost efficiency, and enabled teams to unlock the full potential of Elastic for monitoring and alerting.
Ploy Wongtaladkwon is a Marketing Manager at Hyperflex with a background in social media marketing, content strategy and B2B marketing. Ploy’s career spans marketing strategy, content creation, and event coordination. She holds a MS in Marketing Analysis from DePaul University, Chicago, IL, United States.
