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What's new in FusionReactor

FusionReactor 10

FusionReactor 10 will revolutionize how you monitor and view your data. FR 10 includes improvements to metric shipping, support for Adobe ColdFusion 2023, and bug fixes, including attaching child/sub-transactions to slow/error (ITT) requests.

OpsPilot AI

OpsPilot AI is an advanced observability solution that leverages generative AI to provide powerful insights into the performance of your applications. By combining real-time data collection, predictive analytics, and natural language queries, OpsPilot AI enables you to identify and resolve issues before they occur, optimize the performance of your applications, and reduce downtime.

Instant Error Analysis & Solutions with FusionReactor OpsPilot AI from FusionReactorAPM on Vimeo.

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OpsPilot AI

Kubernetes cluster monitoring

Kubernetes allows developers to deploy, manage, and scale containerized applications across a cluster of nodes, providing features such as load balancing, automatic scaling, self-healing, and rolling updates.

To make things easier for you, we have introduced Kubernetes monitoring in FusionReactor Cloud. With this tool, you can automate many of the manual tasks and focus on getting the most out of your Kubernetes monitoring.


Observability Agent

The Observability Agent, an open source autoconfiguration and installation tool, is a wrapper for the Grafana Agent that can install the agent, detect which services are running on your machine, and automatically create a configuration file with integrations for detected services.


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Observability Agent

Usage based billing

As a complete observability solution, our usage-based billing plan meets your needs in a diverging technical space and we pride ourselves on offering essential observability at sensible prices.

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To help keep things simple and transparent, FusionReactor has developed usage dashboards that allow you to visually track your billing data.

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Billing dashboards

Distributed tracing

We are excited to announce the addition of a new distributed tracing within FusionReactor. Distributed tracing is a method used in observability to track the flow of requests as they move through a distributed system. It allows you to see a detailed view of the entire request flow, from the time the request was initiated to the time it was completed.

FusionReactor captures and displays this trace information in a graphical format, so you can visualize the entire request flow and quickly identify any issues or bottlenecks.

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Distributed Tracing

Log management

Since the release of FusionReactor 9, we are able to ingest and index any log file using Loki.

FusionReactor uses this powerful capability to make it easy to analyze and track system events. We can also automatically link logs to other events such as distributed traces or errors, providing a complete picture of system activity. This makes investigating problems and identifying issues much faster and more efficient which improves overall performance and reliability.


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Logging monitoring

Span Metrics

FusionReactor has several new Span Metrics dashboards which provide detailed insight into specific parts of a distributed trace through the use of spans. Spans are essentially small pieces of time that represent a specific operation or action within a larger process or transaction.

Measuring span duration and status codes allows developers to quickly identify bottlenecks and problem areas in their application's distributed architecture.

With the ability to view 90th and 99th percentile span duration, developers can easily determine areas that need improvement and take action to optimize performance. This new capability is a powerful tool for improving application performance and providing better visibility into distributed architectures.

Coming soon...

Enhanced OpsPilot AI features

We're working on enhancements for our OpsPilot AI so that it solves difficult problems with even greater accuracy. We are improving OpsPilot's broader general knowledge and problem solving abilities by fine tuning your usage journey to enable even faster identification and resolution of issues.

Anomaly detection

Anomaly detection, which is also referred to as outlier detection, involves identifying unusual patterns or observations in a dataset that deviate from an expected behavior. Such patterns or observations are often referred to as anomalies, outliers, or exceptions. The anomaly-detection-service provided by FusionReactor utilizes the PyOD library and is specifically designed to automatically detect anomalies in time series data without the need for labeled data. By identifying these anomalies, corrective actions can be quickly taken to prevent potential issues before they cause issues or unnecessary downtime.

Integrated System Health Dashboards

FusionReactor's new System Health dashboards provide a visual representation of the status and performance of your entire infrastructure. It is a centralized display that provides a real-time view of various metrics and key performance indicators (KPIs) that are critical to the operation of the system.

Our System Health dashboards typically provide an overview of the system's overall health, including metrics such as CPU usage, memory usage, network activity and system uptime. Others also include more specific performance indicators, such as response times for key applications, error rates, and service availability.

Our new System Health dashboards enable you to quickly and easily monitor the health and performance of critical systems, identify potential issues or bottlenecks, and take corrective action before they escalate into major problems. By providing a real-time view of system performance, FusionReactor System Health dashboards proactively address issues and ensure that critical systems remain available and responsive to users.

Further integrations

Exporters are available in many forms and allow you to monitor many aspects of your infrastructure. With Kubernetes already added, FusionReactor Cloud will very soon be able to offer more integrations such as AWS, GCP, Kafka, Mongo and more.