Connect application performance metrics with infrastructure costs. Optimize for both performance and cost efficiency with unified observability.
Teams optimize performance and costs in isolation, missing opportunities for holistic optimization and leading to suboptimal decisions.
Performance teams optimize for speed while cost teams optimize for budget, often working against each other without visibility into trade-offs.
No visibility into how performance changes affect costs or how cost optimizations impact application performance and user experience.
Teams make optimization decisions based on incomplete data, leading to performance regressions or unexpected cost increases.
DeepCost correlates application performance metrics with infrastructure costs, enabling holistic optimization decisions that balance performance and efficiency.
Real-time correlation between application metrics (latency, throughput, errors) and infrastructure costs at service and resource levels.
Single-pane-of-glass dashboards showing performance metrics alongside cost data for comprehensive system understanding.
AI-powered recommendations that balance performance and cost objectives, preventing optimization conflicts and ensuring holistic improvements.
Native integrations with leading observability platforms for seamless performance-cost correlation across your entire stack.
Connect with Datadog, New Relic, AppDynamics, and Dynatrace for complete application visibility.
Integrate with Prometheus, Grafana, Splunk, and ELK stack for comprehensive monitoring.
Correlate trace data from Jaeger, Zipkin, and AWS X-Ray with infrastructure costs.
Define custom business metrics and correlate them with infrastructure costs and performance.
Results from unified performance-cost observability implementations
Correlate database query performance with compute costs to optimize both speed and efficiency.
Balance API response times with infrastructure costs to meet SLAs cost-effectively.
Optimize individual service resources based on performance requirements and cost constraints.
Balance cache hit rates with cache infrastructure costs for optimal performance-cost ratio.
Fine-tune autoscaling policies based on performance impact and cost efficiency metrics.
Understand how error rates correlate with infrastructure costs and optimize accordingly.
Stop optimizing in silos. Get complete visibility into performance-cost relationships.