Observability solutions

Full-stack observability
that drives intelligent automation 

Modern stacks demand full-layer visibility and AI-driven automation to predict risks and keep apps resilient.

Modern environments are complex. Full-stack observability links performance, reliability, and risk insights across layers. Paired with automation, it empowers DevOps teams to move from detection to resolution faster. This improves resilience, reduces toil, and accelerates mean time to resolve across hybrid cloud and distributed systems. 

Observability: Where to start

Observe and fix issues fast

Gain high-fidelity, contextualized, full-stack observability; track and optimize the DevOps lifecycle; and enable proactive remediation through trend analysis and visualization.

Intuitive resource optimization

Recommend actions for continuous resilience across hybrid and public clouds, driven by correlate data from multiple tools for real-time insights.

Automate remediation at scale

Use AI-driven workflows to remediate vulnerabilities and performance issues automatically, applying repeatable runbooks to reduce toil.

Eliminate blind spots

Continuously discover and track all servers, containers, and cloud resources across environments. Account for assets to identify and patch critical vulnerabilities before they put your systems at risk.

Turn complexity into clarity with AI-driven observability

Hybrid and multi-cloud architectures, containers, and microservices create fragmented visibility. Siloed tools force teams to context-switch, slowing root cause analysis and release confidence.
Unified, full-stack observability delivers full-stack correlation and real-time topology mapping, providing a single operational view across dynamic environments for faster RCA and operational clarity.

Engineers waste time triaging alerts and patching systems manually, even with automation frameworks in place. This slows recovery and increases operational risk.
AI-driven observability enables closed-loop automation and AI-assisted remediation, detecting issues, applying policy-checked fixes, and verifying outcomes autonomously to reduce downtime and eliminate repetitive toil.

IBM Instana Intelligent Incident Investigation powered by agentic AI: Automation

Static policies and manual audits lag deployment velocity, leaving resilience and compliance reactive instead of proactive.
Continuous compliance and resilience scoring powered by predictive analytics and continuous posture evaluation ensures real-time visibility into operational risk, preventing incidents before they occur.

Illustration that shows the Instana infrastructure map and dashboard demonstrating the journey fromincident management to debugging

DevOps success depends on data, yet teams struggle to connect deployment metrics with business outcomes. This limits continuous improvement and accountability.
AI-driven analytics provide unified metrics across the lifecycle and real-time feedback loops, connecting technical performance with business impact to guide optimization and continuous improvement.

IBM Instana Intelligent Incident Investigation powered by agentic AI: Topology

Unify IT operations into one intelligent experience

Gain real-time observability, resilience posture management, and automated resource optimization powered by AI and automation.

Move from firefighting to foresight and deliver business value with every decision.

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Full-stack observability

Get real-time visibility across applications, services, and infrastructure with automated discovery and dependency mapping. Instana provides context-rich insights to accelerate root cause analysis and reduce MTTR—helping DevOps and SRE teams maintain application health and meet SLOs at scale.

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Resilience posture management

Identify risks early and automate remediation workflows to maintain uptime and compliance. By integrating observability with resilience posture management, teams can close gaps, track progress, and prevent outages, even during patching and change windows.

Illustration depicting transforming anomaly detection and resolution

Automated resource optimization

Gain full-stack visibility into application resource usage and continuously optimize compute, storage, and network resources. Automate provisioning and scaling to prevent contention and keep workloads within SLOs, ensuring consistent performance without manual intervention.

GenAI observability

Troubleshoot and govern agentic and LLM-powered applications. Bringing AI monitoring into the same powerful workflows teams already use for their full-stack applications. It automatically discovers and maps agents, chains and tasks, then connects their data to the rest of your infrastructure.