Tiered Observability: How To Prioritize and Mature Observability Investments

You may be surprised that delivering observability is a journey and isn’t about observing everything at once — it’s about driving outcomes like proactive detection, faster troubleshooting, and aligning with business priorities. If you’ve followed this series, you’ve already taken steps to:

• Centralize your observability strategy.
• Rationalize & simplify tools.
• Establish measurements to iteratively improve your observability practice.

As Winston Churchill put it, “Perfection is the enemy of progress.” Enterprises managing hundreds of applications must prioritize observability (aka o11y) investments wisely. While every application owner sees their service as critical, business impact varies widely. This requires a structured tiered observability approach. Meanwhile, smaller or fast-growing startups may not yet require tiered observability, but as their business expands, adopting a tiered approach early can provide long-term scalability.

Spreading coverage too thin leads to alert noise and inefficiency, while failing to monitor critical applications creates blind spots. So, what is the solution?

Tiered observability aligns investments with business priorities, ensuring critical services get the highest visibility while optimizing resources for maximum impact.

What is Tiered Observability?

A tiered observability approach helps teams to prioritize investments, reduce complexity, and focus on what matters most. When observability aligns with business priorities, organizations avoid wasted resources, reduce noise, and improve operational efficiency.

A properly executed strategy enables:

• Lower MTTR: Faster issue resolution through deeper visibility into critical applications.
• Cost optimization: Observability spending that scales with business impact.
• Better signal-to-noise ratio: Prioritized, meaningful alerts over unnecessary noise.
• Scalability & efficiency: A repeatable model that grows with your organization.

Observability should be intentional, scalable, and business-aligned. To accomplish this, start classifying applications, aligning observability expectations with tiers, and streamlining tooling and automation.

To understand why a tiered approach to Observability o11y can be beneficial, let's look at the way most organizations today are doing o11y: in an unstructured manner.

Enterprise challenges of observing everything now

Many organizations attempt “observability for all”, believing that full visibility across every system will lead to better outcomes. However, this approach rarely scales. The reality is that observability requires time — often the most limited resource. Without prioritization, organizations quickly run into operational and financial challenges:

• Limited resources to maintain and support full observability coverage.
• Scalability issues: too many alerts, too much noise, and too little context.

Not all applications are designed or maintained with the same level of importance. Likewise, lower-tier services may not require 24/7 observability. For example:

• An out-of-disk-space alert on a production database could trigger an urgent response
• The same issue on a sandbox development server likely doesn’t require immediate attention.

Without a structured approach to prioritization, teams often treat these events with the same level of urgency, leading to wasted cycles and alert fatigue.

The consequences of no prioritization

Trying to observe everything without prioritization doesn’t just create technical debt — it impacts business outcomes. Organizations that fail to focus on the most critical services first often deal with:

• Low-confidence alerting
• Inefficient use of resources
• Tool sprawl
• Engineer burnout

A lack of clear prioritization can delay incident resolution, increasing MTTR and negatively impacting customer experience.

No prioritization also frustrates engineers. This frustration can lead to shadow IT, as teams seek alternative solutions outside the standardized observability stack. This fragmentation leads to:

• Inconsistent visibility
• Rising costs
• Duplicated efforts across teams

Tiered observability balances breadth vs. depth

Observability must strike a balance — wide enough to detect systemic issues, yet deep enough to troubleshoot mission-critical applications. Just as in agile development, teams must focus their efforts on the most important areas first. Full coverage, across every service, can come later.

In my experience, I've learned that teams should apply a foundational layer of observability (see the getting started tiering example table below) to all services. This foundation ensures basic instrumentation for metrics, logs, and alerting.

Initially, deeper observability capabilities should be reserved for Tier 0 and Tier 1 applications (which we'll cover in the next section). This approach ensures deep instrumentation, including APM, RUM, distributed tracing, and profiling, which provides fine-grained telemetry and is positioned to provide the most business value.

Lower-tier services can be improved over time as the observability practice evolves and as business needs shift or failures highlight gaps. Organizations often view tiering as an ongoing strategy, not a one-time classification exercise. (see “Observability Capabilities by Tier: Expectations & Transparency” section below)

Common approaches to tiering

Enterprises and large organizations often classify their applications based on:

• Business impact
• Operational criticality
• Risk tolerance

This classification helps define how applications are managed, secured, and supported, so that resources are allocated efficiently.

Highly critical applications — such as revenue-generating services, customer-facing platforms, or life/safety systems — require greater investment in resilience, observability, and performance management. On the other hand, lower-priority applications may not require the same level of redundancy, 24/7 support, or in-depth observability. These may include internal tools, non-production environments, or non-essential background services.

How application tiering influences IT strategy

These classifications often serve as a foundational input into IT strategy and decision-making, influencing key areas such as:

• Security policies
• Architecture standards
• Performance & testing strategies
• Service management requirements

Observability should be no different. The same classification logic should also drive observability strategy and expectations — ensuring that observability coverage, alerting, and troubleshooting workflows align with application criticality.

Common tiering models

Organizations typically use one of two methods to classify their applications:

• Numeric tiering: Tiers 0-3
• Metal classifications: Platinum, gold, silver, bronze tiers

Key considerations for Tiered Observability

Implementing a tiered observability approach goes beyond simply categorizing applications. It requires aligning observability instrumentation, alerting, and response strategies with business impact. Below are key considerations to ensure observability investments are effectively prioritized and deliver meaningful insights.

Observability across application environments

Observability must extend beyond production — but not every non-prod environment requires full coverage. A “Prod-1” environment for highly critical applications can serve as a pre-production safety net, allowing teams to validate observability coverage before a full production rollout.

As a best practice, adding one tier from production can determine the non-prod environment’s observability level — for example, a Tier 0 application’s non-prod counterpart might be classified as Tier 1. This ensures that developers working on high-priority projects aren’t blocked by observability blind spots, while still keeping costs and noise in check.

A well-monitored pre-production environment allows teams to:

• Validate observability effectiveness by testing thresholds, anomaly detection baselines, and KPIs in a non-production setting. Ensuring that alerting mechanisms work as expected helps avoid post-deployment surprises.
• Detect deployment-related downtime by observing latency spikes, error rates, and resource constraints before go-live.
• Validate observability coverage as part of chaos engineering and load testing, ensuring alerts and dashboards accurately reflect failures under real-world stress conditions.
• Proactively identify changes in functionality, performance, and utilization before production. While true proactive observability is the ultimate goal, catching impactful changes right before production is arguably as proactive as it gets.

As a the observability leader, I dreaded the IT exec asking, ‘How wasn’t this caught in the lower environments?’” Proactively ensuring that Tier 0 and Tier 1 release go/no_go decisions include observability validation can prevent this uncomfortable conversation.

Observability capabilities by tier: Expectations & transparency

A transparent tiering model helps teams understand what level of observability coverage to expect per application tier.

Properly aligning observability coverage with tiered workloads allows organizations to better understand the total cost of ownership (TCO) of their observability strategy, ensuring that investments scale with business impact rather than technical sprawl. A transparent observability tiering strategy not only helps frame the narrative when lower-tier application issues are raised as priorities but also ensures engineers can focus on high-value work instead of constantly tinkering with observability tools.

Getting Started Observability Tiering Example: Start your observability tiering journey with some fundamentals.

Iterative Tiering Maturity Example: As you mature your observability tiering strategy, consider including additional activities and/or leveraging other organizational activities to drive additional business value.

Beyond the tools: Ensuring unified visibility & continuous improvement

Observability isn’t just about the tools — it’s about how teams use them. When multiple tools are required to fully observe an application, there must be a unified experience to avoid excessive tool-switching (“swivel chair” operations).

Observability champions should:

• Ensure tool interconnectivity and alignment across teams, avoiding fragmentation and duplication of effort.
• Promote the utilization of the Golden Set of Tools to meet your observability objectives.
• Facilitate collaboration between Observability/Platform Engineering teams and the engineers (including SREs, ITOps, and Application Development) who rely on these tools to detect, investigate, and resolve issues effectively.
• Encourage teams to continuously upskill observability tools, training, and as-code approaches to optimally leverage the observability tools.
• Keep internal teams engaged with observability vendors through regular syncs. This will lead to stronger tools adoption & utilization, and more effective observability outcomes.

Include tiering in your observability metadata strategy

A well-defined metadata and tagging strategy is a critical enabler for observability. Without proper tagging, high degrees of instrumentation can become overwhelming and difficult to operationalize effectively.

Think of observability metadata as the “split by” function in a pivot table — when properly structured, it allows teams to slice, filter, and correlate data efficiently to drive meaningful insights.

Adding tiering metadata into tagging strategies provides several key benefits:

• Automated observability enforcement: Ensuring observability policies, alerting configurations, and retention settings align with application criticality.
• Enhanced cost optimization insights: Understanding observability spend relative to application tiers to ensure cost aligns with business value.
• Improved cross-tool correlation: Ensuring that applications, services, and infrastructure can be accurately grouped, filtered, and analyzed across observability platforms.

Observability expectations for monoliths vs. modern architectures

A tiered observability strategy is only as effective as its execution across different application architectures. Ensuring that observability expectations are met across both legacy and next-gen workloads is key to delivering value.

Meeting observability requirements for monoliths

Many enterprises still rely on monolithic applications that were never designed for modern observability practices. Many of these systems — such as ERP, CRM, and core transactional platforms — are among the most business-critical.

Key considerations for legacy observability:

• Do the research. Not all modern observability tools are compatible with legacy systems.
• Levarge best of breed APM solutions like Splunk AppDynamics.
• Understand instrumentation risks and limitations before observability deployment.

Next-gen applications: Automating observability from Day One

For modern cloud-native, microservices-based, and serverless architectures, observability must be built into the development process. Best practices for next-gen observability include:

• Enable Baseline Observability as a Default. Every application should have baseline observability (basic logs, metrics, and uptime checks) baked in from day one. From there, tiering determines deeper coverage.
• Leverage Observability-as-Code (OaC) interactions and automation with the observability tools.
• Embrace OpenTelemetry (OTel)’s vendor-agnostic and automatic instrumentation capabilities.

How to build a scalable observability model

Observability isn’t just about visibility, it’s about prioritizing coverage where it matters most. A tiered observability strategy ensures that your most critical applications receive the depth of monitoring, alerting, and response they require, while lower-tier services maintain a right-sized level of observability.

To get started, identify your highest-tier applications and assess whether they have appropriate observability coverage. Do they have the right instrumentation, alerting, and visibility into performance and reliability? If gaps exist, these should be your top priority before expanding observability coverage elsewhere.

To ensure long-term success, tiering should not be a one-time exercise but an integral part of your observability strategy. Regularly reassess application tiers as business priorities shift, ensuring that your most critical workloads continue to receive the highest level of coverage. Refine your observability practices by aligning them with business impact, eliminating unnecessary noise, and making data-driven decisions about where to deepen coverage. By structuring observability investments around tiering, organizations can reduce MTTR, optimize costs, and drive efficiency — keeping engineering teams focused on delivering business value.

Observability how-to's for the real world

Love O11Y content like this? Be sure to check out the other blogs in this series and stay tuned for more!

• Introducing the Observability Center of Excellence (CoE)
• Formation of the Observability CoE
• Measuring and Improving Observability-as-a-Service
• Rationalize and Realize: Simplifying Tools for a Leading Observability Practice
• How To Scale Observability Adoption Through Self-Service