Why Observability is Critical for Modern Cloud‑Native Systems
In today’s cloud-native world, everything is in motion. Containers come and go in an instant, services communicate across clusters and even continents and workloads expand and contract in an instant in response to changing needs. This has resulted in a multitude of variables that are impossible for human minds to process and understand. Observability, the science of inferring the internal state of a system from the signals it emits, has become the foundation upon which to keep modern applications healthy, resilient and responsive.
While traditional monitoring is based on fixed dashboards to monitor a set of pre-defined metrics, observability is about understanding the reasons behind events, particularly the unknown ones that often occur in distributed systems.
From Monitoring to Observability: A Paradigm Shift
The old model of monitoring was looking for a fairly static, somewhat predictable world. We set up monitors to detect clear-cut events. There is nothing wrong with that. The problem is that it is not very good when we are dealing with a rapidly changing world of hundreds or thousands of microservices. We can’t predict everything that is going to fail.
It is more than logs, metrics and traces. Observability is about instrumenting everything: Apps, infrastructure, network and user experience, so we can try to understand what happened, even if we didn’t know what to expect. The three pillars are still relevant. The difference is that observability today gives us context.
We have metadata, topology, signals, user experience and details down to the code. This allows us to ask questions about what happened. The key is to use observability to tell a story about the whole system. This is how we, as IT leaders, make decisions that are data-driven, yet aligned to both our business objectives and our technical objectives, as described by SUSE.
Why Observability is Essential for Cloud‑Native Success
Cloud-native environments bring new complexity with containers, service meshes, dynamic scheduling and multi-cloud environments. The problem with monitoring is the unknown unknowns. That’s where observability comes in. ‘Observability is the ability to answer questions you didn’t think to ask’. Distributed tracing, for instance, can show the path of a user request through the system of microservices. ‘If you have instrumentation all the way from the infrastructure to the user experience, you can very quickly determine if the problem is an API gateway, a database call or a client-side script’. The business case is obvious.
If the checkout process is slow or unavailable, it impacts the bottom line. With observability, you can catch issues quickly to reduce mean time to detect (MTTD) and mean time to resolve (MTTR). ‘Observability is the key to DevSecOps and SRE. With observability, the ops team can use the same data the software development team uses to improve collaboration, the speed of software delivery and the security of the software. The other problem with cloud-native applications is the data deluge.
According to Gartner, “The cost of cloud-native applications is growing exponentially due to the volume of telemetry data.” Without proper data management, the amount of telemetry data gathered may exceed the amount of telemetry data the organization can use. ‘Observability requires smart sampling, smart filtering and smart retention. Telemetry pipelines are the way to route, transform and discard telemetry data.
Data Volume, Cost and Complexity
There is a tremendous amount of telemetry data coming out of a cloud-native system: Every microservice, container and orchestrator is sending its own signals. The hard part is controlling this flood of data and continuing to extract the signals that ensure system reliability. As SUSE points out, organizations must develop a strategy to retain data that meets compliance and operational requirements, and data flows that manage the flow of information.
But as we have a mix of virtual machines and cloud-native workloads, we also have a level of complexity to deal with. To really understand a system of record from a single perspective, IT managers must monitor both their virtual and cloud-native platforms using a common approach. Having a unified approach to observability practices across all platforms is a must to troubleshoot and resolve issues, regardless of where the workloads are actually running.
AI‑Driven Observability and Predictive Operations
As the system increases in size and complexity, the need for automation increases as well. Observability stands out as the essential component for AIOps, or the use of AI for IT operations. This involves the use of AI in conjunction with ML algorithms that can identify issues and even automatically fix them.
The Cloud Native Computing Foundation has listed the latest trends in observability in the cloud native world as including the incorporation of AI and predictive analytics for increased reliability and reduced system downtime. This means that the AI can analyze the patterns in the performance data and identify any issues in the system before the actual problem occurs, such as resource exhaustion or memory leaks.
It can also help in the correlation of the data from various sources, such as logs, metrics and traces. This means that the AI can analyze the data in its totality and identify any issues that might have gone unnoticed if the data were examined separately. Gartner has listed the incorporation of AI as part of the strategic capabilities of observability that can allow companies to process large volumes of data and identify the insights that the human mind cannot perceive.
Standardization and Best Practices
To derive the benefits of observability, it is important to adopt industry standards and best practices. One such trend is the adoption of OpenTelemetry, a project of the CNCF, which provides a unified framework to manage metrics, logs and traces. According to SUSE, it provides vendor-agnostic telemetry from microservices, which simplifies data collection and analysis. It is open source, which provides the advantage of customization to suit business needs, eliminating vendor lock-in and reducing costs.
Another best practice is to centralize observability. According to Gartner, a Center of Excellence or a central team should be established to define the strategy, eliminate redundant data and standardize the tools. This practice reduces tool sprawl, supports business objectives and speeds up issue resolution, while fostering a shared understanding of the system’s health among the application developers, operations teams and security teams.
Another important practice to manage the volume of telemetry data is to adopt a smarter data collection strategy to reduce costs. According to the CNCF 2025 trends report, it is important to sample key traces, store only the logs that matter and store the non-essential data on cheaper storage. The pay-as-you-go model of observability platforms also supports the ability to scale with minimal financial commitments.
The Human Element: Collaboration and Culture
It’s not just a matter of tools; it’s a cultural shift in how we build and operate software. It’s a matter of developers thinking about instrumentation, ops teams valuing telemetry and business leaders using observability data as a window into customer experience and success. It’s a big enabler of cross-functional collaboration between engineering, security and product management teams.
As Segun Onibalusi, CEO of Detutu Media, explains: “Observability isn’t just about data; it’s about providing teams with early signals to innovate instead of react.” This aligns with the argument that leaders should invest in observability, as it empowers teams to innovate rather than merely react. It fosters a cultural shift that drives continuous improvement and innovation. Additionally, it enhances security by helping teams recognize patterns, while also building resilient systems and instilling trust.
Conclusion: Building Resilient Cloud‑Native Systems
The benefits of these cloud-native architectures are tremendous, including the flexibility, scalability and innovation speed, but so are the layers of complexity. This is where observability comes into the picture, enabling us to deal with the complexity of these architectures. This is where the concept of telemetry, through the usage of AI, turns into reality.
In the future, observability will be a key factor for any organization looking to succeed with the concept of cloud native architectures. It is important that people at every level, from the development team to the organization’s leaders, understand the importance of observability, which is no longer a choice but a necessity for the success of the organization, enabling it to deliver the kind of experience that the users are looking for through the usage of modern methods, modern technologies and observability, which is no longer a cost but a factor that enables innovation.
