Observability is the process of using metrics, logs, and traces to learn about the internal states of your DevOps systems. Successful observability implementations allow you to easily find out what’s happening in your services, as well as why and how the current state exists.

For example, observability data could tell you the exact sequence of events leading up to a performance problem, enabling you to ship a fix much faster.

Observability also plays a key role in microservices architectures. Deploying interconnected services across distributed compute environments requires you to easily see which services are running, where they’re deployed, and how they communicate with each other. Metrics, logs, and traces provide the answers, giving you clear visibility into your stack.

In this guide, we review 11 of the best practices for effective observability. These actionable tips will help you build scalable observability systems that deliver proactive insights, align with your business needs, and offer optimal cost efficiency. Let’s get started.

What makes a good observability strategy?

Observable systems allow you to quickly understand their state and how it has arisen. They generate metrics, logs, and traces that enable you to easily debug any problems. Seeing your system’s current state, along with the events that led to it, enables you to pinpoint the precise causes of incidents.

Effective observability strategies are accurate, accessible, and comprehensive, ensuring stakeholders can quickly find the information they need. Beyond these basic components, observability data must also align with your business KPIs so it’s relevant and actionable.

Observability data doesn’t create value on its own, so your strategy also needs to consider the broader picture. Your DevOps processes must be carefully designed so they’re receptive to observability insights at each stage.

Successful observability systems should provide data that directly contributes to DevOps planning, design, and feedback loops, for instance.

11 key observability best practices

1. Focus on the three pillars of observability (Metrics, logs, and traces)

Metrics, logs, and traces are the three main types of observability data. Each one plays an important role in your overall ability to inspect a system’s state:

• Metrics are discrete numerical values such as CPU utilization, latency, and error counts.
• Logs are textual messages that record key events in your applications.
• Traces are detailed analyses of a request’s path through a system, allowing you to see how individual requests have passed between microservices and call stack layers.

A complete observability strategy should include all three components. Missing any of them creates a visibility coverage gap that will prevent you from achieving your observability aims.

Conversely, fully instrumenting your apps and services allows you to identify anomalies in metrics, investigate them using logs, and then check the interactions between services using traces.

2. Align your metrics with your business KPIs

Observability data is only meaningful if it’s aligned with your business KPIs. There’s no point in collecting data that’s not relevant to your objectives — it’ll only create noise, fill up storage, and increase costs.

To decide which metrics you’ll collect, first identify your KPIs. For example, if you’re aiming for 99% availability, you may track latency and error rates within your observability stack.

Given that metrics such as the total number of requests received won’t directly reflect your aims, you don’t need to include them. Linking metrics to KPIs increases the likelihood that your observability investment will actually improve business outcomes.

3. Ensure collected data is actually actionable

We’ve already touched on it above, but this point can’t be emphasized enough: observability data is only valuable if it’s actionable.

Proper analysis of the data should reveal the actions you need to take in response, such as spinning up a new service replica when latency spikes. Avoid tracking metrics that are only weakly connected to your operating procedures, as this creates clutter that can disguise more important trends.

Different stakeholders have unique data requirements, so it’s often useful to present personalized views of the metrics and logs you’ve created.

A filtered view of logs that highlights specific risks could be useful for compliance teams, whereas operations teams may focus on infrastructure metrics instead of application-level traces. Filtering makes the data more actionable for each consumer.

4. Make observability data accessible to the stakeholders who need it

Observability data is most useful when it’s a shared resource that anyone can access. Siloing metrics and logs prevents stakeholders from seeing the bigger picture, potentially leading to misdiagnosis of problems.

In comparison, universally accessible observability systems allow contributors to efficiently analyze how their changes affect workloads, infrastructure components, and business outcomes.

Observability platforms, such as Grafana, enable you to centralize data access, providing both control and flexibility. They give developers, operators, and managers a single destination to achieve end-to-end visibility of the DevOps stack. They also enable you to easily expose data to non-technical stakeholders, such as business leaders, allowing for a broader range of perspectives to be obtained.

5. Instrument your own code for observability

Most DevOps teams begin their observability strategy by focusing on core infrastructure metrics, such as CPU utilization, network bandwidth utilization, and error rates. While this provides useful data about performance and reliability, truly observable systems also provide a clear view of what’s happening inside each deployed application.

To achieve this, you should instrument your own code for maximum observability. This means exposing custom metrics for the values that matter to your team, such as the number of users signing up or transactions being processed.

You can use tools such as the Prometheus client libraries to provide these metrics from an endpoint in your apps, ready to be scraped by your metrics server.

Similarly, ensure your apps generate comprehensive, structured logs that include sufficient information for developers to efficiently diagnose problems. You can use tools like OpenTelemetry to export traces, allowing you to precisely analyze how users and services interact with each other.

6. Standardize observability tools and processes across your stack

Standardizing on a single set of observability systems means there’s one destination to get insights from any service. This enhances debugging efficiency and facilitates the correlation of insights across various services and infrastructure components.

It also prevents confusion during incident response by avoiding team members having to jump between multiple independent platforms.

For instance, if you’re using Datadog for cloud monitoring, it makes sense to instrument your apps using the Datadog client libraries too. You can then monitor application events and cloud provider metrics in one place. If you spot a spike in CPU utilization, you can move directly into investigating the logs and traces emitted prior to the event.

7. Automate observability alerts and actions

Observability is always best when it’s proactive, not reactive. To this end, it’s critical to set up automated alerts that keep you informed when your metrics change. Without alerts, you must remember to check your monitoring systems manually. This means your view of the data always lags behind events; it could even prevent you from noticing transient problems that quickly resolve themselves.

Alerts solve these issues by informing you of significant changes as they happen. Nonetheless, it’s possible to have too much of a good thing.

It’s generally best to reserve alerts for truly important issues that deserve immediate attention. Sending too many alerts risks triggering alert fatigue, so it’s important to find the right balance that fits your operations.

Beyond preconfigured alerts, many modern observability solutions also feature fully automated anomaly detection and resolution capabilities. Platforms like Middleware and Dynatrace allow you to build advanced observability workflows that connect back to your cloud accounts. They enable hands-off tuning by applying infrastructure changes automatically, based on observed anomalies.

8. Build custom visual dashboards to simplify trend monitoring

Using dashboards to visualize metrics makes the data accessible to the stakeholders who need it. Dashboards provide a convenient way to identify trends, compare different time periods, and emphasize the metrics that matter most. Dashboards can also help you contextualize data within your business environment by organizing metrics to reflect your operational approach.

Observability platforms typically come with a built-in set of dashboards designed for the most common use cases. However, while these can offer a useful starting point, they should be customized to reflect your unique needs.

Settling for the defaults will mask insights specific to your KPIs, whereas custom dashboards enable you to highlight the most important values for your stakeholders.

9. Continually review and iterate upon your observability strategy

Your observability requirements will evolve as you refine your DevOps workflows. Regularly review the data you’re capturing and the insights it reveals, and then identify areas to improve. For instance, you may find coverage blind spots, missing alerting configurations, or an excess of low-value metrics that create noise.

Your cycle should create a positive iterative feedback loop:

1. Analyze collected observability data to identify new opportunities.
2. Revise your observability systems to capture the opportunities identified in Step 1.
3. Review your data again to assess the effects of the changes made in Step 2.
4. Continually iterate upon this cycle (return to Step 1).

With this framework, each change provides data that informs the next cycle. Making small improvements incrementally allows you to build observability systems that closely align with your DevOps workflows and system architecture, ensuring long-term success at scale.

10. Choose scalable observability platforms that work with your current tools

Finally, DevOps teams often jump for the newest, shiniest tools. However, when selecting an observability suite, it’s best to opt for platforms that support deep integration with your existing stack. At the same time, you should also consider the performance, scalability, and cost associated with different toolchain options.

Prometheus is a popular solution because it can be used for app-level instrumentation and to monitor infrastructure such as Kubernetes clusters. This doesn’t mean it’ll necessarily fit your operations, however — if you’re all-in on a particular cloud provider, such as AWS, then you may prefer to use built-in tools like CloudWatch to reduce complexity and cost.

The most effective observability system will collect the most data from your stack, provide the simplest query and visualization capabilities, and be easy to integrate with other platforms for future scalability.

11. Integrate observability into your development lifecycle

Do not treat observability as an afterthought or a purely operational concern. Build it into the development and deployment lifecycle so that every change ships with clarity. Add automated observability checks to your CI/CD pipelines, require consistent instrumentation during code reviews, and release new features with predefined metrics, logs, and distributed traces.

This shift-left approach helps teams catch regressions before they reach production, shortens incident response, and strengthens a culture where reliability and visibility are shared across every engineering team.

Improving workflows with Spacelift

Spacelift allows you to connect to and orchestrate all of your infrastructure tooling, including infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.

Spacelift enables powerful CI/CD workflows for OpenTofu, Terraform, Pulumi, Kubernetes, and more. It also supports observability integrations with Prometheus and Datadog, letting you monitor the activity in your Spacelift stacks precisely.

With Spacelift you get:

• Multi-IaC workflows
• Stack dependencies: You can create dependencies between stacks and pass outputs from one to another to build an environment promotion pipeline more easily.
• Unlimited policies and integrations: Spacelift allows you to implement any type of guardrails and integrate with any tool you want. You can control how many approvals you need for a run, which resources can be created, which parameters those resources can have, what happens when a pull request is open, and where to send your notifications data.
• High flexibility: You can customize what happens before and after runner phases, bring your own image, and even modify the default workflow commands.
• Self-service infrastructure via Blueprints: You can define infrastructure templates that are easily deployed. These templates can have policies/integrations/contexts/drift detection embedded inside them for reliable deployment.
• Drift detection & remediation: Ensure the reliability of your infrastructure by detecting and remediating drift.

To learn more about Spacelift, create a free account or book a demo with one of our engineers.

Key points

Observable systems clearly reveal the what, why, and how of their current states. They enable you to make informed, data-driven debugging decisions. Collecting metrics, logs, and traces also allow you to analyze service usage and pinpoint performance problems, making your services more reliable.

This guide has outlined 11 best practices for collecting, accessing, and using observability data. Following these tips will enable you to build a robust observability strategy that delivers meaningful insights throughout your DevOps lifecycle.

You don’t have to implement everything on day one, however. With observability, it’s usually best to start small and then expand your systems as you learn how you actually use your data. Begin by collecting only the key actionable metrics that will allow you to measure your business KPIs. Once you’ve covered the basics, you can refine your observability strategy by iteratively layering in new tools, metrics, and processes.

Ready to start building your observability stack? Discover the leading platforms in our round-up of the top 20+ DevOps monitoring tools to try this year.

James Walker

James Walker is the founder of Heron Web, a UK-based software development studio providing bespoke solutions for SMEs. He has experience managing complete end-to-end web development workflows with DevOps, CI/CD, Docker, and Kubernetes. James is also a technical writer and has written extensively about the software development lifecycle, current industry trends, and DevOps concepts and technologies.