What is Observability?

What is Observability and Why Is It Critical for Modern Cloud-Native and AI Systems?

Observability is the modern approach to understanding the real-time health, performance, and behaviour of complex distributed systems. As enterprises adopt cloud-native architectures, microservices, AI agents, and high-velocity DevOps practices, traditional monitoring is no longer enough. Observability empowers engineering, SRE, platform, and operations teams to gain deep visibility into every component of the system by analysing logs, metrics, traces, events, and contextual metadata together. It helps teams diagnose failures faster, ensure reliability at scale, and maintain consistent performance across on-prem, cloud, and edge environments.

In the era of Agentic AI and autonomous infrastructure, observability plays a critical role in enabling intelligent decision-making and automated remediation. With platforms like Nexastack, organisations can integrate observability into their private cloud AI, sovereign AI environments, and agent-driven workloads, ensuring that every inference, workflow, or multi-agent action is tracked with full transparency and governance. This level of insight is crucial for industries such as manufacturing, robotics, where system reliability, compliance, and operational continuity are mission-critical.

Modern observability goes beyond dashboards—it provides actionable insights through correlation, anomaly detection, dependency mapping, and AI-powered root-cause analysis. It accelerates incident response, improves uptime, optimises performance, and supports high-scale automation. By embedding observability into the entire lifecycle—from development to production—enterprises can build a resilient, predictable, and self-healing digital ecosystem.

Ultimately, observability is the foundation for reliable engineering, scalable AI systems, and autonomous operations, enabling enterprises to move confidently from reactive monitoring to proactive, intelligent control.

Key Takeaways

• Observability = understanding system internals through logs, metrics, and traces — without code changes.
• Traditional monitoring reacts to known failures; observability explores unknown ones.
• The three pillars are Logging, Metrics, and Tracing — each serves a distinct diagnostic purpose.
• SRE teams use observability to define and enforce SLOs, reduce MTTR, and protect error budgets.
• In agentic AI and cloud-native systems, observability is the foundation for automated remediation and sovereign AI governance.

The global observability market was valued at USD 2.9 billion in 2025 and is projected to reach USD 6.93 billion by 2031, growing at a CAGR of 15.62%. (Source: Mordor Intelligence, 2025)

What is Observability in simple terms?
Observability is the ability to understand what is happening inside a system by analysing its outputs like logs, metrics, and traces.

Why is Observability Important in Modern Enterprise Infrastructure?

Observability is a way to get insights into the whole infrastructure. It is essential for the operations team. It means assembling all fragments from logs and monitoring tools and organising them in such a way that gives actionable knowledge of the whole environment, thus creating insight. It is a combination of multiple items to create a deep understanding of the actual health, real issues, and what should be done to improve the environment and troubleshoot at a root level.

A tool that enables end users, administrators and organizations to gauge and evaluate the performance of a given system. Click to explore about, Performance Monitoring Tools.

This means the service can explain any questions about what is happening inside the system just by observing the outside of the system without entering new code to answer further questions. Software is enhancing exponentially extra complexity. It is a term from the control method. It is a test of how well the inner states of a system can be assumed by knowledge of its external outputs. The Observability and controllability of a system are numerical duals. It might mean various points to various characters. For unusual, it's about logs, metrics, and traces. To others, it's the first wine of monitoring in a fresh container.

• Creating and operating an extra-visible system

• A system that can explain itself without the need to deploy new code

• Understanding connections between parts of your background or System

• No complex system is always healthy

• Categorised systems are pathologically unpredictable

Monitoring vs Observability: What Is the Difference?

A Platform for Monitoring, Logging, Tracing and Visualization of Big Data Cluster and Kubernetes with ML and Deep learning. Click to explore, Observability for Kubernetes

Why is Observability Important for SRE and Enterprise Reliability?

In enterprise environments, observability helps cross-functional teams understand and answer specific questions about what’s happening in highly distributed systems. Observability lets you understand what is slow or broken and what you must do to improve performance. With an observability solution, teams can receive alerts about issues and proactively resolve them before they impact users.

Site Reliability Engineering Approach

Site reliability engineering (SRE) is Google's way of service management, where software engineers work on production systems using a software engineering approach. It's explicit that Google is different, and they usually need to catch software bugs and errors in various non-conventional ways.

• Site Reliability Engineering for reliable operating systems and infrastructure scale.

• Define metrics that matter most to the business, such as typical values for those metrics, and plan a reaction if the value isn't met.

• Service level indicator, service level objectives, service level agreement

• RED: The acronym stands for Rate, Errors, and Duration. These are request-scoped, not resource-scoped, as the USE method is. Duration is explicitly taken to mean distributions, not averages.

• The increase means time to failure (MTTF), and the decrease means time to repair (MTTR)

Service level objective (SLO)

There is usually difficulty in using Service Level Agreement (SLA) and Service Level Objective (SLO). Certain SLOs (Service Level Objectives) are usually meant to define the precise, measurable targets that a service must meet to satisfy the expectations outlined in the SLA (Service Level Agreement). These objectives provide specific criteria for evaluating service performance, such as availability, throughput, repetition, response time, or quality. By establishing clear SLOs, organizations can effectively measure and manage service levels, ensuring that they adhere to agreed-upon standards and maintain a high level of service quality. The service demanded by the provider and the client varies depending on the service's needs, resources, and budget.

• NO SLO < Good SLO < Perfect SLO

• Pick an objective and iterate

• Capture a set of events and use a window and target percentage of 99.9% of good events in the last 30 days.

• A good SLO is barely user-happy.

• Determine an error budget that allows failure in the tradeoff to prevent progress and innovation.

What Are the Three Pillars of Observability?

Observability in software systems is typically defined by three main pillars: Logging, Metrics, and Tracing. Each plays a crucial role in understanding system behaviour, diagnosing issues, and ensuring performance.

1. Logging — The Event Record

Logs capture discrete events within a system: errors, state changes, warnings, and informational messages, each timestamped and contextualised.

Types:

• Plain logs — free-form text; easy to generate, harder to parse at scale.
• Structured logs — JSON or key-value formatted; machine-readable and query-friendly.

Best practices:

• Standardise log output using logging libraries.
• Aggregate centrally (e.g., Elasticsearch) for long-term retention.
• Disable debug mode in production — it inflates volume, increases cost, and generates false-positive alerts.
• Visualise with tools like Kibana for operational queries.

2. Metrics — The Performance Signal

Metrics are numerical, time-series representations of system behaviour — response times, error rates, CPU utilisation, request throughput.

• Why they matter: Metrics are aggregated over time to detect trends, set baselines, and trigger alerts. They are the primary input for SLO tracking and capacity planning.

• Implementation: Most runtime environments support metric collection natively. Push data to endpoints (e.g., Prometheus) for aggregation and alerting.
  

3. Tracing — The Request Journey

Distributed tracing captures the end-to-end path of a request as it travels across services, databases, and external APIs.

Key properties:

• Each trace carries a global correlation ID propagated across all service boundaries.
• Tools like Jaeger and Zipkin visualise the full request flow.
• OpenTelemetry provides a vendor-neutral standard for trace instrumentation across languages and platforms.

OpenTelemetry is now the second most active CNCF project after Kubernetes. As of 2024, 85% of surveyed organisations had adopted OpenTelemetry or OpenTelemetry-centric tooling as part of their observability strategy. (Grafana Labs Observability Survey, 2024)

Diagnostic value: Tracing pinpoints exactly where latency is introduced or where failures cascade, enabling precise root-cause analysis in multi-service architectures.

A non-functional type of testing that measures the performance of an application or software under a certain workload. Click to explore, Performance Testing Tools

How Does Observability Work in Practice?

Logging Process

Logging is a mechanism to collect logs from various input sources. Usually, logs are in raw format. To gain real insights, parse these logs and apply queries to gain insights quickly. Usually, logs are sent to an output tool that organises the logs. Logging defines what to log, how it should be logged, and how logs are shipped to an external system for aggregation.

Debug mode is disabled for better logging, as logging everything based on a debug level will become expensive to manage, creating extra false positives, not-so-important alarms, and more difficulty in getting important data. Debug should be the default only while troubleshooting, not in real production environments

Monitoring Process

Monitoring is an activity performed by DevOps guys. It’s simply observing the state of an infrastructure/environment over a period of time. Monitor for three reasons: 

• Detecting Problems— usually by alerts or looking at issues on dashboards

• Finding Resolution for Problem— finding root causes of issues and troubleshooting

• Continuous Improvement— reporting and documenting

Tracing Working

Trace the calls between various applications. Priorities are defined as different service failures, and the one with the highest priority is caught and alerted immediately. Tracing shows what either happened in the past or what is happening at present. Tracing is a very important piece of it proactively. It also suggests what code we can add to the service to provide better insights into the application. There should be transparent visibility end-to-end for all transactions happening in the environment.

How Alerting Works?

Alerting helps define how to notify the Ops guys when an event occurs. It is very important to remove false positives. There should be -

• Alert only to important events

• Self-healing infrastructure

• Enable analytics when something has been done manually many times

• Enable Automation to fix the problems

Monitoring of applications is the biggest aspect, As the speed and the accuracy expected is very high to address the issue on time. Click to explore about, Application Performance Management in DevOps