Kloudfuse announced the launch of Kloudfuse 3.0.
"Kloudfuse 3.0 sets a new standard in unified observability by focusing on critical areas such as data, AI and analytics, scalability, deployment flexibility, and enterprise-grade features," said Krishna Yadappanavar, CEO and Co-Founder of Kloudfuse. "Customers can now gain deeper insights into their digital experiences and optimize application performance in real time. Our advanced features—including digital experience monitoring, continuous profiling, powerful AI/ML capabilities, advanced analytics and visualizations, and a new query language—enable developers to identify and address performance bottlenecks with unprecedented efficiency. We’re proud to offer our clients the enterprise capabilities they need to create large-scale observability for their modern tech stack and drive their businesses forward."
With the launch of Kloudfuse 3.0, customers will now have access to features like Real User Monitoring (RUM) and continuous profiling, the latest AI advancements, along with powerful tools to manage large amounts of real-time data, a new query language, and updated deployment options.
Kloudfuse 3.0 redefines unified observability by integrating metrics, events, logs, and traces with two new data streams for a seamless observability experience. Key highlights include:
- Digital Experience Monitoring (DEM): This includes Real User Monitoring (RUM) and session replays. RUM offers insights into user experiences across digital transactions and click paths, showing how performance, availability, and errors affect the digital experience. Session replays provide pixel-perfect replays of user journeys, giving visual context to every interaction. Kloudfuse integrates frontend RUM and session replays with backend traces, logs, and metrics for full-stack observability.
- Continuous Profiling: This low-overhead, 24/7 code profiling capability enables developers to identify hidden performance bottlenecks in their code, thereby enhancing code quality and reliability in real time. By automatically evaluating CPU utilization, memory allocation, and disk I/O, it ensures optimal performance for every line of code while minimizing resource usage and costs.
Kloudfuse 3.0 enhances its AI and analytics features—such as rolling quantile, SARIMA, DBSCAN, seasonal decomposition, and Pearson correlation coefficient. It also strengthens its analytics and dashboards, and support for open query languages—like PromQL, LogQL, TraceQL, GraphQL, and SQL—by adding new capabilities:
- New AI Capabilities: The addition of Prophet for anomaly detection and forecasting provides more accurate results, effectively managing irregular time series that include missing values, such as gaps from outages or low activity. This results in less tuning and improved forecast, even with limited training data.
- K-Lens: Kloudfuse’s K-Lens uses outlier detection to quickly analyze thousands of attributes within high-dimensional data, identifying those that cause specific issues. It then uses heatmaps and multi-attribute charts to pinpoint the sources of these issues, accelerating debugging and incident resolution.
- FuseQL Language: Kloudfuse introduces a powerful new log query language with advanced capabilities and rich operators for complex queries and multi-dimensional aggregations. This new language enables smarter alerts, anomaly and outlier detection, addressing the limitations of existing log query languages, such as LogQL.
- Facet Analytics: Leveraging Kloudfuse’s patent-pending LogFingerprinting technology, which automatically extracts key attributes from logs for faster analysis and troubleshooting, Kloudfuse 3.0 provides advanced search, filtering, bookmarking, and grouping options, thus significantly boosting log analysis.
Kloudfuse ingests, processes, and analyzes vast amounts of real-time observability data using its scalable observability data lake and advanced shaping capabilities. Key additions include:
- Log Archival and Hydration: This feature provides immediate access to historical logs for compliance and regulatory needs while reducing long-term storage costs. Logs are stored in a cost-effective, easy-to-navigate compressed JSON format within the customer's own storage, such as S3. Tags facilitate easy classification and searching across both live and archived logs in a unified view.
- Cardinality Analysis and Metrics Roll-Ups: Cardinality analysis provides real-time insights into incoming metrics, logs, and traces, enabling organizations to discover and proactively reduce high cardinality data to lower storage and processing costs. Metrics roll-ups aggregate data, enhancing query performance during troubleshooting.
Kloudfuse is extending its flexible Virtual Private Cloud (VPC) deployment options—already available on Amazon Web Services (AWS), Google Cloud (GCP), Microsoft Azure, and multiple-cloud environments—with a new feature:
- Arm Architecture: This feature includes support for AWS Graviton processors and GCP Arm-based VMs, ensuring the cost reduction and efficiency required by large-scale observability deployments.
Kloudfuse 3.0 enhances enterprise capabilities with features including:
- Simplified User Management Experience: This includes user-friendly UI for Role-Based Access Control (RBAC), Single Sign-On (SSO) and multi-key authentication for enhanced security.
- Security Certifications: Kloudfuse supports customers with industry-leading security certifications including SOC 2 Type II, CVE Secure, and penetration test certifications ensure compliance readiness.
- Service Catalog: A central hub for microservice ownership and on-call coverage, the Service Catalog streamlines collaboration and governance during incidents and eliminates knowledge silos. It also discovers active and inactive services, their dependencies, and version changes across APM tools like OpenTelemetry.
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