What Is Observability Architecture, and How Do You Build It?

Modern infrastructure spans cloud, on-premises and edge environments. With that scale comes a large and steady stream of metrics, events, logs and traces. Unified observability helps organizations bring that telemetry under control — ensuring it supports both operational performance and long-term resilience.

A sound architecture embeds governance and correlation throughout the telemetry pipeline. This approach makes signals easier to route, owners faster to identify and issues simpler to resolve. It also helps teams avoid sprawl, noise and compliance blind spots before they happen.

By adopting proven observability patterns and aligning them with business priorities, organizations can build visibility that scales with them. The goal is achieving a consistent framework for clarity, accountability and action.

What is an observability framework?

An observability framework is a system design that enables you to go beyond what happened and understand why. Monitoring helps you watch for predefined metrics and triggers alerts based on thresholds, while observability correlates the data with topology and time. As a result, engineers get fuller context that can inform troubleshooting and decision-making.

At its core, an observability framework establishes an enforceable data contract that guarantees consistent labeling, ownership mapping and correlation keys across all telemetry. This contract ensures that every signal can be routed to the right owner and correlated with related events. If a system can’t tell you who owns something with a single click, it’s worth fixing that friction point before adding another dashboard to your system.

When a framework includes this kind of governance model, alerts consistently go to the right person. That helps teams focus on big priorities — like whether a service is meeting its goals — instead of getting distracted or overwhelmed by infrastructure noise.

Understanding the core components of observability architecture

Observability architecture structures the way that you collect, process, store and visualize your telemetry. In this process, each stage has specific responsibilities that make correlation possible and keep signals trustworthy.

Data collection 

Data collection lays the groundwork for observability, specifically by capturing telemetry from all parts of your system with consistent instrumentation. Most teams start with OpenTelemetry, an open and widely supported standard for collecting logs, metrics, traces and events. In some cases, a single-agent option can simplify deployment, especially when speed and ease of setup matter.

Teams also selectively deploy eBPF to capture kernel-level visibility in places where traditional agents might create too much overhead. This can be especially useful for understanding network flows or system calls without modifying applications.

To make telemetry actionable, data must arrive with key labels already attached — like service name, environment and owner. These labels provide the context needed for routing and correlation. At this stage, the goal is consistent capture and labeling across all environments, from edge locations and virtual machines to Kubernetes clusters. Filtering and enrichment come later, during processing.

Data processing 

The processing stage enforces the observability framework’s data contract. This involves filtering, enriching and redacting telemetry before it moves downstream. In addition, sampling strategies are applied at this point. Head sampling reduces data volume, while tail sampling captures rare or anomalous events.

Each event is assigned correlation keys for time and topology before being stored. These keys enable teams to trace service relationships quickly during incidents. To preserve data quality, telemetry that lacks required labels is rejected or isolated to avoid polluting other systems.

Security and compliance controls also operate during this stage. Personally identifiable information is redacted, and all changes are logged to support auditing. This pipeline-level control sets the stage for consistent handling across the rest of the observability stack.

Data storage 

Storage design can help balance performance with cost. Clear limits on data movement and retention policies will help you avoid unexpected costs. Those limits also ensure that forensic data stays available.

Most teams tier telemetry into hot, warm and cold storage. Hot tiers return recent queries quickly, while cold tiers keep history at lower cost. Your service level objectives should specify expected query times for each tier.

To meet residency and latency needs, you may need to keep raw telemetry in region. Sending summaries and derived metrics to a central repositories will facilitate cross-environment analysis. This model is known as a federated architecture. It keeps detailed data local and moves only what is necessary. 

Data visualization 

The visualization layer delivers a unified view of signals without forcing every team to use the same interface. Instead of replacing tools, it provides a shared access point and supports a consistent understanding across environments.

Effective portals surface the most critical information first. These include service dependency maps that show how systems connect, fast owner lookups that support quick response, and alert policies based on business impact. Many also offer guided runbooks, which help teams respond quickly and consistently during incidents.

This approach accommodates the continued use of existing tools — including open source monitoring tools — while ensuring consistent governance. Effectively, the portal acts as a lightweight control layer that connects to existing platforms through open APIs.

Observability patterns for building resilient systems

Successful observability implementations follow patterns that address specific operational constraints while maintaining correlation capabilities. Understanding these patterns — and their corresponding anti-patterns — helps to turn cloud observability into manageable, day-to-day practice.

When organizations operate across industrial sites, retail locations or other edge deployments with intermittent connectivity, federation becomes essential. These environments require local observability stacks that operate autonomously, collecting and processing data even when disconnected from central systems. Subsequently, during connected periods, summary data and critical alerts can flow to central correlation engines. Note that federating without establishing correlation keys can create problematically incomparable silos, which defeats the purpose of unified observability.

Similarly, the choice between eBPF and traditional agents requires consideration of trade-offs. eBPF excels at capturing kernel-level visibility with minimal overhead, making it ideal for understanding network flows and system calls without modifying applications. Meanwhile, OpenTelemetry provides rich application context and distributed tracing capabilities that eBPF cannot match. The most effective architectures combine both approaches strategically — using eBPF where low overhead matters most and OTel where application context proves critical.

The moment data enters your observability pipeline should be the moment you determine usefulness. Enforcing labeling and ownership at ingestion helps prevent ungoverned data from contaminating downstream systems. At the same time, clearly non-compliant data can be rejected or quarantined immediately, with detailed logs explaining the reason. This approach avoids the common pitfall of accepting all data upfront and hoping to clean it later — a strategy that almost always results in ungovernable noise and troubleshooting dead ends.

In the context of observability, cost control depends on intelligent sampling that manages volume without sacrificing signal quality. Head sampling reduces routine traffic in a predictable way, while tail sampling captures rare but meaningful events like errors and timeouts. Together, these techniques provide visibility into both baseline behavior and unusual edge cases. When teams apply uniform sampling across all data types instead, they are more likely to miss critical signals. These events may be infrequent, but they usually point to serious underlying problems.

As observability architectures evolve, organizations may struggle to balance central governance with team autonomy. A thin portal layer can help by enforcing shared policies such as access control, routing and audit logging, while still allowing teams to use the tools they know best. This approach recognizes that different professionals need different interfaces. A site reliability engineer may require a very different view than a security analyst or a business stakeholder.

How to choose the best observability architecture for your business

Every organization has its own mix of constraints, priorities and existing infrastructure. Making thoughtful decisions about observability architecture helps ensure resilience over time — without losing sight of what is needed in your environment today.

Making defensible architecture decisions requires explicitly documenting trade-offs, constraints and reasoning. Architecture decision records (ADRs) provide a structured approach for capturing these choices. They serve as a historical record that explains what you decided and why you made that decision.

In the context of observability architectures, ADRs facilitate the evaluation of several key trade-offs. Should you prioritize data fidelity or cost control? How much latency can different use cases tolerate? Where must data remain regionalized, and where can it flow globally? The answers to these questions may be nuanced, and they will vary between organizations.

Choosing and implementing an observability architecture will require decisions in the following six areas, each of which involves unique challenges and opportunities.

Scalability and performance 

Start by defining latency targets for each tier of your observability stack. Hot data queries may need to return results in under 500 milliseconds, while cold storage can accommodate longer response times. Set throughput goals based on current usage, and include buffer capacity to support future growth.

Clarify when to use head sampling, which is typically suited for high-volume success traffic. Tail sampling, on the other hand, is better for capturing less frequent but important signals, such as errors, slow requests or security anomalies. Include configuration examples and test scenarios that demonstrate how your sampling strategy maintains signal quality.

Security, privacy and residency 

Begin by identifying which types of telemetry may contain personally identifiable information. Establish redaction policies as close to the data source as possible, and clearly define who has the authority to update those policies. Make sure all changes are logged in a way that supports auditability.

Next, build a regional data residency matrix to determine where different categories of data must remain geographically. For any scenario where data must move across regions, create a structured exception process that includes approval steps and documentation requirements.

Finally, define your encryption requirements. Be specific about how data should be protected both while in transit and at rest.

Integration and interoperability 

Create a list of the observability tools currently in use, along with an assessment of their ongoing value. As part of this process, identify which data formats and APIs are necessary to maintain interoperability. Whenever possible, favor open standards like OpenTelemetry and Prometheus over vendor-specific implementations that will limit your flexibility.

Ensure that service ownership is traceable and accurate by maintaining your ownership catalog as code within a version control system. Define how ownership metadata should flow through telemetry pipelines, which will allow tagging and routing to happen automatically.

Cost and cardinality

Establish cardinality limits for each service and metric family. High-value business metrics may justify higher cardinality thresholds than more routine infrastructure data. Many teams also define retention periods by storage tier, such as seven days for hot data, 30 days for warm and 13 months for cold.

Bring visibility to cost-efficiency by displaying the cost per 1,000 requests alongside your service-level dashboards. This approach will support a shared understanding of system performance and operational spend.

To manage risks proactively, set up alerts for unexpected increases in cardinality and establish circuit breakers that can pause or reroute data when limits are exceeded.

Operations, ownership and alert policy

Store your alerting policies in version control, rather than within a vendor platform. This enables policy-as-code practices that support ongoing collaboration, tracking and reuse.

Map every service to a clearly defined owner. That information should automatically flow into on-call rotations. Base escalation logic on indicators such as error budget consumption or time-to-impact, rather than raw infrastructure metrics. Ideally, on-call responders should be identifiable from any alert with a single click.

Pilot and rollout plan

Before launching a pilot, define its scope, services included, success benchmarks and rollback conditions. Use specific, predefined targets to guide your implementation. For example, over a specific period of time, you might try to cut mean time to resolution in half, correctly route 90% of alerts or reduce tool-related costs by a third.

When planning the broader rollout, take a phased approach. Assign clear responsibilities and set training checkpoints, milestone dates and go or no-go criteria. This level of structure creates smoother transitions and better feedback throughout the process.

Why a unified observability architecture is crucial for modern IT

Modern IT environments are dynamic and distributed, spanning multiple clouds, edge locations and on-premises systems. To navigate this complexity, organizations need architectures that provide hybrid cloud observability and deliver technical insights from all environments — insights that can directly support business priorities and operational realities. A unified architecture can help you achieve these interconnected goals. 

Share context and retain tool choice

When telemetry is consistently labeled, correlated and governed, teams can route signals reliably, respond faster to incidents and maintain clarity even as systems scale. A thin control layer above the observability stack makes this possible without sacrificing tool flexibility. By enforcing routing, access controls and audit policies at this level, organizations can preserve team-specific interfaces while improving governance consistency and reducing friction across environments.

Stay compliant without creating silos

In addition, unified architectures simplify regulatory compliance and data sovereignty. By supporting federated storage, teams can retain raw telemetry locally while centralizing high-value signals for cross-regional analysis. This approach ensures that requirements are met without fragmenting operational visibility.

Increase cost visibility

Cost management also benefits from architectural unification. When telemetry includes business context — such as service ownership or transaction metadata — teams can more quickly and easily correlate cost with impact. Displaying cost per request alongside SLO dashboards helps promote shared accountability across engineering, operations and finance teams.

By connecting governance, context and collaboration through a unified approach, observability becomes a catalyst for operational maturity. You gain the clarity, accountability and resilience needed to operate effectively in today’s fast-moving infrastructure landscape.

What is the future of observability?

As systems evolve, observability architectures must adapt to include new types of signals and safeguards. One important frontier is identity-based telemetry, such as SPIFFE or mTLS verdicts, which confirm workload authenticity and authorization. By integrating these signals into existing MELT schemas, teams can improve traceability and access control without introducing additional silos.

Another key variable is AI workload telemetry. As more organizations run inference workloads, observability systems need to support signals like GPU utilization, inference latency and model drift. Treating these signals as first-class data ensures comprehensive visibility across both traditional and AI-powered systems.

At the same time, AI-assisted automation is beginning to reshape incident response. AI can help cluster anomalies and recommend remediations, but these technologies perform best with appropriate oversight. Guardrails like explicit approval gates, scheduled maintenance windows and reliable rollback procedures will help you manage operational risk.

By updating collection practices and governance policies today, organizations can prepare for a future in which observability supports both human-led decisions and machine-assisted operations.

Building a resilient observability architecture with SUSE

An observability platform can help you put unified observability principles into practice. Solutions like SUSE Cloud Observability support fast, flexible deployment through a single-agent approach, with built-in compatibility for both OpenTelemetry and selective eBPF. This makes it easier to capture the full range of signals — from high-level service traces to low-level kernel events.

In addition, SUSE’s platform supports progressive data correlation. Data moves from raw telemetry through enriched context and pattern recognition, all the way to guided remediation. This layered approach helps teams connect signals quickly and take informed action without sifting through unstructured data.

To reduce setup time, SUSE Cloud Observability includes prebuilt patterns for common scenarios. Step-by-step resolution paths help standardize responses across teams without limiting flexibility. Integration with existing tools is supported throughout,  helping you maintain current workflows while also gaining the benefits of unified observability.

Explore how SUSE Cloud Observability can accelerate your observability transformation.

Observability architecture FAQs

What is the difference between observability and monitoring?

The difference between observability and monitoring is that monitoring only watches for known issues using set metrics and alerts. Observability goes further and helps you find unknown problems by connecting metrics, logs, traces and events with service relationships and timing.

What is a unified observability platform?

A unified observability platform connects telemetry through shared schemas and processing pipelines. It supports multiple tools while ensuring consistent ownership tagging, alert routing and audit trails, providing teams with a single source of truth.

What is topology-driven incident response?

Topology-driven incident response uses real-time service maps and time-based correlation to trace impact, assess blast radius and route alerts to the right owner. Instead of relying on static rules, it reflects live relationships between services.