What is cloud-native observability?

Cloud-native observability differs from traditional observability in its specific focus on the challenges posed by cloud systems. In these systems, containers, virtual machines and other resources can be provisioned and deleted at a moment’s notice, creating massive amounts of sometimes ephemeral data.

Cloud-native observability solutions help organizations track key datapoints in this mutable system, which in turn helps support the DevOps process and its small, frequent, often automated updates.

Cloud-native observability platforms collect data from across an organization’s hybrid cloud environment, which can consist of services from multiple providers (such as Microsoft Azure and Amazon Web Services), onsite servers and the many tools and resources they support (such as microservices or container orchestration tools like Kubernetes). They provide actionable insights into metrics such as network traffic and latency and correlations between those metrics across platforms, often automating necessary repairs and visualization of the data gathered.

For example, a cloud-based observability platform might collect latency metrics from a virtual machine hosted on a cloud server, logs from that virtual machine’s Kubernetes-orchestrated containers describing their API calls and information about network events such as the deployment of a new application. It can then present the data collected as a chart or graph and perform a root cause analysis, giving administrators concrete insight into what causes downtime.

Many modern platforms use artificial intelligence (AI) and machine learning (ML) to power these automated features. According to a 2025 report from 451 Research, 71% of organizations that use observability solutions are using their AI features, an increase from 2024 of 26%.¹

Many popular cloud-native observability tools are open source, such as OpenTelemetry, Jaeger and Prometheus. By allowing the developer community to make platform- or application-specific fixes as problems arise, open-source tools give organizations more flexibility in sometimes unpredictable cloud-native environments, and greater ability to connect their tools with various systems and application programming interfaces (APIs).

Cloud-native observability tools collect logs, traces and metrics from across the cloud ecosystem. They often present raw data, analysis and visualizations through a dashboard that helps users monitor application health and business objectives.

In a cloud environment comprised largely of microservices, new containers and virtual machines can disappear and appear at a moment’s notice, creating a vast amount of telemetry data. This creates a novel problem that cloud-native observability platforms must tackle: seeing everything in a network that’s constantly changing, and tracking data from sources that might no longer exist as the network expands and contracts automatically to meet business needs.

Observability tools facilitate the collection and aggregation of CPU memory data, app logs, availability information, average latency and other datapoints within these complex networks.

Cloud-native observability platforms rely on the three pillars of observability: logs, traces and metrics.

Visibility is a core function of cloud-native observability platforms. The ability to monitor the containers, virtual machines, servers and other elements of a microservices-based network is a critical feature for these architectures, in which distributed tracing and dependency maps can be  convoluted and nearly indecipherable.

Observability dashboards enable users to monitor application health measures such as availability and resource usage and relevant business objectives such as conversion rate or active users. Monitoring features also help clarify how services work with each other (by using tools such as dependency graphs) and how they fit into the overall architecture.

Traditional monitoring was done with application performance management (APM) tools, which would aggregate the data collected from each data source to create digestible reports, dashboards and visualizations—not unlike monitoring features in modern observability software.

In a modern cloud computing environment, observability tools often offload basic telemetry to the Kubernetes layer, where the container orchestration software uses native tools to perform observability within the platform. Allowing Kubernetes to automate this activity enables IT teams to focus data analysis on service-level objectives (SLOs) and service-level indicators (SLIs).

Automation in modern observability software goes beyond collection, monitoring and analysis. Observability tools can also automate debugging processes, instrumentation and monitoring dashboard updates as new services are added to the network. They can also manage agent handling, where agents are small software components deployed throughout an ecosystem to continuously gather telemetry data.

Practicing cloud-native observability can give organizations a more comprehensive view of complex systems, reduce mean time to repair (MTTR) and further integrate automation tools into the DevOps workflow.

In highly distributed systems, a vast number of overlapping servers and cloud-native applications emit signals, metrics, logs and traces, and they don’t always cleanly share data. Cloud-native observability tools help overcome these bottlenecks by collecting observability data from across the ecosystem, allowing administrators to troubleshoot in real time and make data-driven decisions.

Once administrators—or automated tools within the observability platform—have spotted correlations between problems in the cloud, they can perform a root cause analysis. For example, a platform might flag slow application response globally that coincides with high latency in a particular region, and then perform an analysis to identify the misconfigured or malfunctioning server responsible for the issue. 

This analysis can be the difference between triaging an incident for hours and resolving an impending issue before it happens, reducing downtime and freeing up DevOps teams for other tasks.

Artificial intelligence and machine learning tools are at the heart of many modern observability platforms, detecting anomalies without user intervention, performing root cause analysis and using generative AI for data visualization.

The sheer volume of telemetry data produced in a cloud environment makes AI and ML invaluable for cloud-based observability. Automating observability at scale can generate insights that allow organizations to automate other business functions, as well. Predictive analytics, for example, can enable a business to provision new server infrastructure in advance of heavy traffic.

Because it collects and synthesizes such a vast and diverse amount of data, cloud-native observability can pose challenges regarding scaling and complexity, the use of multiple observability tools and data privacy and compliance.

Organizations must balance visibility across a complex cloud environment with practical constraints around storage costs, query performance and data retention. Without proper sampling strategies and data prioritization, the volume of data collected can overwhelm observability platforms.

The sprawling, rapidly changing nature of containerized microservices can also mean that monitoring must extend beyond the application level to the clusters and nodes of an orchestration tool like Kubernetes.

Most organizations operate dozens of monitoring tools accumulated over years, each serving specific teams or technologies. The technology stack typically spans multiple programming languages, legacy systems, multicloud environments, microservices, infrastructure components and frameworks. This makes interoperability challenging and creates fragmented data, which defeats the fundamental goal of observability: creating a unified view of system health.

Cloud-native observability can create compliance challenges by aggregating sensitive data from across the enterprise into platforms. Telemetry data can contain personally identifiable information (PII), payment card details or protected health information. These types of data can fall under the authority of regulations such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and the California Consumer Privacy Act (CCPA).

Without data masking, tokenization, geographic restrictions and role-based access controls, organizations risk exposing sensitive data to unauthorized users or violating regulatory requirements. For example, resolving a transaction issue for a European customer can require accessing logs that contain personally identifiable information. If US-based employees view that data, that situation might open the door to GDPR violations.

Implementing cloud-native observability is a pillar of the shift toward AIOps, the application of AI capabilities to automate, streamline and optimize IT service management and operational workflows.

When organizations have greater visibility into data in the cloud, they can automate decisions about provisioning or troubleshooting even in the cloud’s often vast, sprawling and unpredictable environment. In short, observability enables AIOps by giving organizations greater confidence in their AI and ML tools’ decision-making.

Key AI functions in cloud-native observability include:

• anomaly detection, where algorithms can analyze data at scale to determine the system’s baseline performance and quickly identify deviations;
  
  
• root-cause analysis, which moves beyond correlation to identify actions that can be taken to directly correct an error;
  
  
• and predictive analytics, through which AI models can predict future workloads and scale the network up or down accordingly. 

While the two share important similarities, cloud-native observability is different from the practice of full-stack observability. Cloud-native observability can be considered an evolution of full-stack observability, adapting the same tools and techniques for a cloud-native environment.

Full-stack observability correlates telemetry across all layers of the technology stack. Full-stack observability platforms gather data from multiple systems in real time and use AI and ML to detect anomalies, predict failures and generate insights for administrators.

Cloud-native observability is an evolution of this, where data collection and analysis tools used for full-stack observability are developed specifically for cloud-native technologies, integrating seamlessly with complex, containerized microservices.

In short, while full-stack observability provides comprehensive telemetry data across an IT environment, cloud-native observability is focused specifically on often serverless cloud environments.