A Best Practices Guide to IT Infrastructure Monitoring
Introduction
Infrastructure monitoring forms the foundation of full-stack observability. While application performance monitoring (APM) reveals how code behaves and digital experience monitoring (DEM) illustrates the customer experience, infrastructure monitoring exposes the underlying platform health and the resource capacity that enables or constrains everything above it. When infrastructure fails, applications suffer, regardless of how well optimized the code is. This article covers best practices for monitoring on-premises, hybrid, and cloud infrastructure, integrating infrastructure data into full-stack observability, and leveraging machine learning (ML) and AI to reduce mean time to resolution (MTTR). The infrastructure components range from serverless functions to Kubernetes containers to virtual machines, networks, and storage systems. The covered solutions span from open-source tools to integrated platforms that offer unified visibility with AI-driven insights.
Modern vendor-agnostic platforms provide unified visibility across on-premises servers, networks, and storage systems and work with major cloud providers, helping ensure consistent insight in complex hybrid environments.
Summary of key IT infrastructure monitoring best practices
Best Practices
| Description |
|---|---|
Monitor all infrastructure components
| Track infrastructure across diverse environments while understanding their cascade effects and interdependencies. |
Establish hybrid cloud visibility
| Unify on-premises and cloud monitoring through universal agents and open standards to eliminate blind spots and reduce detection time. |
Define key metrics | Map infrastructure metrics (uptime, latency, throughput, utilization) to business service-level objectives (SLOs) using RED/USE frameworks to drive strategic decisions. |
Integrate data sources | Correlate metrics, logs, and traces via OpenTelemetry to move from symptoms to root cause, thereby reducing context-switching. |
Use intelligent alerting | Reduce alert fatigue through dynamic baselines, policy-driven thresholds, and suppression logic that preserves human attention for real problems. |
Leverage observability for troubleshooting | Enable bidirectional correlation between infrastructure and application layers to cut troubleshooting time from hours to minutes. |
Apply ML/AI for predictive insights | Deploy predictive AI for forecasting, generative AI for natural language analysis, and agentic AI for autonomous remediation to reduce MTTR. |
Monitor core infrastructure components
Compute, storage, and networking persist from mainframes to serverless, but monitoring complexity has exploded. A single transaction might touch 20 different compute instances across containers, VMs, and bare metal. Each layer creates dependencies and failure points. Understanding these layers in isolation isn't enough, as observability depends on recognizing how they interact in real time.
The key pillars of IT infrastructure and their challenges
Enterprise infrastructure spans diverse environments, each generating unique telemetry that must be unified for effective monitoring.
Infrastructure pillar
| On-premises monitoring challenges | Cloud monitoring challenges | What to track |
|---|---|---|---|
Compute | Physical servers, VMware ESXi, Hyper-V, Nutanix AHV: CPU scheduling delays, memory pressure, thermal fluctuations, VM contention | Instance life cycle events, autoscaling patterns, burst credits, and throttling in shared environments | Resource saturation, placement decisions, performance anomalies |
Storage | SAN arrays, NAS appliances, distributed file systems: I/O latency, controller utilization, cache efficiency, replication delays | EBS, Persistent Disks: IOPS limits, burst credits, throughput ceilings, zone-dependent latency | Read/write latency, cache hit ratios, storage pool utilization |
Networking | Switches, routers, firewalls, load balancers, WAN circuits: interface health, packet loss, jitter, QoS conflicts | Virtual networks, security groups, NAT gateways, inter-region routing: misconfigured rules, path changes | Latency distribution, bandwidth saturation, TCP retransmissions |
Organizations typically operate with Cisco/Juniper/Arista for networking, Dell EMC/NetApp/Pure for storage, and VMware/Hyper-V/Nutanix for virtualization, alongside Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP). Vendor-specific tools create blind spots. A unified platform such as SolarWinds normalizes telemetry across all systems, treating hypervisors, VMs, containers, and cloud instances as one compute layer.
Cascading interdependencies
These pillars rarely fail in isolation. For example, a memory leak in one container can trigger CPU throttling, causing network timeouts that fill storage with error logs. Another example is routing issues in the cloud that slow requests to an on-premises database. At the firewall level, an on-premises bottleneck can degrade cloud API performance. Monitoring must catch these chains before they cascade into outages.
SolarWinds® PerfStack™ enables this correlation by overlaying time-series metrics from servers, storage systems, network devices, hypervisors, and cloud resources on a single timeline. Engineers can visually connect cause and effect across layers, exposing the causal chain behind performance anomalies and reducing troubleshooting time from hours to minutes.
AppStack complements this with topology views that show how applications, servers, databases, storage, and virtual environments interrelate. For instance, when performance degrades, AppStack highlights which component is responsible.
For most organizations, infrastructure is an evolving mix of bare metal, VMs, containers, and cloud services. Each generates metrics at different cadences through various protocols. Unified monitoring normalizes this telemetry into a single data model, creating consistent baselines and enabling trend analysis across dissimilar environments.
Capturing these signals is just the start, however. In hybrid environments where workloads span data centers, clouds, and edge locations, teams must unify this telemetry across fragmented ecosystems.
Establish hybrid cloud visibility
While infrastructure monitoring principles apply universally across on-premises and cloud-native environments, hybrid configurations represent the predominant operational model for most organizations.
Today’s environments span on-premises infrastructure, virtualized platforms, public clouds, and edge locations. While cloud-native tools such as AWS CloudWatch and Azure Monitor excel within their ecosystems, the deepest operational complexity often lives on-premises, involving physical servers, hypervisors, storage arrays, and network fabrics that expose health through vendor-specific protocols. True hybrid observability requires deep, vendor-agnostic visibility into on-premises environments that extends seamlessly across AWS, Azure, GCP, and container platforms.
The challenge is that each environment generates its own telemetry formats and APIs, creating fragmented insights. Without unified visibility, teams diagnose these cross-environment issues by switching among disconnected tools, turning what should be a five-minute fix into an hour-long investigation
SolarWinds provides unified visibility across on-premises and cloud infrastructure (source)
Implementation tactics for unified visibility
Achieving hybrid visibility requires architectural design that unifies telemetry from physical hardware, virtualized systems, and cloud platforms without overwhelming teams with noise.
Deploy universal agents across all environments
Unified instrumentation must operate across legacy and modern systems:
Environment type
| Example of what to monitor |
|---|---|
On-premises hardware
| Servers, SAN/NAS arrays, switches, firewalls, load balancers |
Virtualized platforms
| VMware, Hyper-V, Nutanix, OpenStack |
Cloud workloads
| Kubernetes pods, managed databases, serverless functions |
Network devices | Routers, VPN concentrators, SD-WAN nodes |
These agents gather telemetry through multiple protocols: SNMP for network devices, WMI/WinRM for Windows systems, API polling for cloud services, and OpenTelemetry exporters for modern applications. Local preprocessing must be set to reduce noise to ensure that high-volume on-premises signals, such as storage I/O counters and hypervisor scheduling metrics, don't overwhelm downstream systems.
Agents should stream data to a central observability backend for complete cross-environment correlation and unified retention.
Consolidate with consistent metadata
Hybrid environments are observable when their components can be related to one another. Standardized tagging enables correlation between an on-premises VM cluster and its cloud-fronted API, between a physical database server and a cloud application tier, or between a firewall policy change and a spike in cloud latency.
Tag infrastructure with the application name, environment (prod, dev, staging), business service, and owner. This allows teams to view distributed components, such as an AWS checkout service and its on-premises database, as a single correlated system rather than two disconnected entities.
Adopt OpenTelemetry for interoperability
OpenTelemetry unifies diverse telemetry formats, particularly when combining deeply instrumented on-premises sources with cloud-native services. It provides a consistent data model for metrics, logs, and traces across vendors and prevents lock-in. Instead of managing separate proprietary pipelines for different environments, organizations gain a single standard that ties together their entire hybrid ecosystem.
Leverage service meshes for distributed applications
When applications span on-premises clusters and cloud workloads, service meshes such as Istio or Linkerd inject uniform observability into microservices traffic. They provide standardized tracing, traffic metrics, and error reporting across environments, filling visibility gaps for applications that operate across data center cores and cloud regions.
Leverage service meshes for distributed applications
When applications span on-premises clusters and cloud workloads, service meshes such as Istio or Linkerd inject uniform observability into microservices traffic. They provide standardized tracing, traffic metrics, and error reporting across environments, filling visibility gaps for applications that operate across data center cores and cloud regions.
Use cloud-based analytics for on-premises telemetry
Lightweight collectors installed in data centers can forward telemetry to cloud analytics engines. This approach enables real-time alerting, scalable historical data retention, and ML-driven anomaly detection, all without compromising the depth of on-premises monitoring. Teams gain cloud-scale analytics while maintaining granular visibility into physical infrastructure.
Adapt monitoring to environment-specific architectures
Different compute paradigms require tailored approaches:
Environment | Monitoring focus
| Key considerations |
|---|---|---|
Containers
| Auto-discover ephemeral workloads | Track pods that spawn/die in seconds; aggregate by service identity, not instance |
Serverless | Infer health from execution metrics | No host access, so rely on invocation duration, cold starts, and concurrency limits |
Edge computing | Local collection with sync resilience | Use store-and-forward buffers to prevent data loss during connectivity gaps |
Here’s how environment-specific monitoring tailors observability strategies to fit operational realities:
- In containerized systems, monitoring focuses on continuity despite resource churn
- In serverless environments, where there’s no persistent host, visibility depends on execution-level metrics and function traces
- At the edge, monitoring emphasizes reliability in low-connectivity conditions
Together, these adaptations help ensure that whether workloads are centralized, elastic, or distributed, teams can maintain complete contextual visibility, which is a fundamental requirement for full-stack observability.
Adaptive monitoring across modern compute environments.
Common hybrid monitoring scenarios
Unified visibility delivers tangible operational value across several scenarios. Here are some examples:
- Cross-environment root cause analysis: A global retailer operates e-commerce in the cloud while maintaining transaction databases on-premises for compliance purposes. During a sales campaign, customers experience slow checkout. Cloud monitoring reveals web layer latency but does not identify the root cause. SolarWinds correlates this with on-premises database I/O saturation, revealing that the bottleneck isn't in the cloud at all; instead, it's disk contention on the physical database server. Resolution time drops from hours to minutes.
- Disaster recovery and failover: Cloud services monitor the health of on-premises workloads to trigger automated recovery actions, spinning up replicas or rerouting traffic when on-premises conditions degrade. Unified observability makes failover events visible across all platforms, preventing blind spots during critical incidents.
- Cost optimization: Comparing utilization metrics across environments identifies where workloads run most efficiently. Compute-intensive batch jobs may be more cost-effective on dedicated on-premises hardware, while burstable web traffic benefits from cloud elasticity. This visibility turns cost optimization from guesswork into data-driven decisions.
Vendor-agnostic strategies and platform options
While OpenTelemetry provides the foundation for hybrid interoperability, platform choice determines how deeply organizations can monitor the full stack. Organizations typically operate diverse infrastructures: Cisco/Juniper/Arista for networking, Dell EMC/NetApp/Pure for storage, VMware/Hyper-V/Nutanix for virtualization, and AWS/Azure/GCP for cloud workloads.
SolarWinds strengthens hybrid visibility through:
- Native integrations with AWS, Azure, and GCP, alongside deep on-premises monitoring
- Unified dashboards combining cloud services with traditional infrastructure
- AppStack topology views showing how applications, servers, databases, and storage interrelate across environments
- PerfStack correlation overlaying metrics from on-prem arrays, hypervisors, and cloud resources on a single timeline
Server & Application Monitor (SAM) showing end-to-end visibility into business-critical applications (source)
This vendor-agnostic approach provides equal monitoring depth regardless of whether workloads run on bare metal, virtualized clusters, or cloud services, eliminating the blind spots that often fragment most hybrid environments.
Define key metrics
Primary metrics for infrastructure
- Uptime and availability: The most direct reflection of reliability, uptime metrics track system accessibility and are often tied to service-level agreements (SLAs); even small deviations can have contractual and reputational impacts
- Latency: The time it takes to process a request or return a response is a leading indicator of user experience; tracking latency across services, APIs, and databases helps identify performance degradation before it becomes visible to end users
- Throughput: This measures the amount of work the system can handle, typically expressed as transactions per second, requests per minute, or data processed per interval; high throughput is essential for capacity planning and scaling decisions
- Resource utilization: CPU, memory, and disk usage reveal how efficiently the infrastructure is being used; sustained high utilization may indicate the need for scaling, while underutilization can flag cost inefficiencies
- Error rates and saturation: Beyond basic health metrics, advanced indicators such as error frequency and saturation levels show when systems are operating near or beyond their designed limits, often serving as early warnings of instability
Monitoring frameworks for structured insight
Two frameworks bring consistency to monitoring, making sure data is interpreted within context:
- Rate, errors, duration (RED) for request-driven systems such as APIs and web services; RED focuses on request volume, failure rate, and response time, providing clear visibility into user-facing reliability
- Utilization, saturation, errors (USE) for infrastructure and resource components; this framework identifies bottlenecks and capacity issues by analyzing resource usage, saturation points, and error frequency
Cascade mapping: Linking infrastructure metrics to application SLOs
The most effective observability strategies go a step further: they connect infrastructure-level key performance indicators (KPIs) to application-level SLOs. This cascade mapping helps teams see how changes at the hardware or platform level ripple upward through the stack.
For instance, when disk latency exceeds 20 milliseconds, database queries might slow by a factor of two, causing API response times to breach defined SLO thresholds. When memory utilization consistently exceeds 85%, the frequency of garbage collection cycles could increase, potentially degrading the 99th-percentile latency for user transactions.
This type of mapping transforms raw data into diagnostic insights, revealing how underlying resources directly impact user-facing performance and reliability.
Mapping metrics to business objectives
Connecting infrastructure KPIs to business drivers transforms observability from a technical function into a strategic asset.
Infrastructure KPIs should reflect how system behavior affects customer satisfaction and revenue. Organizations typically align metrics to business impact by:
- Translating uptime SLOs into allowable downtime (e.g., 99.9% uptime = ~43 minutes/month)
- Quantifying latency costs (studies show even small latency increases reduce e-commerce conversions).
- Establishing error budgets that balance innovation speed with stability requirements.
Integrate data sources
- Metrics quantify performance over time
- Logs provide contextual detail explaining what happened
- Traces map dependencies across services
The cost of fragmentation
Correlation implementation strategy
An incremental approach to correlation: organizations begin by linking metrics and logs, then progressively add traces, database insights, and deployment context to achieve full-stack observability
OpenTelemetry as the integration standard
OpenTelemetry simplifies observability across servers, VMs, containers, Kubernetes clusters, cloud APIs, and network devices. It provides a consistent data model for metrics, logs, and traces, preventing vendor lock-in and helping ensure data portability.
Observability should be built in from day one, with every service instrumented for metrics, logs, and traces using consistent blueprint patterns. Progress can vary by organization, but each stage brings immediate benefits. The growing ecosystem of vendors supporting OpenTelemetry accelerates integration across diverse data sources.
Example: Users report increased latency in a global web application. Metrics show network packet loss in one region, while logs reveal connection timeout errors. Distributed traces show API calls stalling mid-transaction.
An integrated observability tool, such as SolarWinds Root Cause Assist, correlates these signals, revealing a misconfigured network route that causes packet retransmissions. The team resolves the issue within minutes, preventing a prolonged outage.
Probable correlated events in tabular format, which helps teams identify the series of events that might have led to the health state degradation. (source)
Unified correlation through PerfStack and AppStack
SolarWinds PerfStack lets teams view real-time metric data from multiple sources, servers, databases, network devices, and cloud workloads on a single interactive timeline. Engineers can drag and compare metrics side by side, visually connecting cause and effect across layers.
SolarWinds PerfStack (source)
The screenshot above shows the SolarWinds PerfStack drag-and-drop metric correlation dashboard that visualizes real-time metric relationships from multiple data sources on a single timeline. It enables operators to better focus on key issues without a deluge of telemetry data, helping teams make more informed decisions and be more productive.
SolarWinds AppStack provides a topology view of infrastructure dependencies, showing how applications, servers, databases, and virtual environments interrelate. When performance degrades, AppStack highlights the responsible component, reducing investigation time from hours to minutes.
SolarWinds AppStack Environment view (source)
The AppStack Environment view displays the status of individual objects in your IT environment through the SolarWinds Platform Web Console. Objects are categorized and ordered from left to right, with the worst status shown on the left side of the view. The illustration above shows how AppStack translates complex infrastructure relationships into a single, intuitive view. When used alongside PerfStack’s metric correlation timelines, AppStack closes the visibility gap between data points and dependencies, allowing teams to see not only what is failing but also where and why within the full application delivery chain.
Together, these tools exemplify how SolarWinds integrates multiple data layers, metrics, topology, and dependencies into a coherent observability model that accelerates root cause analysis across hybrid and multi-cloud environments.
Use intelligent alerting
Integrated observability generates comprehensive insights, but without the right alerting, it also generates overwhelming noise. Traditional static thresholds inundate teams with excessive notifications and insufficient context. Typically, most critical alerts are ignored when noise overwhelms teams. Modern alerting replaces rigid thresholds with context-aware, policy-driven logic tied to SLOs, user experience metrics, or business impact rather than arbitrary CPU limits.
Modern alerting relies on three pillars: threshold management, behavior learning, and alert noise control.
Aspect | Old way
| New way |
|---|---|---|
Threshold management | Static thresholds set manually, often based on arbitrary values (e.g., “Alert when CPU > 80%”). These thresholds quickly become outdated as environments change. | Policy-driven thresholds are tied to business impact, such as SLO violations or transaction failures. Policies define what “normal” looks like for each environment and evolve automatically with workload patterns. |
Behavior learning
| No adaptive learning; alerts trigger on any deviation, regardless of context, which leads to excessive false positives. | Dynamic baselining with ML: systems learn expected patterns (such as predictable CPU spikes during nightly backups) and only alert when deviations exceed statistically normal ranges. |
Alert noise control | High alert volume with many duplicates or irrelevant notifications; manual filtering is required. | Contextual suppression, where known noise (e.g., from maintenance windows) is automatically suppressed. Related alerts, such as latency, packet loss, and API timeouts from a single failing switch are correlated into one unified incident. |
Quality metrics for measuring alert effectiveness
Even intelligent systems need governance. Tracking the effectiveness of alerting policies enables continuous improvement and alignment with operational goals. Standard alert-quality metrics include:
- Signal-to-noise ratio: The proportion of actionable alerts versus total alerts generated
- Alert-to-incident correlation: How often alerts lead to confirmed issues or incidents
- Acknowledgment and response time: How quickly teams react to valid alerts (an indicator of operational efficiency)
Regularly reviewing these metrics helps teams tune thresholds, retrain ML models, and refine suppression logic, keeping alert volume meaningful and relevant.
As an example, consider a financial services trading platform that processes thousands of transactions per second. Static alerting creates noise from transient CPU spikes during background tasks.
With intelligent alerting, the system learns these predictable spikes and suppresses them unless CPU exceeds 95% (versus the normal 80%), persists 10+ minutes, and correlates with application errors. Later, when CPU hits 96% with rising error rates, the platform generates one high-priority alert. The team identifies a misconfigured thread pool affecting order execution. The result is fewer distractions, faster response, and reduced MTTR. SolarWinds enhances this by correlating alert conditions across infrastructure and application layers, surfacing the context behind critical events rather than isolated symptoms.
The role of ML and anomaly detection
ML-driven alerting learns behavior over time to recognize patterns, seasonal usage, and workload cycles. Instead of static limits, systems flag deviations that truly require attention.
For example, an anomaly detection model can identify a subtle increase in disk write latency that doesn't cross thresholds but statistically diverges from normal behavior. Flagging this early signal allows teams to act before performance becomes user-visible. Models improve accuracy by learning from incident feedback.
Leverage observability for troubleshooting
Learn From Production Failures
Your production environment shows failures that staging can't replicate, such as race conditions from double-clicked buttons or cascading failures from real traffic distributions.
Coordinate Incident Response
Automate Common Failure Recovery
It’s good practice to automate responses to common failures; for example, memory leaks could trigger rolling pod restarts at 90% utilization. Each repeated failure is a candidate for automation. If it happens twice, consider automating the recovery. Chaos engineering validates these automations through controlled experiments, such as terminating pods to confirm graceful degradation, injecting latency to verify timeouts, or simulating zone failures to test failover.
Turn Production Data into Development Priorities
As discussed earlier, production telemetry drives development priorities by revealing actual user impact, not merely synthetic test results. For example, when a new search algorithm increases latency by 30 ms, production monitoring quantifies the impact (e.g., a 2% decrease in conversion is worth $10,000 per day). This data changes vague performance concerns into concrete business decisions.
Another case is feature flag correlation, which hints at unexpected interactions between changes. A new recommendation engine might improve engagement by 15% but also increase database load by 40%, a trade-off you'd never discover in staging.
Similarly, A/B testing uncovers other surprises, like the fact that simplified checkout increases conversions but also drives more support tickets.
These production insights guide iteration and help you keep the benefits while fixing problems. Resource allocation optimization responds to actual usage patterns instead of capacity projections.
With intelligent alerting providing actionable context, the next step is leveraging full-stack observability for troubleshooting and root cause analysis. Modern environments are too distributed for manual, siloed investigation. Observability brings together metrics, traces, logs, and events to help teams move from symptom to cause with precision and speed.
A typical troubleshooting workflow spans multiple layers:
- User layer: A customer reports slow page loads
- Application layer: APM traces show increased transaction latency in a specific service
- Service layer: Distributed traces reveal a downstream database call bottleneck
- Infrastructure layer: Monitoring confirms high disk I/O latency on the database node
Without multi-layer visibility, teams see symptoms (missed SLOs, error spikes, etc.) without understanding the cause. Observability enables tracing problems across boundaries, accelerating diagnosis and resolution.
To see how observability accelerates troubleshooting across layers, consider a real-world scenario from a global e-commerce company experiencing intermittent checkout failures during a major flash sale:
- User layer: Customers report delays during payment processing and occasional “checkout failed” messages
- Application layer: Dashboards show rising transaction latency but no critical exceptions, suggesting the issue may be downstream
- Service layer: Distributed traces reveal that each affected transaction spends excessive time in a payment-processing microservice that depends on a shared database cluster
- Infrastructure layer: Infrastructure telemetry pinpoints the cause, which is that container nodes hosting the checkout service show steadily increasing memory usage, leading to kernel-level restarts and transient connection drops
By correlating these signals, the observability platform maps the chain of causation end to end: memory saturation at the infrastructure level, container restarts, dropped database connections, and slow or failed checkouts. The integrated traces and logs confirm that all failures align with automatic container evictions triggered by a memory leak introduced in the latest deployment.
Armed with this insight, the DevOps team rolls back the release, patches the faulty code, and redeploys within hours, preventing further revenue loss and helping ensure a seamless customer experience. What once required days of manual correlation across multiple monitoring tools is now achieved in a single observability workflow, showcasing how unified visibility transforms reactive troubleshooting into proactive resilience.
Bidirectional correlation patterns
- From infrastructure to application (predict before impact): You notice the database server memory utilization climbing to 90%; before users complain, you correlate this with application traces and discover query response times degrading, so you scale resources proactively
- From application to infrastructure (diagnose after impact): Users report slow checkout, and application traces show database query timeouts; you correlate this with infrastructure metrics and discover the database server’s memory is exhausted, causing disk swapping
Direction | Starting point
| Correlation reveals | Result |
|---|---|---|---|
From infrastructure to application | Database memory at 90% | Degrading query response times | Proactive scaling before user impact |
From application to infrastructure | Slow checkout complaints | Database memory exhaustion | Root cause identities in minutes |
Strategic benefits
Apply ML/AI for predictive insights
AI/ML capability | What it does | Example use case |
|---|---|---|
Anomaly detection | Learns “normal” system behavior and flags deviations that suggest early warning signs of performance degradation or failure | Detecting abnormal latency spikes or power usage patterns before an outage occurs |
Dynamic baselines | Continuously adjusts performance thresholds based on historical and seasonal trends | Automatically adapting CPU utilization limits during predictable peak hours |
Causal correlation
| Links symptoms to root causes by analyzing relationships among metrics, logs, and traces | Connecting increased API errors to the underlying database's slow queries |
Predictive forecasting | Uses historical data to predict future capacity or failure events | Forecasting storage saturation or network congestion before thresholds are breached |
Agentic AI/automated remediation | Moves beyond detection to execute self-healing workflows or suggest next best actions | Automatically restarting failing services or recommending configuration changes based on pattern analysis |
AI-enhanced root cause analysis and resolution
AI accelerates root cause analysis by correlating vast volumes of data that would take hours to investigate manually. When latency spikes across services, an AI model determines that all affected services share a dependency on the same overloaded database shard, presenting a prioritized, explainable diagnosis within seconds.
Tools like SolarWinds Root Cause Assist use AI-driven correlation to surface probable root causes, highlight impacted entities, and recommend fixes. This reduces MTTR and helps teams spend less time sifting through noise.
SolarWinds AI identifies the health state degradation issue as being caused by underlying infrastructure issues. (source)
Agentic AI and automated remediation
While AI-driven analytics and prediction have become mainstream, autonomous remediation remains an evolving frontier. Most organizations today rely on automated remediation, where predefined workflows or policies resolve known issues without human intervention. These automations are deterministic and controlled, helping ensure predictable responses aligned with governance and compliance frameworks.
Agentic AI, however, represents the next stage in this evolution: systems capable of learning from historical data and making contextual decisions beyond fixed playbooks. In theory, such AI agents could identify anomalies, predict their impact, and execute remediation steps autonomously. Yet, in practice, these capabilities remain rare and experimental, requiring extensive privilege and deep integration across infrastructure layers. They also introduce new challenges in security, auditability, and predictability that organizations must carefully manage.
Looking ahead, the combination of predictive analytics, rule-based automation, and supervised agentic intelligence offers a balanced path forward. This approach keeps remediation safe, explainable, and governed while gradually introducing adaptive learning to refine actions over time. The result is an operations model where observability platforms not only detect and predict issues but also drive measured, intelligent self-healing across complex hybrid systems.
Final Thoughts
Modern IT ecosystems span data centers, clouds, and edge environments that constantly evolve. Infrastructure monitoring serves as the unifying layer that powers end-to-end visibility, connecting every part of the technology stack and helping ensure teams understand how infrastructure behavior influences application performance and the user experience.
Effective monitoring requires a cohesive strategy: unify telemetry across hybrid environments, define actionable metrics aligned with business goals, integrate data sources to eliminate silos, and deploy intelligent alerting. Observability practices enable faster troubleshooting, while AI and machine learning elevate monitoring from detection to prediction.
As infrastructure scales and diversifies, operations must shift from reacting to incidents toward orchestrating performance and reliability as ongoing outcomes. With AI-assisted observability, organizations evolve into proactive, self-optimizing systems that deliver resilience, efficiency, and customer trust. When unified visibility is combined with intelligent automation, monitoring becomes a cornerstone of operational excellence. With vendor-agnostic coverage across major clouds and on-premises infrastructure, SolarWinds helps organizations build a unified observability foundation that supports reliability at scale.