Modern software systems are complex, distributed, and in a constant state of evolution.
Before something goes wrong, engineering teams need more than a dashboard of colored dots to understand what is happening. They need observability signals: the rich telemetry data that reveals a system's internal state from its external outputs so that they can optimize performance.
In this guide, we'll break down the three core observability signals: metrics, logs, and traces, and explain how they relate to the four golden signals of site reliability engineering:
Latency (Time taken)
Traffic (Demand)
Errors (Failure rate)
Saturation (Capacity)
We'll include everything you need to know about enterprise observability from signals to business outcomes - and provide a practical decision framework for choosing the right signals for your environment.
"Observability is not about collecting more data - it's about understanding the relationships between the data you already have. Correlation is the capability that separates effective observability from sophisticated data collection." Michael Tomkins, CTO, IR
Find out more about Enterprise Qbservability in our guide:
What is Enterprise Observability? Benefits, Strategy, and Tools – 2026 Guide
Observability signals (often called telemetry) are the external outputs produced by a system, such as software applications, infrastructure, or networks. Observability signals enable engineers to understand its internal state.
In modern distributed systems, these signals are essential for identifying, investigating, and resolving "unknown unknowns", unpredictable issues which impact system reliability, and that traditional, reactive monitoring can't detect
Logs, metrics and traces are the "three pillars of observability" in modern software development. They provide different lenses to understand the health and behavior of systems, resource utilization, and system stability.
Logs tell you why the problem happened (detailed event records).
Metrics tell you if there is a problem (numerical, aggregated data).
Traces show you where the problem is (request flow through services).
|
Feature |
Metrics |
Logs |
Traces |
|---|---|---|---|
|
Data Type |
Numerical (time-series) |
Text/Structured Events |
End-to-end Request Flow |
|
Purpose |
Monitoring, Alerting |
Troubleshooting, Audit |
Performance Tuning |
|
Volume |
Low |
High |
Medium/High (sampled) |
|
Example |
CPU usage, Request Rate |
"Error: Invalid User ID" |
Order Service -> DB |
|
Best For |
"Is the system up?" |
"What happened at 2 AM?" |
"Why is this request slow?" |
Modern distributed systems are far too complex to analyze in isolation. To have a clear picture of what’s happening beneath the surface, teams depend on the core pillars of observability - logs, metrics, and traces to interpret system behavior from the signals it produces.
Telemetry data reaches your observability platform through three primary mechanisms:
Lightweight software installed on hosts or containers that collects infrastructure metrics such as CPU usage, memory usage, and network throughput, plus logs from the host OS and applications.
Many cloud-native services and network devices expose metrics and logs through APIs that observability platforms poll at regular intervals.
Application code is instrumented using OpenTelemetry SDKs. This is the vendor-neutral, CNCF-standard framework that has become the default for collecting telemetry data across metrics, logs, and traces in modern software systems.
OpenTelemetry's widespread adoption reduces vendor lock-in and simplifies managing multiple tools across complex systems.
The combination of agent-based collection for infrastructure and SDK-based collection for application code gives engineering teams comprehensive visibility from the network layer through to individual function calls.
Not all observability data is created equal, so storage costs can vary significantly by signal type.
Metrics are aggregated numerical values, making them cheap to store at scale.
Logs contain rich text data and can accumulate rapidly in high-traffic environments, making log data one of the largest drivers of observability platform costs.
Distributed tracing generates spans for every request, meaning that in high-volume systems, capturing every trace is neither practical nor cost-effective.
Logs are the most contextually rich observability signal, and the most expensive to manage at scale.
In a large distributed system generating millions of events per minute, log data volumes can reach terabytes per day. Most commercial observability platforms price log ingestion and retention by volume, meaning that unmanaged log data collection is often the single largest driver of observability budget overruns.
Teams operating at enterprise scale should implement structured logging - or machine-parseable JavaScript Object Notation (JSON) rather than unstructured text. Additionally, log-level policies will reduce verbosity in production environments, and tiered retention strategies can move older log data to lower-cost cold storage.
Distributed tracing at full fidelity in high-throughput systems is impractical from both a cost and a performance perspective. The two primary sampling approaches each carry trade-offs:
Head-based sampling: The decision to sample a trace is made at the point of ingestion, before the outcome is known. This is simple to implement but may under-sample rare error conditions.
Tail-based sampling: The decision is made after the trace completes, allowing teams to prioritise traces that contain errors or latency outliers. Tail-based sampling is more diagnostically valuable but requires buffering spans before the sampling decision is made, which introduces infrastructure complexity.
A practical approach for most enterprise environments is to capture 100% of error traces and apply tail-based sampling to successful requests, retaining a statistically meaningful sample for performance analysis while controlling costs.
Enterprise observability transforms t raw technical signals, the logs, metrics, and traces into tangible business outcomes by providing contextual, real-time insights that map technical performance directly to business key performance indicators (KPIs).
For enterprise IT teams, observability is not an engineering concern in isolation, it's a business-critical capability.
When a contact center platform degrades, every minute of reduced performance translates directly into customer experience impact and revenue loss. Correlated observability signals allow teams to move from detection to root cause analysis in minutes rather than hours.
In practice, this means correlating system metrics to isolate the root cause without manually correlating data across multiple tools:
For example: Rising latency on a payments gateway, or authentication errors appearing in the application logs, or distributed traces such as a third-party API call timing out on 12% of requests
|
Metric |
Traditional Monitoring |
Enterprise Observability |
|
Incident Detection |
15-60 minutes (after user reports) |
Seconds to minutes (automated) |
|
Root Cause Identification |
1-3 hours (manual investigation) |
Minutes (automated analysis) |
|
Mean Time to Repair (MTTR) |
2-4 hours |
45-90 minutes |
|
False Positive Rate |
40-60% |
<10% (with tuned AI) |
Read more on
How to Reduce MTTR with AI: A 2026 Guide for Enterprise IT Teams
Enterprise unified communications (UC) ecosystems present a particularly complex observability challenge.
A single contact center environment may span multiple vendors — SIP trunking providers, cloud UC platforms, contact center as a service (CCaaS) solutions, and on-premises hardware, none of which share a common observability framework.
Traditional monitoring tools that inspect individual components in isolation are insufficient for these environments. Effective observability to maintain system reliability across multi-vendor UC ecosystems requires:
Modern observability platforms are designed to handle complex, cloud-native environments and provide crucial management and workflow features aimed at reducing Mean Time to Resolution (MTTR), improving developer experience, and controlling operational costs
One of the underappreciated challenges of implementing observability in large enterprises is organizational, not technical.
IT operations, DevOps, and network engineering teams often use different tools, speak different diagnostic languages, and have different definitions of what constitutes a performance issue. Modern observability platforms address this by providing shared dashboards, unified alerting workflows, and collaborative incident management that bridges these organisational silos.
Effective observability practices require agreement on key metrics and shared alert thresholds before a platform is deployed, not after. The platform is then the mechanism for enforcing those agreements at scale.
The observability tools landscape has matured significantly with the emergence of OpenTelemetry as the de facto standard for telemetry data collection.
OpenTelemetry's vendor-neutral instrumentation libraries mean that organisations can implement observability once in their application code and route data to any compatible observability platform — reducing the risk of tool-driven lock-in.
Enterprise observability platforms should integrate natively with major cloud providers (AWS CloudWatch, Azure Monitor, Google Cloud Operations), CI/CD pipelines, ITSM tools, UC platforms and contact center solutions. The goal is a unified observability platform that eliminates the need to context-switch between multiple tools during incident investigation.
A robust decision framework for observability prioritizes signals based on business impact, user experience, and system architecture. The framework requires moving from "monitoring everything" to "understanding what matters.
Metrics are numerical representations of data measured over time, making them efficient, cheap to store, and ideal for high-level monitoring.
Proactive alerting: When you need to detect issues via automated alerts based on thresholds (e.g., CPU > 90%, Error Rate > 1%).
System health monitoring: When you need a "dashboard" view of system behavior, such as throughput, response times (latency), or uptime.
Capacity planning & trends: When analyzing resource usage patterns over long periods to decide when to scale infrastructure.
Low-overhead requirements: When you cannot afford the performance overhead of logging every single event.
Logs are detailed, timestamped records of discrete events. They provide the context needed when a metric indicates a problem.
Debugging & troubleshooting: When an alert has fired and you need to see the exact error message, stack trace, or state of the application.
Root cause analysis (RCA): When you need to know exactly why a transaction failed or a specific user encountered an error.
Compliance & auditing: When you need an immutable record of actions taken for security, fraud detection, or regulatory requirements.
Anomalous event investigation: When investigating rare, unpredictable events that metrics cannot capture.
Traces (specifically distributed traces) follow a request through a complex, distributed system, capturing the path and time spent in each service.
Microservices/distributed architectures: When a user request traverses multiple services, containers, or serverless functions.
Diagnosing latency/slow performance: When you need to pinpoint which service, database query, or network hop is causing a slowdown.
Uncovering hidden dependencies: When you do not know how services interact or how a slow, asynchronous background job affects the frontend.
Bottleneck identification: When you need to visualize the entire request workflow to find where it is stuck.
Unified observability means ingesting, storing, and correlating metrics, logs, and traces in a single platform.
High-complex environments: In cloud-native systems, where individual components are ephemeral, making it difficult to rely on disparate tools.
Reducing Mean Time To Resolution (MTTR): When you need to switch quickly between a high-level metric graph to a specific trace, and finally to the detailed log line without changing context.
"Unknown unknowns": When you need to investigate issues you didn't know to look for, requiring the ability to correlate different types of telemetry.
Breaking down silos: When Dev, Ops, and SRE teams need a single source of truth to collaborate on investigations.
"Start small: Start small by testing on a limited set of services to minimize risk. Use auto-instrumentation... Don't rush into full-scale deployment: Understand components before scaling." Michael Tomkins, CTO, IR
The right combination of observability signals depends on the complexity of your architecture, your team's maturity, and the business outcomes you are trying to protect. The following framework provides a starting point:
|
Environment |
Primary Signal Need |
Recommendation |
|
Small / startup team |
Metrics + structured logs |
Start with key metrics and centralized logging before adding tracing |
|
Cloud-native microservices |
Distributed tracing + metrics |
OpenTelemetry-native instrumentation with full trace correlation |
|
Enterprise UC & contact centre |
All three signals + events |
Unified observability platform with cross-domain correlation |
|
High-volume payments |
Metrics + traces + audit logs |
Low-latency metrics for alerting; traces for transaction flow; logs for compliance |
|
Hybrid infrastructure |
Metrics + logs |
Infrastructure metrics from agents; logs aggregated centrally for IT systems |
As a general principle: start with metrics to establish visibility, add structured logging to enable root cause analysis, and introduce distributed tracing once your architecture involves multiple interconnected services.
Unified observability, or correlating all three signal types in a single platform becomes essential as system complexity, team size, and business criticality increase.
Want to find out more? Read on here
AI Observability: Complete Guide to Intelligent Monitoring (2025)
Moving beyond tool sprawl to unified observability involves consolidating fragmented monitoring tools into a single, cohesive platform that leverages AI and automation to provide end-to-end insights across hybrid, multi-cloud environments.
Managing multiple tools across complex, multi-vendor environments creates exactly the kind of fragmented visibility that observability is supposed to eliminate.
IR's unified observability platform brings together metrics, logs, and traces across unified communications, payments, and infrastructure environments. We deliver the correlated, end-to-end view that modern enterprise environments demand.
From contact center platforms handling tens of thousands of concurrent interactions to hybrid infrastructure spanning on-premises and cloud environments, IR provides independent, vendor-agnostic observability that connects technical signals directly to business outcomes.
Research shows enterprises using AI-driven observability solutions like IR Collaborate and IR Transact can reduce Mean Time to Repair (MTTR) by approximately 40-60%. What previously took 2-3 hours of manual investigation now happens in minutes, allowing teams to move directly to remediation.
Meet Iris - Your all-in-one solution to AI-powered observability
Q: What is AI observability?
AI observability is the process of continuously collecting and analyzing data, including logs, metrics, and traces from AI systems to understand their internal state, model performance, model inference, and behavior.
This allows IT teams to gain critical insights into how AI systems behave, and diagnose performance issues in real time. As AI is embedded in more enterprise applications, AI observability is becoming an important extension of standard observability practices, helping teams detect model drift, data quality issues, and unexpected model behavior before they affect business outcomes.
Q: What are observability signals?
Observability signals are the data outputs - metrics, logs, and traces - that a system produces to reveal its internal state.
They allow engineering teams to understand system behavior, identify performance issues, and diagnose root causes without requiring direct access to the system's internals. Collectively referred to as telemetry data, these signals are the foundation of any effective observability strategy.
Q: What is the difference between monitoring and observability?
Traditional monitoring tells you when something is wrong, typically through predefined metrics and thresholds.
Observability tells you why something is wrong by providing the rich, correlated data needed to investigate unknown failure modes. Monitoring is reactive; observability is exploratory. In complex distributed systems, monitoring alone is insufficient because it cannot capture the novel failure conditions that emerge from service interactions.
Q: What are the three pillars of observability?
The three pillars of observability are metrics, logs, and traces. Metrics provide quantitative data about system performance over time. Logs provide detailed, timestamped records of discrete events. Traces map the journey of individual requests through distributed systems.
Each pillar answers a different question: metrics reveal what is happening, logs explain why it happened, and traces show where in the system the problem originated.
Q: What are the four golden signals in SRE?
The four golden signals introduced in Google's Site Reliability Engineering handbook, are latency, traffic, errors, and saturation. They represent the four metrics most indicative of user-facing system health.
Latency measures request response times; traffic measures demand volume; errors measure failure rates; saturation measures resource constraint levels. They sit within the metrics pillar of observability and typically serve as the alert triggers that prompt log and trace investigation.
Q: Are metrics enough for troubleshooting?
Rarely. Metrics are necessary for detecting and alerting on performance degradation because they tell you something is wrong and approximately when it started. However, they do not carry the contextual detail needed to identify root causes in complex systems.
For effective troubleshooting, metrics should be correlated with logs (for event-level detail) and traces (for request-flow visibility). In simple, single-service architectures, metrics and logs together are often sufficient.
Q: Why are traces important in microservices?
In a microservices architecture, a single user request may traverse dozens of services. When performance degrades, identifying which service, or which interaction between services is responsible, is extremely difficult without distributed tracing. Traces capture the full journey of a request as a series of spans, revealing latency at each hop and making it possible to isolate performance bottlenecks that would be invisible in metrics or logs alone.
Q: What is OpenTelemetry?
OpenTelemetry is a vendor-neutral, open-source framework for collecting telemetry data from applications and infrastructure.
Maintained by the Cloud Native Computing Foundation (CNCF), it provides a standardized set of APIs, SDKs, and instrumentation libraries that allow teams to instrument application code once and route observability data to any compatible backend. OpenTelemetry has become the industry standard for telemetry data collection and significantly simplifies managing multiple observability tools.
Q: How does observability reduce MTTR?
Mean Time to Resolution (MTTR) is reduced by enabling faster root cause analysis. When metrics, logs, and traces are correlated in a unified observability platform, engineers can move from alert to diagnosis without manually cross-referencing data from multiple tools. I
nstead of spending 80% of incident time gathering data, teams can focus on remediation. In enterprise environments with complex, multi-vendor architectures, correlated observability signals can reduce MTTR from hours to minutes.
Q: Is observability only for cloud-native systems?
No. While observability practices originated in cloud-native and microservices contexts, they apply equally to on-premises infrastructure, hybrid environments, and legacy IT systems.
Any system that produces telemetry data, including network devices, unified communications platforms and mainframes, can benefit from structured observability practices. The tooling and instrumentation approaches differ, but the underlying principle of understanding system behavior through its external outputs is universally applicable.