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It takes collaboration between Dev, QA Perf Engineers, and Ops to make apps thrive in production. A common platform for performance data and infrastructure context that fits in the flow of CI and CD makes it easier for teams to focus on common goals.
Optimized infrastructure leads to better app performance and reduced footprint. In an ever-changing world of deployments, and with hundreds of compute infrastructure types, optimization is a constant challenge. Optimization needs to account for infrastructure resource needs, nature of workloads and latency behavior. We make it easier and faster to achieve all this and more.
Applications get a performance boost when the configuration of Platforms such as JVMs are optimized for the app. Finding the right JVM settings to optimize for latency or throughput takes significant effort. It’s dependent on the Platform version and the app behavior to the expected load. We reduce the pain and effort in optimizing your platform.
You enabled us to do more! The insights to infrastructure behavior and its impact on app performance was amazing. Now, I can assist the development team to optimize the app for resource consumption in addition to code execution.
You saved us time and brought consistency in analysis. Automating the analysis of the impact of the IT infrastructure on app performance saved us time. We were doing it manually with visual chart analysis. The consistently in the method of analysis made it easier to collaborate with Developers and Op
Speeds up the release process! Comparing the changes in IT infrastructure behavior between builds helps us to have the foresight and reduce the time in load testing phase. Being able to integrate the analysis into our CI process makes our life easier!
Product Category : Devops Collaboration
Product Type : SaaS Product
Performance Engineers are the bridge between Developers and Operations. Ops use the results of performance and load (P&L) tests done by Performance Engineers as an input for server sizing, deployment strategy, and setting SLO thresholds for apps being deployed to production environment.
However, collaboration requires that the performance tests to be meaningful to Ops. That requires the tests to use workloads that are similar to production workloads and to be run on servers that are comparable to production servers. The similarity of test workloads to production workloads has a direct impact on the confidence of the (P&L) test results.
Generating test workloads that are similar to production workloads is laborious and time consuming. New version app being tested typically has new features and urls that those in app deployed in production environments. Using production session replays to generate load will not cover the new features and urls. Since workloads on IT systems are random, they are stochastics processes. Deterministic types of load patterns such as stepped test do not represented the load characteristics found in production. Stochastics processes are best modeled using statistical techniques.
IntelliSense™ Performance Workbench uses proprietary statistical and machine learning techniques to compare and generate test workloads that are similar to production workloads. Improving the confidence in your P&L test results is now made easy.
Product Category : IaaS Optimization
Product Type : SaaS Product
IntelliSense™ IaaS Workbench is a platform that solves the most pressing problems faced by Performance Engineers and Ops. Optimizing the infrastructure has many benefits such as improved app performance and right sizing the deployments. It can reduce costs and improve manageability.
Selection of optimal compute instance for an app can provide all those benefits. However, it is a challenge as compute instance selection needs to account for app’s resource utilization and the load they are under. To add to the complexity, there are dozens of instance types which IaaS vendors provide as well as internally available IaaS. Since load on IT systems are random, they are stochastics processes. Deterministic approaches cannot be used to determine solutions but they are ideal for probabilistic approaches.
IntelliSense™ IaaS Workbench uses proprietary statistical and machine learning techniques to recommend the optimal compute instance type for any app. It saves you time and effort making it easier and faster. Make it as part of your Continuous Delivery (CD) workflow to keep your deployments optimized.
Product Category : Performance Testing
Product Type : SaaS Product
Understanding your performance data will never be the same once you experience intelligence that helps you make sense of it all. Gain performance insights for your app using your favorite JMeter Open Source tool.
Instantly analyze your performance test results using powerful analysis reports with charts and tables. Built-in algorithms automatically detect test type, system saturation, and more!
Understand the performance impact between two builds or multiple test results. Compare multiple analysis reports. Track KPI correlations to system resources.
Use IntelliSense with our Infrastructure Performance Analytics product add-on to gain smarter insights and make smarter decisions.
Product Category : Performance Testing
Product Type : SaaS Product Addon
Wouldn’t you like to know the root cause of CPU spikes? To understand the impact that CPU spikes have on an app’s Quality of Service? Wouldn’t you like to compare analyses to know if an app's CPU characteristics have changed?
Our Infrastructure Performance Analytics add-on enables all of the above and more; allowing you to go beyond visual analytics!
Instead of manually analyzing JMeter Performance test results slowly, and with error – rather sit back and let our algorithms do all the work. They will quickly provide you with insights along with supporting data. Put that time saved for better use.
Use Infrastructure Performance Analytics as an add-on with IntelliSense™ Visual Performance Analytics.
Gain smarter insights. Make smarter decisions.
Optimal JVM Settings
Better Infrastructure Performance!
We will invite you when a spot opens up
We develop statistical models that fit IT Systems behavior. We don’t fit IT Systems to generic algorithm
We embed domain knowledge into our statistical models and algorithms.
We understand machine-generated data whether they are metrics, events or configurations.
We understand that data from Perf & Load Testing is different from Production.
We understand that the production environment can be dynamic. Apps, the load, the platform, and the infrastructure can all change over time.
We understand that infrastructure is different at different stages of CI and CD.