Observability: Beyond Monitoring — How to Actually Understand Your Systems

You cannot fix what you cannot see. That principle drives every decision in modern software operations, and observability is the discipline that makes seeing possible. According to Splunk's 2023 State of Observability report, organizations with mature observability practices resolve incidents 69% faster than those without. Yet most engineering teams confuse observability with monitoring, buy tools without strategy, and end up drowning in dashboards that answer questions nobody asked.

I built engineering teams at companies where production incidents consumed 30% of our sprint capacity. The turning point was not buying a better monitoring tool. It was shifting from a monitoring mindset (watching for known failures) to an observability mindset (understanding system behavior from the outside by examining its outputs). That distinction sounds subtle. In practice, it changes everything about how you instrument, debug, and operate software.

This guide covers what observability actually means, how it differs from monitoring, the three pillars that make it work, and how codebase context transforms observability from a reactive tool into a proactive practice.

Observability is the ability to understand a system's internal state by examining its external outputs. The term originates from control theory, where a system is "observable" if you can determine its complete internal state from its outputs alone.

In software, this translates to a practical question: when something goes wrong in production, can your team figure out what happened and why without deploying new code to add more logging? If the answer is no, your system is not observable. It is merely monitored.

Monitoring tells you when predefined metrics cross predefined thresholds. "CPU usage exceeded 80%" is a monitoring alert. Observability lets you ask arbitrary questions about system behavior after the fact. "Why did checkout latency spike for users in the EU between 2:14 PM and 2:23 PM on Tuesday?" is an observability question.

The difference matters because production failures are increasingly novel. In distributed systems with dozens of services, the failure modes are combinatorial. You cannot predict every possible failure and set up a dashboard for it in advance. Observability gives your team the tools to investigate failures they have never seen before.

Charity Majors, CTO of Honeycomb and one of the most influential voices in the observability space, puts it directly: "Observability is about being able to ask new questions of your systems without having to ship new code." That capability is what separates teams that resolve incidents in minutes from teams that spend days reproducing issues.

Monitoring and observability are not synonyms, and treating them as interchangeable causes real problems.

Monitoring is reactive and predefined. You decide in advance what to watch (CPU, memory, error rates, request latency) and set thresholds that trigger alerts. Monitoring answers the question "is this specific thing broken?" It works well for known failure modes. If your database runs out of connections, a monitoring alert fires.

Observability is exploratory and open-ended. You instrument your system to emit rich, structured data (logs, metrics, traces) and then query that data to investigate behaviors you did not anticipate. Observability answers the question "why is this thing behaving unexpectedly?"

A practical example: monitoring tells you that the checkout service error rate jumped from 0.1% to 5%. Observability lets you drill into those errors and discover that they only affect users with more than 10 items in their cart, only when the inventory service responds in over 200ms, and only since the deployment at 2:00 PM introduced a timeout change.

According to Gartner's 2024 Market Guide for Observability Platforms, organizations spend an average of $3.2 million annually on observability tooling. Despite that investment, the same report found that only 10% of organizations rate their observability practices as "mature." The gap between spending and maturity suggests that tools alone do not solve the problem. Strategy and culture matter more.

The teams I have worked with that got observability right did not start by buying tools. They started by asking: "What questions do we need to answer when things go wrong?" and then instrumented their systems to support those questions.

The observability community has converged on three complementary data types, often called the three pillars, that together provide comprehensive system visibility.

Logs are discrete, timestamped records of events. A log entry might read: 2024-03-15T14:22:03Z INFO checkout-service user=12345 action=payment_processed amount=49.99 duration_ms=234. Logs are the most familiar data type. They are also the most expensive at scale. Datadog's 2023 State of Log Management report found that the average organization generates 1.5 TB of log data per day. Without structure and strategy, logs become a firehose that costs a fortune to store and search.

Metrics are numerical measurements collected over time. Request latency (p50, p95, p99), error rates, throughput, and resource utilization are standard metrics. Metrics are compact and efficient for identifying trends, setting alerts, and tracking SLOs. Their limitation is granularity. A metric tells you that p99 latency spiked. It does not tell you which specific requests were slow or why.

Traces follow a single request as it moves through multiple services. A trace shows that a user's checkout request hit the API gateway (12ms), called the inventory service (45ms), called the payment service (890ms), and returned a response (952ms total). Traces are essential for debugging distributed systems because they reveal where time is spent and where failures propagate.

Each pillar alone provides partial visibility. Logs give you detail without context. Metrics give you trends without specifics. Traces give you flow without depth. Effective observability connects all three, allowing you to move from a metric anomaly to a specific trace to the detailed logs of the failing component in a single investigation flow.

The observability tooling market has consolidated around a few dominant categories, each with distinct trade-offs.

Commercial platforms (Datadog, New Relic, Splunk, Dynatrace) provide integrated solutions covering all three pillars. Their strength is correlation: linking metrics anomalies to traces to logs in a unified interface. Their weakness is cost. Datadog's pricing, based on hosts and data volume, can produce monthly bills that shock teams that did not model their telemetry volume in advance. A 2024 FinOps Foundation survey found that 49% of organizations consider observability tooling their fastest-growing cloud cost category.

Open-source stacks (Grafana + Prometheus + Loki + Tempo, or the OpenTelemetry + Jaeger combination) offer flexibility and cost control. Prometheus handles metrics. Loki handles logs. Tempo or Jaeger handles traces. Grafana ties them together with dashboards and queries. The trade-off is operational overhead. You run and maintain the infrastructure yourself, and correlating data across separate backends requires more manual effort.

Cloud-native tools (AWS CloudWatch, Google Cloud Operations, Azure Monitor) integrate tightly with their respective cloud platforms. They are the easiest to get started with if you are already in that cloud. The limitation is vendor lock-in and weaker cross-cloud support. If your system spans AWS and GCP, you need a strategy for unified observability across both.

OpenTelemetry deserves special mention. It is not an observability platform but a vendor-neutral standard for instrumentation. OpenTelemetry provides libraries and APIs for generating logs, metrics, and traces in a format that works with any backend. Adoption is accelerating. The CNCF's 2024 survey found that OpenTelemetry is the second-most-active CNCF project after Kubernetes. Instrumenting with OpenTelemetry gives you the freedom to switch backends without re-instrumenting your code.

The right choice depends on your team's size, budget, and operational appetite. For teams tracking DORA metrics, the tool choice should support measuring deployment frequency, lead time, change failure rate, and mean time to recovery alongside custom business metrics.

Getting from "we have some dashboards" to "our system is observable" requires a structured approach. Here is the framework I use.

Step 1: Define your SLOs. Service Level Objectives define what "working" means for your system. "99.9% of checkout requests complete in under 2 seconds" is an SLO. SLOs give observability a purpose. Without them, you are collecting data without knowing what matters.

Step 2: Instrument at the boundaries. Start with the entry points and exit points of each service. Incoming requests, outgoing calls to other services, database queries, and external API calls. These boundaries are where latency accumulates and errors manifest. You can add internal instrumentation later, but boundary instrumentation gives you 80% of the debugging capability for 20% of the effort.

Step 3: Adopt structured logging. Unstructured log messages ("Payment failed for user") are nearly useless for debugging at scale. Structured logs with consistent fields (user_id, request_id, service_name, duration_ms, error_code) enable querying and correlation. The effort to switch from unstructured to structured logging is a one-time investment that pays dividends on every incident investigation.

Step 4: Implement distributed tracing. Propagate a trace ID across every service boundary. When a request touches five services, every log entry, metric, and span should carry the same trace ID. This single correlation mechanism transforms debugging from "search all logs and hope you find something" to "look up this trace and see the entire request path."

Step 5: Build alerts on SLOs, not metrics. Alert when your error budget is burning too fast, not when a single metric crosses a threshold. An SLO-based alert that says "at the current error rate, we will exhaust our monthly error budget in 4 hours" is more actionable than "error rate is 2%." The first tells you the urgency. The second tells you a number without context.

A well-instrumented system, integrated into a CI/CD pipeline that validates observability checks alongside code quality, catches problems before they reach production.

This is the gap most observability practices miss entirely. Your observability tools show you what is happening at runtime. They do not show you why the code behaves that way.

When an incident occurs, the investigation typically follows this path: an alert fires, an engineer checks the dashboard, identifies the affected service, reads the traces and logs, and then opens the codebase to understand the code path that produced the failure. That final step, navigating from runtime behavior to code, is where investigation time balloons.

The problem is that observability data and codebase knowledge live in completely separate systems. Your traces show that the payment service took 890ms. Your codebase shows why: the code path hits three sequential database queries, one of which scans a table that has grown 10x since the original implementation.

Connecting these two domains, runtime behavior and code structure, accelerates incident resolution and root cause analysis. Glue provides this connection by giving teams the ability to ask questions about their codebase that complement observability data. "What services call the payment processor?" or "Which code paths touch the user session table?" are questions that turn a trace anomaly into an architectural understanding.

For engineering leaders, this combination of observability data and codebase intelligence transforms post-incident reviews from "we found the bug and fixed it" to "we understand the structural pattern that created the bug and can prevent the entire class of failure." That is the difference between reactive firefighting and proactive system improvement.

Observability maturity is not binary. Teams progress through stages, and knowing where you stand helps you prioritize investments.

Level 1: Basic Monitoring. You have uptime checks, CPU/memory alerts, and application-level error rate monitoring. You know when things are down. You do not know why.

Level 2: Structured Telemetry. You have structured logs, basic metrics, and some tracing. You can investigate known failure modes. Novel failures still require adding instrumentation after the fact.

Level 3: Correlated Observability. Your logs, metrics, and traces are connected through shared identifiers. You can follow a single request across your entire system. You can answer arbitrary questions about system behavior without deploying new code.

Level 4: Proactive Observability. You use SLO-based alerting, automated anomaly detection, and codebase context to identify problems before they cause incidents. You measure observability coverage alongside test coverage.

Splunk's 2023 report found that organizations at Level 3 or above resolve incidents 69% faster and experience 2.4x fewer unplanned outages compared to organizations at Level 1 or 2. The investment in moving up the maturity ladder has direct operational and financial returns.

A useful self-assessment: how long does it take your team to answer "why did latency spike at 3 PM yesterday?" If the answer is minutes, you are at Level 3 or 4. If the answer is hours or "we cannot determine that," you are at Level 1 or 2.

Tools and instrumentation are necessary but not sufficient. Observability only works when the engineering culture supports it.

Make observability a development practice, not an ops responsibility. The engineers who write the code should instrument the code. They understand the system's semantics. They know what "healthy" looks like. When instrumentation is treated as someone else's job, it gets done poorly or not at all.

Bake instrumentation into your definition of done. A feature is not complete when the code passes tests. It is complete when the code is instrumented, has SLOs defined, and has runbook entries for common failure modes. This is a cultural shift that requires leadership support.

Conduct blameless post-incident reviews. When incidents happen, the review should focus on system behavior, not individual mistakes. "The system allowed a configuration change to propagate without validation" is a useful finding. "Bob deployed a bad config" is not. Blameless reviews encourage engineers to instrument honestly, including instrumentation that reveals their own code's failure modes.

Share observability wins. When a team resolves an incident in 5 minutes because they had the right instrumentation, celebrate it publicly. When someone adds tracing that prevents a future incident, recognize the contribution. Cultural reinforcement is how practices become habits.

For engineering leaders building observability culture, Glue adds a dimension that traditional tools miss: connecting runtime behavior to codebase structure. When your team can ask "which code owns this failing component?" and get an instant answer mapped to files, authors, and feature context, incident resolution becomes a team capability rather than a hero-engineer dependency. That shift from individual knowledge to shared system understanding is what transforms observability from a tool investment into an organizational competency.

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What is observability in simple terms?

Observability is the ability to understand what is happening inside your software system by looking at what comes out of it: logs, metrics, and traces. Think of it like a doctor diagnosing a patient. Monitoring checks vital signs against known thresholds (heart rate too high, temperature too high). Observability lets the doctor ask open-ended questions and investigate symptoms they have never seen before. In software terms, an observable system lets you debug problems without deploying new code to add more logging.

What are the three pillars of observability?

The three pillars are logs, metrics, and traces. Logs are timestamped records of discrete events (a payment was processed, an error occurred). Metrics are numerical measurements tracked over time (request latency, error rate, CPU usage). Traces follow a single request as it moves through multiple services, showing where time is spent and where failures propagate. Each pillar alone provides partial visibility. Combined and correlated through shared identifiers, they provide comprehensive system understanding.

Is observability the same as monitoring?

No. Monitoring is a subset of observability. Monitoring watches predefined metrics against predefined thresholds and alerts when something crosses a boundary. It answers "is this specific thing broken?" Observability is broader. It provides the data and tools to investigate any system behavior, including behaviors you did not anticipate. It answers "why is this system behaving this way?" Teams need both, but observability provides the exploratory capability that monitoring lacks.

What observability tools should I use?

The right tool depends on your team size, budget, and operational capacity. Commercial platforms (Datadog, New Relic, Dynatrace) provide integrated, managed solutions but can be expensive at scale. Open-source stacks (Grafana, Prometheus, Loki, Tempo) offer cost control and flexibility but require operational investment. Cloud-native tools (CloudWatch, Google Cloud Operations) are easiest if you are single-cloud. Regardless of backend, instrument with OpenTelemetry for vendor flexibility. Start with your SLOs and work backward to determine what data you need to collect.