Application performance
monitoring (APM) with logz.io
Monitor the health and performance of modern,
microservices-driven applications and architecture.
Distributed Tracing demo video
DevOps teams need advanced monitoring to ensure the health and performance of distributed cloud applications. Logz.io delivers Application Performance Monitoring (APM) capabilities that:
- Combine the power of Distributed Tracing and Metrics.
- Correlate end-to-end transactions with relevant logs and metrics.
- Instrument services with OpenTelemetry for interoperability.
Go beyond traditional APM and translate applications performance data into actionable insights for engineering and business users.
Logz.io Distributed Tracing dashboard analysis
Logz.io goes beyond traditional APM by combining Metrics and Distributed Tracing, backed by OpenTelemetry data collection, via key capabilities including:
- Service performance monitoring: Collect and aggregate trace data to calculate R.E.D. metrics.
- Real user monitoring: Record and analyze key user interactions to optimize experience.
- Synthetic monitoring: Simulate user behaviors across cloud services to improve performance.
- Cross-platform alerting: Automatic alerts inform teams whenever services or applications falter.
A system-wide view of application architecture
Logz.io’s SaaS model – backed by the power of open source and a team of proven experts goes far beyond traditional platforms. Benefit from key delivery elements including:
- Advanced OpenTelemetry: Send trace data using OpenTelemetry backed by frameworks.
- Powerful analysis and visualization: Combining APM and trace data with Jaeger and OpenSearch.
- Fully managed stack: Benefit from the power of popular Jaeger-based APM and Tracing.
Correlate traces with related logs
Ensure applications owners can continuously analyze and improve applications performance. Logz.io Distributed Tracing offers maximum TCO via:
- Contextual change analysis: Track changes that impact service or business performances.
- Cross-audience intelligence: Combine data for stakeholders across engineering, ops and the business.
- Optimized data management: Control sampling rates and maintain lower data volumes and costs.
