No Observability

What This Looks Like

The team deploys a change. Someone asks “is it working?” Nobody knows. There is no dashboard to check. There are no metrics to compare before and after. The team waits. If nobody complains within an hour, they assume the deployment was successful.

When something does go wrong, the team finds out from a customer support ticket, a Slack message from another team, or an executive asking why the site is slow. The investigation starts with SSH-ing into a server and reading raw log files. Hours pass before anyone understands what happened, what caused it, or how many users were affected.

Common variations:

• Logs exist but are not aggregated. Each server writes its own log files. Debugging requires logging into multiple servers and running grep. Correlating a request across services means opening terminals to five machines and searching by timestamp.
• Metrics exist but nobody watches them. A monitoring tool was set up once. It has default dashboards for CPU and memory. Nobody configured application-level metrics. The dashboards show that servers are running, not whether the application is working.
• Alerting is all or nothing. Either there are no alerts, or there are hundreds of noisy alerts that the team ignores. Real problems are indistinguishable from false alarms. The on-call person mutes their phone.
• Observability is someone else’s job. A separate operations or platform team owns the monitoring tools. The development team does not have access, does not know what is monitored, and does not add instrumentation to their code.
• Post-deployment verification is manual. After every deployment, someone clicks through the application to check if it works. This takes 15 minutes per deployment. It catches obvious failures but misses performance degradation, error rate increases, and partial outages.

The telltale sign: the team’s primary method for detecting production problems is waiting for someone outside the team to report them.

Why This Is a Problem

Without observability, the team is deploying into a void. They cannot verify that deployments are healthy, cannot detect problems quickly, and cannot diagnose issues when they arise. Every deployment is a bet that nothing will go wrong, with no way to check.

It reduces quality

When the team cannot see the effects of their changes in production, they cannot learn from them. A deployment that degrades response times by 200 milliseconds goes unnoticed. A change that causes a 2% increase in error rates is invisible. These small quality regressions accumulate because nobody can see them.

Without production telemetry, the team also loses the most valuable feedback loop: how the software actually behaves under real load with real data. A test suite can verify logic, but only production observability reveals performance characteristics, usage patterns, and failure modes that tests cannot simulate.

Teams with strong observability catch regressions within minutes of deployment. They see error rate spikes, latency increases, and anomalous behavior in real time. They roll back or fix the issue before most users are affected. Quality improves because the feedback loop from deployment to detection is minutes, not days.

It increases rework

Without observability, incidents take longer to detect, longer to diagnose, and longer to resolve. Each phase of the incident lifecycle is extended because the team is working blind.

Detection takes hours or days instead of minutes because the team relies on external reports. Diagnosis takes hours instead of minutes because there are no traces, no correlated logs, and no metrics to narrow the search. The team resorts to reading code and guessing. Resolution takes longer because without metrics, the team cannot verify that their fix actually worked - they deploy the fix and wait to see if the complaints stop.

A team with observability detects problems in minutes through automated alerts, diagnoses them in minutes by following traces and examining metrics, and verifies fixes instantly by watching dashboards. The total incident lifecycle drops from hours to minutes.

It makes delivery timelines unpredictable

Without observability, the team cannot assess deployment risk. They do not know the current error rate, the baseline response time, or the system’s capacity. Every deployment might trigger an incident that consumes the rest of the day, or it might go smoothly. The team cannot predict which.

This uncertainty makes the team cautious. They deploy less frequently because each deployment is a potential fire. They avoid deploying on Fridays, before holidays, or before important events. They batch up changes so there are fewer risky deployment moments. Each of these behaviors slows delivery and increases batch size, which increases risk further.

Teams with observability deploy with confidence because they can verify health immediately. A deployment that causes a problem is detected and rolled back in minutes. The blast radius is small because the team catches issues before they spread. This confidence enables frequent deployment, which keeps batch sizes small, which reduces risk.

Impact on continuous delivery

Continuous delivery requires fast feedback from production. The deploy-and-verify cycle must be fast enough that the team can deploy many times per day with confidence. Without observability, there is no verification step - only hope.

Specifically, CD requires:

• Automated deployment verification. After every deployment, the pipeline must verify that the new version is healthy before routing traffic to it. This requires health checks, metric comparisons, and automated rollback triggers - all of which require observability.
• Fast incident detection. If a deployment causes a problem, the team must know within minutes, not hours. Automated alerts based on error rates, latency, and business metrics are essential.
• Confident rollback decisions. When a deployment looks unhealthy, the team must be able to compare current metrics to the baseline and make a data-driven rollback decision. Without metrics, rollback decisions are based on gut feeling and anecdote.

A team without observability can automate deployment, but they cannot automate verification. That means every deployment requires manual checking, which caps deployment frequency at whatever pace the team can manually verify.

How to Fix It

Step 1: Add structured logging (Week 1)

Structured logging is the foundation of observability. Without it, logs are unreadable at scale.

1. Replace unstructured log statements (log("processing order")) with structured ones (log(event="order.processed", order_id=123, duration_ms=45)).
2. Include a correlation ID in every log entry so that all log entries for a single request can be linked together across services.
3. Send logs to a central aggregation service (Elasticsearch, Datadog, CloudWatch, Loki, or similar). Stop relying on SSH and grep.

Focus on the most critical code paths first: request handling, error paths, and external service calls. You do not need to instrument everything in week one.

Step 2: Add application-level metrics (Week 2)

Infrastructure metrics (CPU, memory, disk) tell you the servers are running. Application metrics tell you the software is working. Add the four golden signals:

Expose these metrics through your application (using Prometheus client libraries, StatsD, or your platform’s metric SDK) and visualize them on a dashboard.

Step 3: Create a deployment health dashboard (Week 3)

Build a single dashboard that answers: “Is the system healthy right now?”

1. Include the four golden signals from Step 2.
2. Add deployment markers so the team can see when deploys happened and correlate them with metric changes.
3. Include business metrics that matter: successful checkouts per minute, sign-ups per hour, or whatever your system’s key transactions are.

This dashboard becomes the first thing the team checks after every deployment. It replaces the manual click-through verification.

Step 4: Add automated alerts for deployment verification (Week 4)

Move from “someone checks the dashboard” to “the system tells us when something is wrong”:

1. Set alert thresholds based on your baseline metrics. If the p95 latency is normally 200ms, alert when it exceeds 500ms for more than 2 minutes.
2. Set error rate alerts. If the error rate is normally below 1%, alert when it crosses 5%.
3. Connect alerts to the team’s communication channel (Slack, PagerDuty, or similar). Alerts must reach the people who can act on them.

Start with a small number of high-confidence alerts. Three alerts that fire reliably are worth more than thirty that the team ignores.

Step 5: Integrate observability into the deployment pipeline (Week 5+)

Close the loop between deployment and verification:

1. After deploying, the pipeline waits and checks health metrics automatically. If error rates spike or latency degrades beyond the threshold, the pipeline triggers an automatic rollback.
2. Add smoke tests that run against the live deployment and report results to the dashboard.
3. Implement canary deployments or progressive rollouts that route a small percentage of traffic to the new version and compare its metrics against the baseline before promoting.

This is the point where observability enables continuous delivery. The pipeline can deploy with confidence because it can verify health automatically.

Measuring Progress

Related Content

• Pipeline Architecture - Where deployment verification fits in the pipeline
• Rollback - Observability enables data-driven rollback decisions
• Progressive Rollout - Canary deployments require metric comparison
• Metrics-Driven Improvement - Using production data to guide improvement
• Baseline Metrics - Establishing the numbers you need before you can improve them