What is SRE observability?

It uses software instrumentation to collect and analyze data across the computing environment (including infrastructure and applications), enabling IT teams to better understand, maintain and improve their architecture and site reliability over time.

SRE observability goes beyond standard systems monitoring, which serves as a vital component of any observability strategy but can’t provide the comprehensive visibility needed to optimize modern computing networks.

Traditional monitoring tools can, for instance, provide dashboards to visualize system state and alert IT personnel of malfunctions. However, today’s cloud-native computing environments are increasingly distributed, relying on a range of microservices, edge servers, Docker containers and serverless functions.

These networks are highly dynamic and require limited human intervention to manage network services, so traditional monitoring systems often prove insufficient even for straightforward monitoring tasks.

The goal of observability is to equip site reliability engineers with the actionable data they need to maintain secure, scalable, high-availability sites and services. When systems are observable, engineers can easily view internal activities and better troubleshoot issues and vulnerabilities that can negatively affect site reliability. SRE observability also helps engineers optimize overall network performance and implement continuous improvement practices across network services.

SRE is a software engineering practice that combines DevOps and traditional IT operations (ITOps) to solve customer problems, automate ITOps tasks, accelerate software delivery and minimize IT risk. It focuses on achieving resiliency by consistently automating key processes.

Traditionally, SRE comprises manual IT operations and system administration processes, such as log analysis, performance tuning, patching, production environment testing, incident management and postmortem evaluation. However, modern SRE automates these tasks to save time, reduce human error and streamline collaboration between development and operations teams.

SRE tools automatically search for system deficiencies using a process called chaos engineering, wherein site reliability engineers intentionally cause failures in production and preproduction environments. This process helps teams understand how failures can impact software systems and develop strategies for mitigating failures in the future.

SRE also prioritizes capacity planning, a process that determines the resource requirements for essential business functions, scales those business functions and enables developers to create new applications and features. By using established key performance indicators (KPIs), SRE teams can evaluate the delivery of updates and the implementation of new features.

Observability plays an integral role in maintaining the availability, performance and security of modern software systems and cloud computing environments.

The term “observability” comes from control theory, an engineering theory concerned with automating the control of dynamic systems (regulating the water flow through a pipe based on feedback from a flow control system, for instance).

Observability provides deep visibility into modern, distributed tech stacks for automated, real-time problem identification and resolution. The more observable a system, the more quickly and accurately IT teams can determine the root cause of performance issues, often without extra testing or coding.

Building and maintaining observable systems require software tools capable of aggregating, correlating and analyzing steady streams of performance data from apps and the hardware and networks they run on. IT teams can then use the data to monitor, troubleshoot and debug every network component, helping businesses optimize customer experience and meet service level agreements (SLAs).

Observability is often confused with application performance monitoring (APM) and network performance management (NPM). However, observability tools represent a natural evolution of APM and NPM data collection methods, 1 better suited for distributed networks and cloud-native application deployments.

Achieving observability requires organizations to collect telemetry data, including:

Metrics are raw, derived or aggregated quantitative measurements that speak to system health and performance (of a server or an API, for instance) over specific intervals of time. They help organizations build a solid foundation for SRE monitoring and data analysis practices so engineers can identify data patterns and predict systems issues.

Common metrics in SRE include CPU usage, memory consumption, request latency, error rates and network bandwidth, each of which provides a snapshot of the system's state and helps teams resolve potential issues before they escalate.

Logs are detailed, timestamped textual records of events, typically recorded in plain text, binary or structured formats. They often provide a starting point for engineers seeking to understand and diagnose system issues.

Logging functions within SRE observability tools collect, store, analyze and correlate a range of data (including error messages, startup and shutdown processes and configuration changes). They enable SRE teams to understand events chronologically and contextually, making it easier for them to trace the root cause of issues and deploy resolution workflows.

Traces, such as HTTP requests and database queries, provide a comprehensive view of a data request's lifecycle from initiation to completion. They represent the journey of a request through a computing network, capturing the interactions (dependencies, for instance) between different components and services.

Tracing—namely distributed tracing—is valuable in microservices architectures, where requests might traverse multiple services before reaching their destination.

SRE observability tools automatically send out notifications when issues arise so that engineers can resolve them promptly and minimize downtime for end users.

SRE observability solutions help businesses collect and process performance telemetry in near real-time, offering SRE teams data-driven insights on system errors and why they occur. These insights enable organizations to reduce the cognitive load on engineers during site development and maintenance so smaller, cross-functional, autonomous teams can manage services more efficiently.

The integration of artificial intelligence (AI) and machine learning (ML) with SRE observability solutions is rapidly changing how businesses approach site reliability engineering. AIOps approaches enable SRE teams to incorporate advanced tools and algorithms into observability practices, analyzing datasets from observability tools to identify patterns, predict outages and recommend solutions.

Instead of focusing solely on manual tasks and scripting, SREs can become trainers and strategists for AI systems, teaching AI to recognize patterns, filter out noise and avoid costly errors. This shift will elevate the SRE function from a task-oriented role to a strategic discipline centered on managing intelligent automation systems.

For example, SRE observability tools can use AI technologies to emulate and automate human decision-making in the remediation process. AI-based observability functions can continuously monitor and analyze incoming data to find activities that surpass established thresholds and perform a series of corrective actions (such as remediation scripts) to address the issue.

If—and only if—the software can’t solve the problem, it will automatically generate a detailed support ticket in the SRE team’s issue management platform so that SRE staff will only deal with problems the observability platform can’t handle.

AI-driven observability tools can also use the advanced text processing capabilities of large language models (LLMs) to simplify data insights in SRE observability platforms. LLMs excel at recognizing patterns in vast quantities of repetitive textual data, which closely resemble telemetry data in complex, distributed systems. Today’s LLMs can be trained—or driven by prompt engineering protocols—to return information and insights using human language syntax and semantics.

Advanced LLMs help SRE teams write and explore queries in natural language, moving away from complex query languages and enabling IT staff at every skill level to manage complex data more effectively.

Furthermore, SRE observability tools benefit from causal AI functions, which clarify and model causal relationships between variables as opposed to merely identifying correlations. Traditional AI techniques (ML, for instance) often rely on statistical correlation to make predictions. Causal AI instead aims to find the underlying mechanisms that produce correlations, improving the predictive power of SRE observability tools and enabling more targeted decision-making.

Causal AI can help SRE teams analyze the relationships and interdependencies between sites and network components. These features boost site reliability by clarifying not just the “when and where” of system issues but also the “why.”

SRE observability often requires the use of advanced observability tools, which enable:

With observability tools, SRE teams can use metrics, logging and distributed tracing capabilities to detect and rectify system issues before they impact users. Observability solutions monitor and aggregate data from across the network, providing clear visibility into system behavior and helping engineers quickly conduct root cause analyses. They encourage proactive, enterprise-wide SRE practices and help businesses maximize network availability. 

Observability solutions that use aggregated, contextualized data help SRE teams and on-call engineers quickly initiate troubleshooting processes and glean insights about a system state when an incident is detected. These solutions enable rapid diagnosis and resolution and help businesses maintain site reliability and compliance with SLAs.

Data-driven decision-making is a cornerstone of SRE. Observability platforms provide teams with all the information they need to make informed decisions about system architecture, capacity planning and operational strategies, ensuring that changes are based on empirical evidence. Telemetry data also enables teams to continuously tune system performance to maximize reliability.

SRE initiatives are inextricable from broader business goals, as user satisfaction plays a key role in creating and maintaining system reliability. SRE observability solutions provide tools to gauge user satisfaction by helping businesses establish service level objectives (SLOs).

SLOs provide actionable insights about user experiences, unlike indirect metrics, such as CPU and memory usage. Typically, observability tools can be tailored to specifically assess user satisfaction (identifying the issues that users face during product purchases, for instance). SLO-based strategies drive data-driven discussions, helping businesses understand when to focus on reliability and when to pursue new features.

SRE observability helps organizations optimize site reliability and uptime for a range of use cases across business sectors, including:

For e-commerce platforms, SRE observability helps create seamless user experiences and transaction reliability. Teams can monitor website performance, transaction processing and user engagement metrics in real-time. They can also use observability tools to identify slowdowns or disruptions, helping retailers prevent cart abandonment and helping site engineers optimize server loads and scale resources during peak shopping seasons.

SRE observability enables businesses to monitor package delivery times, shipment volumes and inventory levels, facilitating quick anomaly detection for issues such as shipment delays and low inventory. SRE observability tools can also track service level indicators (SLIs)—quantitative measurements of the system behaviors associated with different services—such as delivery success rates.

SRE observability enables financial institutions to monitor vital transactions such as wire transfers, ATM withdrawals and online payments. SRE tools also help banks automatically scale their sites and systems to meet the growing demand for digital financial services.

SRE observability enables healthcare providers to monitor and analyze patient data in real-time. For instance, a hospital's SRE team can implement a system to track vital signs so doctors and nurses can quickly intervene in the case of a medical emergency. Observability tools can also monitor the hospital's infrastructure, identifying performance issues that might prevent staff from delivering the highest-quality patient care.