Core capabilities
Explore the capabilities that Instana provides for full-stack monitoring.
Instana provides comprehensive observability capabilities that help you to monitor, analyze, and optimize your applications and infrastructure. Each capability addresses specific monitoring needs and works together to provide complete visibility into your systems.
In the following sections, Instana's core capabilities are organized based on their primary function.
Observe and monitor
Core monitoring capabilities form the foundation of your observability practice, automatically discovering and monitoring your entire stack.
- Application perspectives - Organize and monitor applications based on business context
- Infrastructure monitoring - Monitor hosts, containers, and cloud resources
- Distributed tracing - Track requests across microservices with 1-second granularity
Detect and alert
Stay ahead of problems with automated detection, intelligent alerting, and reliability tracking. Identify issues and notify your team when action is needed.
- Events and incidents - Automated issue detection and root cause analysis
- Smart Alerts - Intelligent alerting with statistical models and zero configuration
- Service level objectives - Define and track reliability targets and error budgets
Visualize and analyze
Transform raw data into actionable insights with powerful visualization and analysis tools. Create custom views and perform deep analysis of your systems.
- Dashboards - Create custom visualizations and share insights across teams
- Infrastructure map - Visualize all monitored systems grouped by zones with real-time health indicators
- Code profiling - Identify performance bottlenecks at the code level
Automate and test
Reduce manual work and catch issues before they impact users with automation and synthetic testing.
- Automation and actions - Automate responses to incidents and reduce mean time to resolution (MTTR)
- Synthetic monitoring - Proactively test applications from multiple locations
Extend observability to AI agents and LLMs
Move beyond basic AI monitoring to gain deep understanding and control over AI-driven systems. Automatically discover AI components, evaluate output quality, detect issues before they impact the business, and understand how agents reason and decide in production.
- Generative AI observability - Monitor LLM performance, token usage, and AI service quality with automatic discovery and task-level visibility
- Model Context Protocol monitoring - Observe MCP servers, agent interactions, and tool usage patterns
Monitor specialized workloads
Monitor specialized workloads and enterprise systems.
Mainframe observability - End-to-end visibility for z/OS environments
Not sure where to start?
See the following common use cases and the capabilities that can help.
| Use case | Recommended capabilities |
|---|---|
| I want to monitor microservices and understand dependencies | Start with Application perspectives and Distributed tracing |
| I need to reduce MTTR | Use Events and incidents for root cause analysis and Automation for automated remediation |
| I want to ensure SLA compliance | Set up Service level objectives and monitor with Dashboards |
| I need to optimize application performance | Use Code profiling to identify bottlenecks and Distributed tracing to analyze request flows |
| I want to catch issues before users are affected | Implement Synthetic monitoring and configure Smart Alerts |
| I need to monitor AI or LLM applications | Use GenAI observability for comprehensive AI monitoring |