Overview
- Client: Elastic
- Industry: Enterprise Software
- Cloud Provider: AWS
- Challenge: Complex multi-service AWS environment with limited visibility into deep inefficiencies
Elastic, the company behind Elasticsearch and the Elastic Stack, operates a substantial AWS infrastructure supporting its cloud-hosted search, observability, and security solutions. As their cloud footprint grew, the team needed deeper visibility into cost inefficiencies that traditional monitoring tools were not surfacing.
The Challenge
Elastic's infrastructure spans dozens of AWS services supporting a distributed SaaS platform used by thousands of customers worldwide. The complexity of this environment -- involving EC2, EBS, S3, networking, and managed database services -- made it difficult to identify optimization opportunities beyond surface-level recommendations.
Existing tools provided basic cost reporting but lacked the depth to analyze workload-level behavior, detect architectural inefficiencies, or provide the engineering context needed to act on recommendations with confidence.
The Solution
PointFive's DeepWaste Detection Engine analyzed Elastic's AWS environment at a level of depth that went beyond billing data analysis. By combining cost data with usage telemetry and workload behavior patterns, the platform identified inefficiency categories that had gone undetected.
Key areas of impact included:
- Storage optimization -- Identifying over-provisioned EBS volumes and underutilized S3 storage tiers across production and development environments
- Compute right-sizing -- Analyzing actual workload patterns rather than peak utilization to recommend more accurate instance sizing
- Network cost reduction -- Detecting unnecessary cross-AZ and cross-region data transfer patterns driving up networking costs
- Service-specific insights -- Providing granular recommendations for managed services that traditional tools treat as black boxes
Results
PointFive delivered visibility into cost drivers that Elastic's existing tooling had not surfaced, enabling the infrastructure team to prioritize and implement optimizations with engineering context and confidence.
- Deeper detection coverage across more AWS service categories than previous tools
- Engineering-friendly recommendations with technical context that built trust with implementation teams
- Reduced investigation time through contextualized findings rather than generic alerts
- Continuous monitoring that surfaces new optimization opportunities as the environment evolves
Conclusion
For enterprises operating complex cloud environments at scale, the gap between what basic cost tools find and what actually exists can be substantial. PointFive's deeper detection methodology gave Elastic the visibility needed to move from reactive cost management to proactive cloud efficiency.