From Cost Centre to Value Driver: A Strategic Approach to Observability in the Cloud

Cloud-native architecture delivers faster time-to-market, higher availability, and richer telemetry – fundamentally transforming how organisations operate. Yet, as cloud-native infrastructure scales, the rest of the tech stack must adapt to a new environment with distinct challenges and vulnerabilities.

Compared to traditional systems, data volumes increase by 10 to 100 times. This leads to observability costs often outweighing the cost of cloud infrastructure itself. The dilemma for cloud-native adoption planners and implementers is clear: how can companies keep costs under control while maintaining the visibility needed to deliver excellent customer experiences?

Cardinality’s cost complexity

The total number of unique combinations of metrics a system produces, also known as cardinality, is a critical driver of cost. Cloud-native architectures drive up cardinality as these combinations rapidly multiply. High cardinality then leads to increased telemetry data, which the organisation has to contend with.

As cardinality increases and additional dimensions are added to measurements, it becomes more expensive and more challenging to navigate. Organisations need smart data that is captured with the proper fidelity, for the right duration, and for the intended purpose. An exponential increase in data volume without those considerations becomes a hindrance instead of help.

Engineers recognise that strong observability is essential for innovation and the foundation for all business value. High-performing teams treat observability like any other business-critical programme. An effective operating model can be broken into a four-step optimisation cycle: govern, analyse, refine, and operate.

Govern

Many businesses don’t have real-time insight into which teams and applications consume the most observability capacity. Cost allocation is often manual and delayed, leaving teams ignorant to their consumption impact. Because of this, a team's budget can easily be exceeded before anyone notices.

To enforce governance, observability capacity must be logically divided into teams, services, and environments. IT managers can then set clear quotas to ensure that one group's data surge can't impede the rest.

Going one step further, more advanced observability practices not only monitor cost and usage attribution, but also set alerts for when limits are approached. With this increased visibility, a business can avoid unexpected bills, and take steps to mitigate the outcome.

Analyse

Traditional observability tools employ blunt levers to reduce data. At first glance, this is effective in meeting organisational needs, as those looking to cut costs can push data into a lower-cost tier, such as archive storage. While this might be useful in the short term, it also introduces risk. For instance, how can business leaders know their teams won’t need that data during a production incident?

Sustainable cost control requires understanding how teams actually use the data. Adopting platforms that demonstrate how data is utilised in dashboards, alerts, and queries provides a solid overview. Businesses can confidently minimise data collection by removing redundant and underused metrics or logs that teams never use. Once these processes are in place, identifying quick wins becomes easy.

This will enable the business to identify data streams that can be optimised in more complex ways, including aggregating noisy, high-frequency series using rollup rules, which is an effective way to control costs. These sophisticated rules maintain the visibility required to provide the best possible client experience.

Refine

Simply collecting less data is not a realistic option for those looking to maintain visibility into production services. Instead, businesses should analyse the most important metrics to gain insights. This will guide them in selecting and formatting the exact data required.

This calls for controls that enable teams to adjust structure and fidelity during incident response without losing signal quality. Strong data controls are delivered centrally. This way, an IT team can drop, summarise, or transform metrics without needing to re-instrument or redeploy services, and can preview changes to pre-empt the impact of a rule before enabling it. This capability allows for timely, safe responses during an incident whilst offering continuous optimisation.

Operate

Optimisation should be seen as a feedback loop rather than a one-off tidy-up. Once rules are in place, advanced observability practices will begin assessing impact. For example, one could track the efficiency of rules, such as the percentage by which data points per second are reduced, and demonstrate the resulting impact on observability costs.

Constant awareness of the financial implications guarantees that improvements stick. To detect regressions early, teams will need to maintain dashboards that track the impact of optimisation over time and monitor in real time for cardinality spikes and cost fluctuations.

Teams that bring governance, usage-aware analysis, and real-time control to observability achieve the outcomes that matter most to the board: lower, predictable costs and faster, more confident releases. Strong observability supports commercial value and is not a luxury. However, clarity, rather than accumulating everything, is what creates value. Organisations can maintain the transparency and sustainability of cloud systems by prioritising high-utility telemetry, transforming data at the source, and implementing continuous optimisation.