Java Resource Management
Autonomously Manage Java Workloads
ScaleOps autonomously optimizes Java workloads in production by continuously aligning JVM heap and container memory requests with actual Java memory usage in real time. The result: fewer OOMs, less waste, and stable performance without manual tuning.
Autonomous Java Resource Management
ScaleOps analyzes live JVM behavior (heap usage, garbage collection patterns, and OOM signals) to make accurate, real-time resource decisions. By aligning JVM heap with container memory, it prevents OOM kills, cuts overprovisioning, and keeps Java performance stable as load changes.
Real-Time Visibility and Troubleshooting
Gain instant visibility into JVM internals, including insights into Java heap and non-heap memory breakdown, garbage collection behavior, memory pressure, and OOM events. ScaleOps surfaces these signals alongside resource decisions to help teams troubleshoot faster.
The only platform that truly optimizes the JVM
Automatic Java Workload Detection
ScaleOps automatically identifies all workload types, including Java workloads across your clusters. No onboarding or configuration required, and optimization starts immediately. Both custom Java applications and widely used Java-based frameworks are supported out of the box, including Spark executors and long-running Java services.
Cloud Resource Management Reinvented
Boost Performance & Reliability
Ensure consistent performance and uptime, even in the most dynamic environments.
Free Your Engineers
Eliminate repeated manual tuning forever, allowing you to focus on innovation.
Cut Costs by 80%
Pay only for the cloud resources you need without compromising performance.
Frequently Asked Questions
What is ScaleOps and what does it do for Java workloads?
ScaleOps autonomously optimizes Java workloads in production by continuously aligning JVM heap allocation with Kubernetes container memory in real time, preventing OOM kills and reducing resource waste.
How does ScaleOps prevent Java out-of-memory errors?
ScaleOps analyzes live JVM behavior including heap usage, garbage collection patterns, and memory pressure to align JVM heap with container memory requests, preventing OOM kills before they occur.
Does ScaleOps require manual configuration for Java workloads?
ScaleOps automatically detects Java workloads across clusters with no onboarding or configuration required, and optimization starts immediately.
What types of Java applications does ScaleOps support?
ScaleOps supports custom Java applications and widely used Java-based frameworks out of the box, including Spark executors, Wildfly-based workloads, and long-running Java services.
What JVM metrics can I monitor with ScaleOps?
ScaleOps provides visibility into Java heap and non-heap memory breakdown, garbage collection behavior, memory pressure, and OOM events alongside resource decisions.
How does ScaleOps handle changing workload demands for Java applications?
ScaleOps continuously monitors JVM behavior and adjusts resource allocation in real time to maintain consistent Java performance under changing load without manual tuning.
Instant Value with Seamless Automation