Observability has become a critical component of modern platform engineering, allowing teams to diagnose issues, optimize performance and maintain system reliability.
Unlike traditional monitoring, which alerts teams when something breaks, observability provides deeper insights into why failures occur and how systems behave under various conditions.
Anil Lingutla, director of CloudOps at TEKsystems, said large-scale platforms must focus on the right telemetry data to ensure effective observability.
“Key metrics to prioritize include CPU and memory usage, request rates, error rates and latency,” he said.
System metrics and Golden Metrics serve as fundamental indicators for monitoring large-scale applications, helping teams detect performance bottlenecks early.
Logs play a crucial role as a granular record of events, capturing errors, warnings and critical state changes.
“Implementing structured logging enhances indexing and searchability, making it easier to analyze system behavior,” Lingutla said.
Traces provide insight into request flows across microservices, with distributed tracing enabling faster root cause analysis during production outages.
“However, organizations must adopt appropriate tracing techniques to avoid introducing latency,” he said.
By strategically leveraging these telemetry components, teams can proactively identify issues, optimize performance and ensure reliability in complex distributed systems.
Lingutla explained platform engineers can ensure observability tools provide actionable insights by effectively aggregating data and correlating logs, metrics and traces to offer a clear, contextual view of system performance.
“Implementing intelligent alerting with anomaly detection and dynamic thresholds helps reduce noise and ensures teams are only notified of significant issues,” he said.
Additionally, designing meaningful dashboards that highlight system health trends—rather than displaying excessive raw data—enables teams to focus on key performance indicators and quickly identify potential problems.
“By prioritizing relevance and clarity in observability strategies, engineers can prevent data overload and drive more efficient incident response and decision-making,” he said.
Prioritizing the Right Metrics Without Creating Noise
However, collecting large amounts of data can introduce unnecessary complexity, making it difficult for teams to extract meaningful insights.
“It’s all too easy to measure everything and report as-is with the assumption that the receiver will be able to make sense of it,” said Jamie Boote, associate principal, software security consultant at Black Duck.
Instead of flooding teams with raw logs, engineers should define clear thresholds that trigger action and align observability with operational decision-making.
“By only reporting what needs to be addressed, engineers can ensure their observability platforms aren’t reduced to noise generators,” he added.
To avoid unnecessary complexity, intelligent sampling, adaptive logging and AI-powered alerting can help teams focus on actionable insights.
“Today’s observability tools offer automated issue detection with AI support,” said Boris Cipot, senior security engineer at Black Duck.
Solutions including Grafana Cloud, Dynatrace and New Relic use machine learning models to reduce alert fatigue and surface high-priority incidents without requiring teams to sift through massive amounts of data manually.
Observability’s Growing Role in Security
Observability also plays a growing role in security monitoring, helping teams detect potential breaches and vulnerabilities before they escalate.
“Secure operation is an aspect of normal operations,” Boote said. “Deviations from a normal baseline may have many causes, some of which could have a security cause.”
Integrating operational and security data, can help engineers evaluate security incidents within the broader context of system behavior and avoid misdiagnosing threats.
Connecting observability with security monitoring enables faster incident detection and forensic analysis.
“Observability and security monitoring intersect by providing real-time insights into system behavior,” said Kausik Chaudhuri, CIO at Lemongrass.
Best practices include centralizing logs with structured formats, using distributed tracing to track suspicious service interactions and setting up real-time alerts for anomalies like unauthorized access or unusual traffic patterns.
Distributed tracing has become especially critical in microservices architectures, where applications are broken down into smaller, interconnected components.
However, implementing tracing at scale presents challenges, including instrumentation complexity, performance overhead and storage costs.
“Instrumenting services consistently across different programming languages and frameworks requires standardized tracing libraries like OpenTelemetry,” Chaudhuri explained.
Sampling strategies are also essential to balance trace-level visibility with system efficiency and prevent excessive resource consumption.
Another challenge in observability is ensuring it’s built into the platform from the beginning rather than an afterthought.
“Observability should not be a concept that one thinks about at the end of platform development,” Cipot said.
Instead, it must be integrated into the architecture from day one to ensure it scales effectively and aligns with infrastructure needs.
Selecting observability tools with strong integration capabilities and scalability helps organizations avoid retrofitting costly solutions later.
Leveraging Automation for Scalability, Future Growth
Boote noted automation is playing an increasingly important role in maintaining observability across large-scale systems.
“Automated systems continuously monitor user sessions, picking up on abnormal activities and validating requests in real time,” Boote said, noting AI-driven automation ensures that security policies and observability data remain consistent even as platforms scale.
“As platforms scale up during peak times, leveraging automation ensures observability policies are applied in real time,” Chaudhuri added.
If a Kubernetes cluster scales up with new nodes, automation ensures that each new node inherits security and monitoring policies without manual intervention.
This minimizes configuration drift and reduces operational risk.
Emerging trends in observability focus on automation, AI-driven insights and deeper integration across distributed systems.
“AI/ML-powered anomaly detection is becoming essential for identifying patterns and predicting failures before they impact users,” Chaudhuri said.
OpenTelemetry has also gained traction as the standard for unified instrumentation, helping organizations correlate logs, metrics and traces more effectively.
Context-aware observability, which integrates business logic into monitoring, is another promising development.
“By prioritizing issues based on actual user impact rather than just system metrics, teams can focus on fixing what matters most,” he said.