SolarWinds Observability

Comprehensive application observability for both internally written and commercial applications, to go beyond basic metrics, traces, and logs offerings. It combines application performance metrics with distributed tracing and leverages log monitoring capabilities along with AIOps-driven notifications—and supports cloud-native open-source frameworks and third-party integrations. Application observability helps ensure the availability and performance of cloud-native custom applications and microservices while also observing your commercially acquired packaged applications and databases. With application observability, you can:

• Eliminate tool sprawl with a single, fully integrated, full-stack observability offering spanning metrics, traces, and logs, to provide a single interface into every layer of cloud applications.
• Reduce alert fatigue with AIOps-enhanced alerts, along with ML-powered baselining and customized metrics to automatically prioritize and surface real issues with a single view of thresholds exceeded as well as alert integration with major notification providers.
• Troubleshoot internally written applications down to a poor-performing line of code
• Monitor the availability and health of your commercial applications and databases

Infrastructure observability helps ensure the health and performance of on-premises, and cloud-based resources, including physical servers, VMs, hosts, containers, serverless, and cloud service providers. Integration with AI-powered analytics and application and log observability combine to deliver context-rich intelligence to help you proactively identify and resolve performance issues. With infrastructure observability, you can:

• Deliver data-driven insights. AI proactively monitors health and performance across on-premises and cloud resources including servers, VMs, hosts, containers, serverless environments, and infrastructure-as-a-service-provided resources to enable proactive performance tuning and issue resolution.
• Break down data silos. Aggregation of performance and health data across on-premises, cloud and hybrid IT stacks, entity grouping, and deep integration with log and metric data provides context to performance data and simplifies issue identification and resolution.
• Scale seamlessly. Wide support for open-source frameworks, as well as third-party integrations delivers easy instrumentation. Auto-discovery and AI-enabled baselines and metrics provide dynamic infrastructure observability.

Log observability provides scalable, full-stack, multi-source log management, combining wide support, powerful search, AI-driven analytics, and built-in integration with application and infrastructure observability to deliver context-rich intelligence to help teams troubleshoot smarter and faster.

• Provide context to event data. AI-driven insights via event aggregation and ML-powered analysis of log data across the entire cloud applications, services, and infrastructure stack.
• Spend less time troubleshooting. Real-time log tailing and intuitive search across all log data helps accelerate root cause identification and reduce time spent troubleshooting, so teams have more time to innovate.
• Leverage cloud-native frameworks. Easy setup with wide support for cloud-native and open-source frameworks, as well as third party integrations, lets teams use familiar tools for faster time to value.
• Simplify log management with full-stack, multi-source log aggregation and a single search bar across all event data​.
• Manage risk with log archiving to ensure the availability of log data for forensic analysis and compliance reporting​.

Database observability provides deep performance monitoring to diagnose and analyze issues, using sophisticated root cause analysis. Get comprehensive visibility into your database instances to help increase system performance and team efficiency while helping ensure infrastructure cost savings.

ML-based AIOps and health scores based on golden metrics quickly highlight issues. Get full-stack observability by correlating database metrics with the application performance, distributed tracing, and log monitoring capabilities of SolarWinds Observability.

Database observability simplifies the complexity of managing multi-vendor environments by providing support for databases such as MySQL, PostgreSQL, Microsoft SQL Server, AWS Aurora (PostgreSQL, MySQL), AWS RDS (PostgreSQL, MySQL), MongoDB, MongoDB Atlas, and Redis, so you can:

• Ship better code. See query responses before and after a deployment event. Examine query details and performance, including samples and execution plans. Compare the performance of your top queries over time.
• Troubleshoot and diagnose outages. Correlate query response or behavior to system metrics to understand impacts. Isolate unusual behavior and potential contributing factors within the database.
• Understand database health. Track metrics and watch for trends with health summaries for databases and systems. Get recommendations based on best practices. Explore and examine performance outliers.
• Simplify performance management with full-stack tracing and built-in intelligence designed to identify anomalies​.

Digital experience observability enables DevOps teams to optimize web application customer experience. Granular, real-time performance data, combined with AI-powered analytics, delivers deep insights into application performance impacts on the end user experience. Armed with this intelligence, DevOps and IT ops teams can experience the application the way the customer does—and make better design and implementation choices.

• Understand the impact of web application availability and performance on the end-user experience with synthetic monitoring. Availability and performance data from the user perspective combine with AI-powered analytics to deliver deep insights.
• Gain user insights with scalable real user monitoring (RUM) and experience your websites like an actual end user. RUM is lightweight, offers accurate client-side visibility, and can be deployed with just a snippet of code.
• Optimize performance. Identify emerging performance issues with real-time metrics, including page speed, load time, and response speed.
• Validate improvements. Test and optimize web elements using synthetic transaction queries to fine-tune critical web flows.
• Spot usage trends. Use global sensors to track performance and filter performance data by geography, device, and browser.
• Simplify monitoring with artificial intelligence (AI)-powered health scores, uptime status, and active alerts by website—in a single dashboard view.
• Manage performance proactively for standard websites and single-page applications with real-time performance metrics, APEX satisfaction scores, and real user monitoring.
• Get started quickly with automated wizards designed to guide you through website setup, synthetic checks, and real user monitoring.

Network observability helps enable troubleshooting of the availability, health, and performance of on-premises networks and end-to-end connections to public cloud networks through the collection and analysis of diverse network metrics and logs. With network observability, organizations can more easily understand and visualize the overall picture of how the network is impacting the services and experiences depending on it. With network observability, you can:

• Get a holistic end-to-end view of on-premises network health, flows, traffic analysis and connections to your cloud provider’s infrastructure. This helps provide complete visibility into the network resources underpinning services, thereby reducing blind spots associated with traditional network tools and enabling more accurate and accelerated troubleshooting.
• Eliminate tool sprawl. Network observability provides broad visibility across multi-vendor and multi-cloud networks by aggregating a comprehensive set of metrics into a single-pane-of-glass network view and enabling insights into how any impact on end users and business services may be correlated to network issues historically or in real time. This holistic approach also helps reduce the inefficiencies and risk often associated with piecing together information from multiple disparate tools.
• Scale with efficiency. Network observability helps reduce the overhead and inaccuracy associated with static thresholds through AIOps-enabled pattern recognition and anomaly detection allowing network operations teams to be more productive. It also provides insights into correlated alerts and events to accelerate root cause analysis. This can help you scale the network with a leaner management team.