What is observability?
Observability refers to the ability to analyze and measure the internal states of systems based on their outputs and interactions across assets. The three pillars of observability—metrics, logs, and traces—are used to gain insights into system behavior, identify and resolve performance and security issues to improve system reliability, and provide visibility into complex systems to help identify bottlenecks and failures.
This article provides a deep dive into the importance of observability, the differences between observability and monitoring, how observability tools work, implementation challenges, the key criteria to consider when evaluating solutions and platforms, and how each solution and platform approaches observability.
The importance of observability
As an organization’s IT infrastructure becomes more dynamic and complex, the organization’s teams face increasing pressure to manage distributed systems, data, and users effectively and efficiently across multiple environments, while also facing greater security risks and increasing compliance demands. The accelerated data and application growth, associated rising costs, and the need to innovate in order to stay competitive make it even more complex to run the business in the most optimal way across all teams. Observability provides visibility and actionable insights into IT performance, security, delivery, reliability, and costs to help organizations address this increasing complexity.
What are the differences between observability and monitoring?
Observability and monitoring are related but distinct concepts. Think of monitoring solutions as similar to the pulse or heartbeat monitoring on a smartwatch, whereas observability is closer in concept to a comprehensive medical exam that includes blood tests, diagnostic imaging, and a full review of a patient’s medical history.
Monitoring focuses on tracking the health and performance of specific parts of the technology stack, and it is reactive in purpose. For example, monitoring could involve an alert being triggered under certain conditions, prompting DevOps teams to react to that alert.
Observability is about providing context to the state of every part of the business including performance regressions, outages, or issues concerning security and compliance. This context doesn’t only help teams realize that there is a problem or where it is coming from but also informs them about the reasons why something is happening and how to remedy that issue. Observability is, in a sense, a broader version of monitoring that allows operations to be proactive and resolve sophisticated issues faster.
| Monitoring | Observability | |
|---|---|---|
| Core question | Is the system working? | Why is the system behaving this way? |
| Assumes | You know in advance what can go wrong | You don’t, and new failure modes will appear |
| Built on | Predefined dashboards and thresholds | Correlated, high-cardinality telemetry you can query freely |
| Typical output | An alert that a threshold was crossed | An answer to a question nobody thought to ask in advance |
| Handles novel failures | Poorly | This is the point |
| Relationship | A component of observability | Includes monitoring, plus the ability to investigate |
What are the three pillars of observability?
Metrics, logs, and traces are the three fundamental pillars of observability, each providing unique insights into the state and performance of a system. The three pillars are described as:
Metrics: Metrics provide a broad view of system health, consisting of quantitative data that measures various aspects of system performance and resource utilization, allowing for trend analysis and forecasting. Examples include CPU usage, memory consumption, request rates, and error rates.
Logs: Logs are detailed chronological records of specific events that occur within a system. Logs offer a granular view of what happened within the system, helping in debugging and understanding specific events. Examples include error messages, transaction records, and system events.
Traces: Traces are records that track the flow of a request from the frontend to the backend through various components of a system. Traces help in understanding the path and performance of requests, identifying bottlenecks, and diagnosing latency issues. For example, a trace might show how a user request travels through different microservices.
Beyond the three pillars
Metrics, logs, and traces remain the standard framing, and they cover most of what teams collect. But the model has limits in practice, and most mature observability implementations now include signals that don’t fit neatly into any of the three:
Profiles show where code spends CPU time and allocates memory, at line-level resolution. A trace can identify which service is slow; a profile identifies which function.
Real user monitoring and session data capture what an individual user actually experienced, including client-side errors and interaction timing that never reach the backend.
Events and change tracking record deployments, configuration changes, and feature-flag flips, which is often the fastest route to a root cause when something breaks.
The more useful question than how many pillars there are is whether signals are correlated. Three telemetry types in three separate tools produce less understanding than two that share tags and can be pivoted between.
What are the benefits of observability?
An observability solution provides a holistic view of a system or of an entire architecture. Organizations employing observability techniques achieve several benefits:
Accelerated digital transformation: The complexity of platforms running in the cloud or in hybrid architectures can lead to a greater risk of failure or downtime, expose a larger attack surface to security threats, and hinder efforts to maintain compliance and accountability. For organizations facing greater demands for performance and efficiency while maximizing budgets and capacity, observability enables them to modernize applications, accelerate digital transformation, and deliver more reliable software faster.
Effective operational scalability and cost reduction: With observability, teams can automate routine tasks and optimize resource usage, leading to more efficient operations and cost savings. Observability platforms can help DevOps, security, and business teams consolidate disparate tools and telemetry types, which helps improve incident resolution times, resource and cost efficiency, and capacity planning.
Enhanced customer experience: Observability aids in identifying and resolving performance bottlenecks, delivering a seamless user experience, and driving higher customer conversion rates. Observability can help improve end-to-end application performance across the entire customer journey—from interactions with visual components to database queries—achieving greater conversion, retention, and loyalty rates. Through shared visibility across frontend applications and backend services, engineering teams can provide a superior user experience, in addition to effective resource scaling.
Reduced operational, security, and compliance risks: Infrastructure, DevOps, Security and other technical teams seek greater uptime and performance, better workload and application security, and compliance results with fewer and shorter operational and security incidents. Acting as a single source of truth, observability platforms enable better collaboration and decision-making among different teams. Unified observability also helps organizations improve their security posture by detecting risks and threats across their infrastructures and applications throughout the software development lifecycle, allowing the organizations to adopt a DevSecOps culture.
Types of observability
As systems have specialized, so has the practice. These are related disciplines rather than separate ones — each applies the same principle of inferring internal state from telemetry to a different layer of the stack.
Application observability covers request-level behavior in services and APIs, typically through distributed tracing and APM.
Infrastructure observability covers the hosts, containers, and cloud resources underneath, through metrics and logs.
Frontend observability covers what users actually experience, through real user monitoring and session data.
AI observability covers models, prompts, retrieval, and tool calls in production AI systems, where failures often appear as low-quality output rather than as errors.
LLM observability narrows to the model layer specifically: token usage, latency, output quality, and hallucination rates.
AI agent observability covers multi-step agentic workflows, where a single user request may fan out across many tool calls and sub-agents.
Data observability covers the freshness, volume, and quality of data moving through pipelines and warehouses, which downstream analytics and AI depend on.
Pipeline observability covers CI/CD systems themselves: build durations, test failures, and flaky tests.
Security observability applies the same telemetry to threat detection, vulnerability context, and compliance evidence.
How do observability platforms work?
An observability platform has to work with the organization’s infrastructure, applications, and overall technology stack to be effective. Most platforms incorporate the following components, processes, and tools to deliver information.
Agents (Collectors)
Agents are a software component that collects and routes telemetry data from systems, applications, or processes. The data is refined, standardized, enriched, tagged, and then exported to an observability platform. A single agent that is able to collect, process, and route multiple telemetry types can provide consistent data collection across technology stacks and enhance correlation and troubleshooting. Agents should have low overhead (consuming few CPU and memory resources to avoid impacting system performance), they should be secure by design, and they should be easy to deploy and manage at any scale, via configuration files or remotely. Some organizations could consider a platform-neutral collector, like in the case of OpenTelemetry.
Instrumentation libraries and software development kits (SDK)
An observability platform should incorporate an instrumentation library or a software development kit (SDK) to assist IT operations and development teams in generating telemetry from frontend applications, backend services, CI/CD pipelines, streaming data pipelines, and more. An SDK implements APIs and provides definitions, functions, and sampling mechanisms to enable code instrumentation. Some organizations could consider platform-neutral components, like in the case of OpenTelemetry.
Backend observability platform
An observability platform is required to handle and make sense of ever-increasing amounts of data, regardless of its source or how it is being generated. This data must be transformed, cataloged, analyzed, and stored for IT teams to query, understand, and take action on. The platform must adapt to meet an organization’s needs, such as increasing available data storage and resource allocation to handle varying telemetry volumes.
A selected observability platform should be capable of scaling its reach through an organization’s growing infrastructure and applications, and adapting to new technologies and requirements. In this regard, a software-as-a-service (SaaS) platform has an advantage over open-source solutions (OSS) that are installed and maintained by an organization’s engineers. A SaaS-based platform is managed by the service provider, where software testing and upgrades, enhancements, fixes, resource provisioning, and security patches are routinely and remotely performed, which frees up developers’ time and reduces both infrastructure and full-time employee (FTE) costs.
User interface (UI)
The user interface (UI) for an observability platform is key to understanding the volumes of telemetry data retrieved through the platform, ideally without learning scripting or querying languages. The interface should enable easy query and analysis capabilities that allow users to quickly find the answers they are looking for, such as the source of high error rates or bottlenecks. Dynamic reporting can aid reviewing for security risks and threats, and it can provide evidence for compliance requirements. Visualizations can help map user journeys and behavior patterns to problems further down the tech stack. The UI’s features can help users set actionable alerts using thresholds or machine learning capabilities to proactively monitor issues. Charting tools are essential for supporting different data types and provide, for example, time-series charts, report lists, trace flame graphs, heatmaps, pie charts, and other visualizations.
Use cases for observability platforms
IT operations, DevOps, security, and business teams use observability tools to fulfill some of the functions and responsibilities described in the following sections.
Monitor and optimize system performance, security, and cost
An observability solution grants visibility across an organization’s infrastructure, application stack, and software development lifecycle, giving teams critical information to track, analyze, and proactively address issues before they impact end users. By organizing, correlating, and reviewing data obtained from telemetry, teams can make informed decisions and take collaborative actions based on a shared source of truth.
Detect and resolve incidents
Observability solutions are capable of more than monitoring, which typically involves setting alert thresholds or usage conditions, often with the help of machine learning capabilities. Observability provides context by examining multiple data points through a wide variety of lenses: performance, security, user behavior, costs, and more. Teams can then move and respond faster through issue identification, localization, root-cause detection, impact analysis, and remediation.
Enhance developer experience
Development teams that possess a holistic picture of their organizations’ infrastructure and applications, in addition to a better understanding of how their system components work together, can efficiently complete their tasks and build better code, CI/CD pipelines and tests, and production applications. With improved system performance, developers can spend less time troubleshooting, debugging, waiting for tests to complete, or searching for owners and resources, and focus on feature development, innovation, and optimization.
Improve security posture
Observability helps security and DevOps teams map out their organizations’ infrastructure, identify user groups and access permissions, and map application services and dependencies. Using the three pillars of observability, DevOps and security teams can detect and respond to vulnerabilities and other types of risk—as well as attacks—at the code, infrastructure, and application levels. Observability also aids in compliance by providing audit trails, sensitive data scanning, reporting, and evidence to help maintain an organization’s regulatory requirements and industry certifications.
Automation
The data collected, transformed, and stored by an observability platform provides organizations with actionable steps needed to improve operations, resolve issues faster, and deliver increased uptime. Through observability, manual processes can be replaced with automated workflows. Teams can apply necessary system changes at a faster pace, such as scaling infrastructure when needed or rolling back deployments that fail to meet release standards before they hinder operations.
Industry shifts affecting observability
Observability practice has changed faster than most operational disciplines over the past two years, driven less by new tooling than by changes in what teams are actually running.
AI systems in production
Applications that call language models behave differently from the request/response services observability tooling was built around. A single user request may fan out across model calls, retrieval steps, and tool invocations, with latency dominated by inference rather than by database or network time. More importantly, these systems fail differently: a request can return HTTP 200, complete in normal time, and still be wrong. Error rates and latency percentiles don’t detect that.
This has pushed teams to instrument things that traditional APM never tracked. LLM observability covers token consumption, prompt and response content, retrieval relevance, tool call success, and output quality scored by evaluation rather than by status code.
Agentic workloads
Agents that plan, call tools, and delegate to other agents are now real production traffic rather than prototypes, and they strain the assumptions built into request-scoped tracing. A single agent run can produce dozens of nested operations across several services and external APIs, with a depth and branching factor that vary per execution. The unit worth observing is the whole run, not the individual call.
OpenTelemetry has begun standardizing this. The GenAI semantic conventions now model an agent execution as a span tree, with defined operations for creating an agent, invoking it, planning, and executing tools, and with Model Context Protocol calls sharing the same trace vocabulary as the agent that issues them. The conventions are still marked as in development rather than stable, so attribute names continue to move, but the shape of the model is settling. AI agent observability addresses this layer specifically.
AI-assisted investigation
The same technology is changing how teams use observability data. Rather than a responder manually correlating a spike to a deployment to a log pattern, platforms increasingly surface a proposed explanation and a summary of what changed, with the supporting telemetry attached. This compresses the investigation phase of an incident, which is typically the longest phase and the largest contributor to MTTR.
Worth being clear about the limit: suggestion quality depends entirely on the breadth and consistency of the underlying telemetry. A platform with partial instrumentation produces confident and wrong explanations, which is worse than no suggestion at all.
Telemetry volume as an architectural constraint
Data volume has grown faster than the budgets allocated to it, which has moved decisions about what to collect from a billing conversation into a design one. Sampling strategy, ingest filtering, and where telemetry is aggregated are now made at architecture time rather than negotiated after an invoice.
The practical effect is a shift toward controlling telemetry in transit rather than at rest. Tail-based sampling retains error traces while discarding most successful ones. Filter rules drop health check spans before they leave the cluster. Attribute processing redacts sensitive fields in the pipeline. The goal is a deliberate decision about what data is worth keeping, enforced centrally instead of service by service.
Platform engineering
As organizations consolidate infrastructure tooling behind internal platform teams, observability increasingly gets configured once and inherited by every service, rather than adopted per team. That raises the stakes on defaults: instrumentation coverage, tagging conventions, and retention policy set by a platform team become the ceiling on what every downstream team can investigate.
Cloud migration and hybrid infrastructure
Cloud migration and hybrid infrastructure are leading to faster business growth but also significant increases in complexity and security risk. As organizations move from on-premises infrastructure to cloud-hosted virtual machines, containers, serverless functions, and databases, IT teams face a growing challenge to collect, transform, and analyze metrics, logs, and traces from these distributed systems in a unified manner.
DevSecOps
As the complexity and scale of IT infrastructure and applications grow, coupled with greater demands placed on performance and speed, a new paradigm of shared responsibility between security and engineering teams has emerged. To address growing risks, security needs to be integrated throughout the software development lifecycle, from development to production, in a single pane of glass for all teams.
OpenTelemetry and observability standards
OpenTelemetry is the open standard for generating and collecting telemetry. It provides vendor-neutral APIs, SDKs, and a collector, so applications can be instrumented once and have that data sent to any compatible backend.
The project graduated from the Cloud Native Computing Foundation in May 2026, placing it in the foundation’s highest maturity tier alongside Kubernetes and Prometheus. Practically, that means instrumentation written against OpenTelemetry today is a durable investment rather than a bet.
What OpenTelemetry provides
A common data model. Traces, metrics, and logs share the same context, so a metric anomaly can be pivoted to the traces behind it and then to the logs that explain them, without correlating by timestamp and hope.
Vendor-neutral instrumentation. Instrumentation lives in the application rather than in a proprietary agent, which means changing or adding a backend doesn’t mean re-instrumenting.
The Collector as a control point. The collector sits between applications and backends and is where processing happens: sampling, filtering, redaction, enrichment, and routing. This is the mechanism most teams now use to manage telemetry cost and handle sensitive data, because it applies centrally rather than in every service.
Semantic conventions. Standardized attribute names mean telemetry from different languages, frameworks, and teams lines up in the same queries. This is the least visible part of the standard and often the most valuable, since inconsistent tagging is what makes cross-service investigation fail.
OpenTelemetry signal maturity
Maturity varies by signal and by language, and it’s worth checking the project’s own status page before committing.
| Signal | Status | Notes |
|---|---|---|
| Traces | Stable | Stable across essentially all supported languages |
| Metrics | Stable | Stable in the major languages |
| Logs | Stable, varying SDK maturity | Last of the three to standardize; SDK maturity still varies by language |
| Profiles | Public alpha | Added as a fourth signal, alpha since March 2026, with support in Collector v0.148.0 and later |
| GenAI conventions | In development | Moved to a dedicated repository in mid-2026 to version independently; no stable release |
The Profiling special interest group advises against relying on profiles for critical production workloads, so 2026 is best treated as an evaluation window rather than an adoption one. Similarly, because the GenAI conventions are still in development, gen_ai.* attribute names continue to change between releases.
Client-side and browser telemetry remain less mature than the server-side story, so teams needing real user monitoring generally still rely on purpose-built instrumentation there.
OpenTelemetry and observability platforms
A standard for collecting telemetry isn’t a substitute for a platform that stores, queries, and correlates it. OpenTelemetry defines how data is produced and moved; deciding what to keep, making it queryable at high cardinality, and building alerting and investigation on top of it remains the platform’s job. Most teams pair the two: standardized instrumentation feeding a backend they didn’t have to instrument twice for.
What are the challenges associated with observability solutions?
It can be challenging to introduce an observability solution. Different teams have differing goals, and incorporating a solution for use throughout an organization can introduce complexity, additional overhead, and costs. Some additional challenges for implementing an observability solution include the following:
Complex technology stacks
Modern applications rely on various technologies like containers, microservices, and programming languages, in addition to serverless functions, databases, CI/CD pipelines, and more. This complexity makes it difficult to get a comprehensive view of a system’s entire technology stack. Different parts might also be owned by different teams or monitored by different observability tools, creating fragmented views and visibility.
Manual instrumentation of unsupported languages or frameworks
An observability solution might not support specific or legacy languages, frameworks, or entire systems. This increased complexity and maintenance can require manual instrumentation to add those components to the solution. If the components cannot be added, this could introduce performance, data, and security risks.
Controlling data volumes and costs
The sheer amount of data generated by these systems can be overwhelming. Managing and analyzing this data in real time requires robust tools and infrastructure, and doing so comes with an expectation for increases in cost. Organizations need controls, in their own environments and in their observability solutions, that manage data ingestion and retention in a way that retains business-critical data while remaining within their budgets.
Laborious analysis
Raw telemetry gathered from an observability solution does not provide immediate value, and it can be difficult to extract insights. Analyzing observability data can require custom queries and detailed manual analysis, all of which could hinder value realization and organization-wide adoption of observability. Additionally, manual correlations between data types can take time to process and can prevent teams from fully understanding system issues and root causes.
Alert storms and fatigue
As a byproduct of a solution that produces large volumes of data, excessive alert messages can lead teams to focus on duplicate, non-critical, or false signals, sometimes ignoring or missing critical disruptions. An observability solution should provide an effective alerting strategy that accounts for baseline system behavior and the fine-tuning of alert thresholds or reporting of problematic conditions, with and without machine learning capabilities. Alerts must be specific and actionable so that they are routed to the appropriate person with clear description and instructions.
Features to look for in observability solutions
When evaluating observability solutions, consider the following:
Unified platform with end-to-end visibility
Organizations gain the most value from a single solution that provides end to-end visibility across their entire stack and across the software development lifecycle. Such a solution breaks down silos between teams, enabling them to rapidly troubleshoot and act upon performance and security issues based on a single source of truth. A solution that automatically applies common tags to all telemetry types can accelerate troubleshooting and remediation by giving teams the capability to query, analyze, and correlate all their data.
Breadth and depth of coverage
As systems and applications become more complex, more sophisticated problems could stem from anywhere in the stack. It is critical for teams across the organization to have access to more context. In addition to metrics, traces, and logs, other types of information from user sessions, security signals, code profiles, database queries, network data, queues, cloud provider bills, and more are crucial for understanding where issues are coming from, why they are happening, and who’s impacted by them. The granularity of this data (based on collection intervals and code-level performance) also plays an important role in reducing investigation and resolution times.
Ease of use
Ease of navigation, search, analysis, correlations, and dashboard creation without the need to learn a custom query language is a rudimentary element of an effective observability platform. A solution that provides a detailed analysis is especially important when teams investigate customer-impacting problems such as application slowdowns, resource contention, and slow database performance. The solution’s user experience and related components should work in concert.
Integrated AIOps
AI for IT operations (AIOps) that is embedded into observability solutions uses machine learning algorithms to automatically detect anomalies, surface outliers, and find the root cause and blast radius of incidents, helping teams reduce mean time to detection (MTTD) and mean time to resolution (MTTR). Features of AIOps integrated with observability solutions might include:
- Automated monitoring: Continuously monitor IT systems to proactively detect anomalies and forecast potential issues.
- Automated root cause analysis: Automatically identify causal relationships between symptoms across applications and infrastructure and pinpoint the problem.
- Predictive analytics: Use historical data to predict and prevent future problems.
- Event correlation: Analyze data from various sources to identify patterns and help determine the root causes of issues.
- Natural language querying: Search and analyze observability data using natural language, without requiring any specific syntax.
- Cost efficiency: Reduce operational costs by automating repetitive tasks and improving resource allocation.
High data resolution
The resolution or interval in which telemetry data is collected and stored can directly impact the level of insights derived from such data over time. For example, during a 60-second time range, collecting a metric every 15 seconds will result in four data points, as opposed to one data point collected every 60 seconds. Data resolution can impact the ability to catch performance spikes, and it can subsequently affect the accuracy of AIOps features. Data should be retained at a consistent resolution for as long as the data is needed.
Automation
Automated actions can help improve system uptime and performance by enabling teams to resolve issues faster and with confidence. For example, automated workflows can help teams roll back faulty deployments, block suspicious IP addresses, or scale cloud resources when demand is high. All of these features, when integrated in an observability platform, help teams adhere to service-level objectives and improve developer productivity.
Conclusion
Observability provides insights into system performance and security, helps identify and resolve issues to improve customer experience, and increases visibility in complex distributed systems. Learn more about Datadog’s approach and solutions to enable observability within your organization:
