IT Infrastructure Metrics: What to Track and Best Practices

With mountains of infrastructure data spread across tools, dashboards, and alert rules, teams often have to jump between tabs and connect the dots in real time when an incident hits.

Effective infrastructure monitoring isn’t about tracking every possible metric. It’s about focusing on the signals that best explain user impact, service health, and operational risk, then making them easy to correlate in one place.

In this guide, we’ll cover which infrastructure metrics matter most, how they relate to KPIs and SLIs/SLOs, and how to build a metrics program that helps teams troubleshoot faster and make better decisions under pressure.

Key takeaways

• Infrastructure metrics are raw signals about system health. They become useful when you connect them to user experience, reliability goals, and business outcomes.
• You do not need to track every metric. Start with a focused checklist for compute, containers, storage, network, databases, and cloud services.
• Metrics, KPIs, SLIs, and SLOs each measure different layers of performance. You need all four to move from detection to diagnosis to prioritization.
• Reducing noise requires baselines, sensible aggregation, and alerts designed for action.
• With New Relic, teams can bring telemetry together in one place, connect signals across layers, and speed up troubleshooting with AI-assisted analysis.

What are infrastructure metrics?

IT infrastructure metrics are critical signals that highlight the health and performance of systems, allowing teams to identify and address potential issues before they escalate. These metrics capture key indicators, such as when a node is overloaded, a database is nearing capacity, or network latency is increasing between services.

By offering this visibility, metrics empower teams to maintain stability and optimize infrastructure performance. They could be signals such as:

• CPU usage
• Memory
• Disk
• Network
• Throughput
• I/O latency to error rates across hosts
• Containers
• Databases
• Cloud providers

These are foundational to infrastructure monitoring because they tell you when a node is overloaded, a database is constrained, or network latency is rising between services. But a metric by itself rarely tells the full story.

A CPU graph at 90% utilization, for example, is only useful when you know which workload caused the spike, whether user-facing latency increased at the same time, and whether that pattern is normal for that hour or workload.

That’s why the goal is not just to collect infrastructure metrics, but to turn them into actionable signals that explain what is happening, why it matters, and what to do next.

Metrics vs. KPIs vs. SLIs/SLOs: Understanding measurement layers

It's easy to conflate metrics, KPIs, and SLIs/SLOs. But they operate at different levels of abstraction, and mixing them up can lead to confusion about what you're actually measuring and why. 

Each layer serves a different purpose in IT management:

• Metrics: Raw telemetry like CPU, memory, error counts, and request duration—these give you the foundational data to understand system performance
• Key performance indicators (KPIs): Business-facing aggregations like checkout success rate or cost per transaction—these connect technical performance to business goals and help justify infrastructure investments
• Service level indicators (SLIs): Reliability measurements such as "percentage of requests under 300 ms"—these quantify user experience so you can track whether your service is meeting expectations
• Service level objectives (SLOs): Targets for SLIs, like "99.9% of logins succeed within 300 ms over 30 days"—these set clear reliability goals that balance user needs with engineering effort and guide where to focus improvements

In New Relic, you can define SLIs and SLOs on top of your metrics, then drill from an SLO breach down to the infrastructure bottleneck—linking endpoints, services, queries, and resource constraints in one view.

Which IT infrastructure metrics should you track and where?

You can track thousands of metrics from any modern platform, but that doesn't mean you should. Every new metric competes for attention, increases cardinality, and makes it harder to see what matters during incidents and disruptions.

A better approach is to select a focused set of metrics for each infrastructure type your application runs on, based on three questions:

• Does it map to user experience? Latency, availability, and error rates directly affect how users perceive your service.
• Does it tie to business outcomes? Checkouts, API usage, and SLA compliance connect technical performance to what the business cares about.
• Does it help you debug? Metrics that surface root causes during incidents earn their place; metrics that only add noise don't.

If a metric doesn't clearly answer at least one of these, it's a candidate for cutting. Start with the metrics below for the components in your stack, then expand only when incidents reveal real gaps.

Practical metrics for each infrastructure type

For specific infrastructure types, track a small set of metrics that you can act on to spot problems early and reduce avoidable downtime.

Compute (VMs, bare metal, cloud instances)

• CPU utilization: Track per-host and per-service CPU usage. Sudden spikes often correlate with latency or throttling higher in the stack.
• Memory usage: Watch used memory, free memory, and swap usage. Gradual growth can indicate leaks, and sustained high usage can cause OOM kills.
• System load average: On Linux, load shows how many processes are waiting on CPU or I/O. A load consistently higher than CPU cores is a red flag.

Containers and Kubernetes

• Pod restart rate: Frequent restarts usually mean crashes, OOMs, or failed liveness/readiness checks. This is often your first “something is wrong” signal.
• Container CPU throttling: If Kubernetes and other containers hit CPU limits, the kernel will throttle them, which shows up as latency or timeouts for your users.
• Node capacity and utilization: Track allocatable CPU/memory against requested and used resources to avoid overcommit and scheduling failures.

Storage

• Disk utilization percentage: Low free space can cause write failures, log truncation, or node crashes. Track both root volumes and data volumes.
• Read/write latency: Even small increases in storage latency can cascade into slower APIs and timeouts, especially for databases and queues.

Network

• Latency / round-trip time (RTT): Measure latency between critical services, not just to the internet. Internal network issues often don’t show in generic ping checks.
• Packet loss rate: Even small packet loss on chatty protocols (like HTTP/2 or gRPC) can cause big performance issues.
• Error rate: Track connection errors, resets, and retransmits. Spikes here often show up before application-level errors.

Databases

• Query response time: Monitor p50/p95/p99 query latency for key operations. Tie this back to user-facing endpoints where possible.
• Connection pool utilization: A saturated pool causes cascading timeouts. Track max connections, in-use connections, and wait times.
• Replication lag: In replicated setups, lag can cause stale reads and consistency bugs that are hard to reproduce.

Cloud services (managed APIs, queues, gateways, etc.)

• API request rate: Requests per second (or per minute) per endpoint or operation helps you detect traffic anomalies and plan capacity.
• Error rate by endpoint: Break down by status code or result type (e.g., 4xx vs. 5xx) to differentiate client issues from provider or configuration errors.
• Service quota consumption: Monitor usage against cloud provider limits so you don’t discover quota ceilings during peak traffic.

You can pull these IT infrastructure metrics from host and container agents (like New Relic infrastructure agents and Kubernetes integrations), cloud provider APIs (like CloudWatch, Azure Monitor, or GCP Cloud Monitoring), or OpenTelemetry collectors exporting to a centralized platform.

The key is to bring them into a single place so you can correlate them with logs, traces, and higher-level service metrics without juggling tools—turning fragmented telemetry into a unified view that speeds up troubleshooting and reduces context switching during incidents.

How to build an effective IT infrastructure metrics program

Collecting metrics is easy. Building a program that scales with your systems and IT team requires structure: a way to connect low-level telemetry to reliability targets and business outcomes so you can make informed decisions. A good program follows four steps:

1. Define service outcomes and targets

Before choosing metrics, decide what "healthy" means for your key services across five dimensions:

• Availability: uptime per service
• Latency: acceptable response times
• Capacity: traffic thresholds before degradation
• Cost: per unit of work
• Risk: blast radius of failures

Each dimension becomes an SLO, and each SLO maps to a specific set of infrastructure metrics. For example, a latency objective like "99% of checkout API requests complete in under 400ms over 30 days" translates directly into metrics worth tracking: p99 API response time, database query latency, connection pool wait time, and network RTT between the checkout service and its dependencies.

This mapping, outcome to SLO to metric, is what keeps a metrics program focused. If a metric doesn't ladder up to a service outcome someone cares about, it's probably not worth the cardinality cost.

2. Instrument and collect telemetry

Instrument based on what you care about, not everything available. Choose from:

• Agent-based instrumentation (fastest path to standard metrics and traces)
• Agentless sources (cloud APIs and managed services)
• OpenTelemetry (for polyglot and multi-cloud environments)

3. Normalize, aggregate, and set thresholds

Raw metrics from different systems rarely align. Make telemetry usable by:

• Normalizing naming and labels
• Focusing on golden signals (latency, traffic, errors, and saturation)
• Establishing baselines from historical data
• Accounting for seasonality in your thresholds

4. Operationalize action

Metrics only matter when they drive decisions. Tie your program into workflows with:

• Role-specific dashboards
• Alert routing to the right teams
• Runbooks linked to alerts
• Capacity planning based on trends
• Executive summaries of uptime and SLO performance

Best practices for IT infrastructure metrics that reduce noise and improve reliability

As your estate grows, the biggest risk is getting lost in the metrics. Poorly managed metrics explode cardinality, drive up costs, and worsen your signal-to-noise ratio. A few disciplined practices can help:

Start with user impact, not system activity

Begin with critical user journeys, such as search, checkout, login, and API usage, and define what "good" means for each. Work backward to identify which infrastructure components sit on those paths, then instrument only the metrics that explain or predict changes in user-facing performance. Add counters surgically during investigations, then decide if they're worth keeping.

Establish baselines before setting thresholds

Static thresholds like "CPU > 80%" are quick to configure and almost always noisy. Instead, collect a few weeks of data, use percentiles for latency, and account for known peaks before defining ranges. 

Design alerts for actionability, not awareness

If an alert doesn't tell you what to do next or who should care, it isn’t valuable. Make alerts actionable by including clear ownership, short descriptions of what the condition means, links to dashboards and runbooks, and severity levels that match business impact.

Regularly audit and refine your metrics strategy

Even well-designed programs drift over time. Build a lightweight quarterly review to prune unused dashboards and alerts, consolidate overlapping metrics, and remove high-cardinality dimensions that are expensive but rarely queried.

Build a sustainable IT infrastructure metrics program 

Effective IT infrastructure metrics programs share a few common principles, regardless of stack or team size:

• Prioritize clarity over volume. More metrics don't mean better visibility. A focused set of signals per infrastructure type will almost always outperform a sprawling catalog of counters.
• Start with user impact, not system activity. Work backward from critical user journeys to the components that support them, and instrument only the metrics that explain or predict changes in user-facing performance.
• Establish baselines before setting thresholds. Static cutoffs like "CPU > 80%" create noise. Historical data and seasonality produce alerts that actually mean something.

Connecting these signals to KPIs and SLIs/SLOs turns detection into prioritization, and a lightweight quarterly audit keeps dashboards, alerts, and metric cardinality from drifting over time. When telemetry is unified and contextualized in a single platform, teams can shift from reactive firefighting to proactive system optimization, staying in flow rather than chasing false signals.

New Relic helps teams bring metrics, logs, traces, and events together in one place so they can move from symptom to cause faster. With support for OpenTelemetry, broad integration coverage, and AI-driven capabilities for anomaly detection and issue correlation, teams can reduce tool sprawl, cut alert noise, and troubleshoot with more context.

Ready to streamline your monitoring strategy? Request a demo to see how New Relic can help you simplify monitoring, improve signal quality, and resolve issues faster.

FAQs about IT infrastructure metrics

What IT infrastructure metrics should I start tracking first?

Start with user-facing metrics: request rate, latency, and error rate for key services, plus CPU, memory, and disk I/O for underlying hosts. Add database query latency, connection pool usage, and inter-service network latency. Expand only where incidents reveal blind spots.

What's the difference between IT infrastructure metrics and application metrics?

IT infrastructure metrics describe underlying platforms, like hosts, containers, databases, storage, and networks, while application metrics focus on code behavior like latency and errors. You need both: infra metrics show platform health, and application performance metrics show user-facing efficiency. Correlating them reveals whether slowdowns or outages stem from code, data, or resource constraints.

How do I know if my IT infrastructure metrics are actually useful?

Your metrics are useful if they help you answer questions quickly during incidents and support decisions during calm periods. Do on-call engineers use them to find a root cause? Do they appear in post-incident reviews, capacity planning, and SLO tracking? If not, deprioritize them so you can focus on higher-value signals.