Observability and monitoring are related concepts in the realm of IT and system management, but they have distinct differences in terms of scope, approach, and capabilities. If a vendor is suggesting they are offering observability, then its quite easy to qualify whether they do or not.
Quick recap on observability, small history lesson for those who are new. The term "observability" is rooted in control theory, a mathematical discipline used to study systems, including IT systems. The goal is to ensure the safe and efficient operation of these systems. In this context, a system is considered observable when its internal state can be inferred from external outputs, such as Service Level Indicators (SLIs) and Service Level Objectives (SLOs). For example, while we may not have direct visibility into the exact speed of data processing, we can use SLIs and SLOs, via volume, response time and error rate, to create models that help us understand the system's performance and whether it meets its objectives.
While some vendors have only recently embraced the term "observability," the concept has been in practice for quite some time. Going back to 2008, during my tenure at a prominent mortgage company in the UK, we were advocating for the inclusion of health checks in developers' code. These checks encompassed internal assessments such as "x transactions are okay" and "all pre-requisites are in order." By swiftly examining an API or web service, these checks provided a straightforward indication of whether things were functioning smoothly. In essence, observability was already a part of our practices at that time. We were simulating a dummy credit check to external vendors like Experian to check connectivity and response integrity was as expected as part of those checks. If any checks failed, we knew exactly what the business impact would be and how to fix it (wish we had stuff like automation then, although we were pretty handy with our scripts).
Today, we collaborate with numerous vendors and frequently experiment with new tools in the marketplace to explore their potential for providing unique solutions to our customers. It's noteworthy that many vendors label their offerings as 'observability' tools, often with similar features. Peel them back a little, and its not quite hitting the spot. To help differentiate and compare these concepts effectively, here is a handy table for reference.
Aspect | Monitoring | Observability | Pointer |
Focus | Specific metrics and known issues. | Broader system behaviours, insights, and unknown issues. | Observability tools generally support more combinations and cater for more scenarios than typical monitoring tools. There is where a monitoring tool is sometimes labelled a point solution. There to monitor metrics in a database, but not considering the logs and traces from real transactions that build a true picture. |
Data Collection | Metrics and predefined thresholds. | Metrics, logs, traces, and events, often unstructured data. | Monitoring tools typically do not cater for traces or logs, they tend to handle metrics only. This is a sure way to tell if you are going to monitor, or observe. Synthetic only tools or infrastructure monitoring tools tend to claim "observability", but actually do not cater for any outside data or richer telemetry like a log. |
Data Volume | Typically lower volume. | High volume of data from various sources. | In summary, monitoring emphasizes specific, structured metrics with lower data volumes, while observability embraces a broad range of data types, both structured and unstructured, and handles high data volumes. Observability's capability to handle diverse data types and high volumes makes it well-suited for understanding complex, dynamic, and often unpredictable modern software systems. Look out for data retention and control, some vendors offer only short term fixed. |
Alerting | Rules-based, predefined alerts. | Alerts based on anomalies, patterns, or deviations. | If your solution provides automatic notifications when issues arise, it's a strong indicator of observability. In contrast, predefined thresholds are typically associated with monitoring tools. Think about zero touch monitoring and alerting, that's the way to go here. |
Root Cause Analysis | Limited to what is monitored. | Provides insights for deeper root cause analysis. | Overall, monitoring is well-suited for tracking specific metrics and known issues, up vs down, hot vs cold. Whereas observability excels in providing a more holistic view of complex and dynamic systems, facilitating proactive issue detection and comprehensive root cause analysis. The better tools on the market capture the whole series of events and offer a replay capability. This is a brilliant use case. |
Reactive vs. Proactive | Mostly reactive, based on predefined thresholds. | Proactive, as it allows discovering issues before they become critical. | If your solution can predict an event, and pre-emptively self heal...you are in the land of observability and AIOPS. Not doing this means you are treating symptoms only, or monitoring. |
Scalability | Scales well for specific metrics. | Scales to handle diverse data sources and high volumes. | Monitoring scales efficiently for predefined metrics with lower data volumes, while observability is designed to handle high data volumes from diverse sources, suitable for understanding complex and dynamic systems. |
Complexity | Less complex and straightforward. | More complex but provides a deeper understanding. | Monitoring is comparatively less complex, focusing on predefined metrics and thresholds, while observability is more intricate, capturing a wide range of structured and unstructured data, allowing for a deeper understanding of complex systems and issues. |
Exploration & Discovery | Limited exploration beyond predefined metrics. | Supports ad-hoc exploration of data and correlations. | In summary, while monitoring is ideal for tracking predefined metrics and known issues, observability excels at supporting exploratory analysis and the discovery of unknown or unexpected issues within a system. Thinks outside the box and recognises patterns. |
Troubleshooting | Focused on known issues and their resolution. | Supports identifying and troubleshooting unknown issues. | In summary, while monitoring is limited in its ability to provide an in-depth view of root causes, observability offers a broader and more diverse dataset, enhancing the capacity for comprehensive root cause analysis, especially in complex and dynamic systems. |
Context | Mainly technical insight measurement. | Combines both technical and business insight through context. | While monitoring may provide technical insights, observability offers a deeper understanding of the context and relationships between technical components and their impact on business services, making it particularly valuable for troubleshooting and improving the user experience. |
Audience and Personas | Typically smaller technical audiences. | Company wide audience with insights and context that are meaningful to persona. | While monitoring tools are primarily used by technical teams to ensure system stability and performance, observability tools have a broader user base, including cross-functional teams, and offer a more comprehensive view of complex systems and user experiences, facilitating a proactive approach to issue detection and analysis. |
In summary, monitoring is typically more focused on specific, predefined metrics and known issues, with rule-based alerting and limited exploration. Observability, on the other hand, provides a broader view of system behaviour, with the ability to analyse unstructured data, detect anomalies, and proactively troubleshoot issues, making it a valuable tool for understanding complex and dynamic systems.
Before embarking on the evaluation of new tools, renewing existing ones, or engaging with new vendors, a pivotal initial step is to define and comprehensively understand your use cases. This necessitates collaborative discussions with your DEVOPS, SRE, Incident Management, and support teams to pinpoint their specific requirements and identify existing gaps. Open communication and transparency are key to building trust in the tool selection process, a fundamental factor that significantly influences the success of your observability journey.
The foundation of this process is an observability use case qualification document, comprising meticulously defined use cases. These use cases play a crucial role in guiding the evaluation of both existing and prospective solutions. They not only expedite the process of eliminating contenders who make claims of offering observability but cannot deliver, but also help in identifying and prioritizing those providers that can.
If you need help with selection, a strategy or defining use cases, drop us an email hello@visibilityplatforms.com