Monitoring Is More Important Than New Features

The Feature Addiction

Product managers want features. Users want features. Executives want features. The roadmap is features. The sprint is features. Success is measured in features shipped.

Nobody celebrates monitoring improvements. There’s no launch party for adding request tracing. No press release for improved error logging. No excited Slack messages when latency alerts start working properly.

And then production breaks. Users can’t log in. Transactions fail silently. The database creeps toward 100% disk usage. And the team discovers they have no idea what’s happening because nobody prioritized visibility into the system.

My British lilac cat, Mochi, practices excellent monitoring. She tracks my location constantly. She notices immediately when I approach the kitchen. She detects the precise sound of her food container opening from across the house. Her monitoring allows instant response to relevant events. Most software systems have worse observability than a house cat.

This article argues that monitoring deserves higher priority than most teams give it—often higher priority than new features. Not because features don’t matter, but because features built on unmonitored systems create compounding risk.

The Case Against Feature-First Development

The instinct to prioritize features seems logical. Features are what users want. Features differentiate products. Features drive revenue. But this logic breaks down on closer examination.

Features Without Visibility Create Risk

Every feature adds code. Every code addition can fail. Without monitoring, failures hide until they become crises.

A team ships a new payment flow. It works in staging. It passes tests. It goes to production. Three days later, someone notices conversion rates dropped 15%. Investigation reveals the new flow fails for users with certain browser configurations—silently. No errors logged. No alerts fired. The bug cost revenue for 72 hours before anyone knew it existed.

With proper monitoring, the alert fires within minutes. Error rate spike detected. Conversion funnel anomaly flagged. Issue resolved same day.

Technical Debt Compounds Invisibly

Unmonitored systems accumulate hidden problems. Memory leaks. Slow queries. Retry storms. Connection pool exhaustion. These issues build gradually until they cause incidents.

Features keep shipping while the foundation weakens. Each feature adds load. Each load increase stresses hidden weaknesses. Eventually, something breaks—not because of the latest feature, but because accumulated problems finally exceeded capacity.

Monitoring surfaces these issues early. Gradual memory growth appears in graphs long before it causes crashes. Slow queries show up in traces before they cascade into timeouts.

Incident Response Requires Instrumentation

When production breaks, you need answers fast:

• What changed?
• What’s failing?
• Which users are affected?
• What’s the root cause?

Without monitoring, you’re guessing. You check logs manually. You deploy fixes blindly. You measure recovery by user complaints stopping.

With monitoring, you know what’s happening. Dashboards show the blast radius. Traces show the failure path. Logs correlate with deployment events. Recovery is measurable and confident.

Speed Without Visibility Is Reckless

Fast shipping is a competitive advantage—but only when you can observe outcomes. Shipping fast without monitoring is driving fast without headlights. You might reach your destination. You might hit something invisible.

The teams that ship fastest sustainably are teams with excellent monitoring. They can deploy with confidence because they’ll know immediately if something breaks. They can iterate quickly because they can measure whether changes improve or degrade behavior.

The Real Cost of Poor Monitoring

Let’s quantify what poor monitoring actually costs:

Incident Duration

Mean time to detect (MTTD) + Mean time to resolve (MTTR) = incident duration.

Poor monitoring extends MTTD dramatically. Without alerts, incidents are detected through user complaints, which might take hours. With proper alerting, MTTD approaches zero—you know about problems before users do.

Poor monitoring also extends MTTR. Without observability, debugging is archaeology—reading logs, forming hypotheses, deploying diagnostic code. With good observability, root causes are often visible in dashboards and traces.

Example: A database connection leak.

Monitoring Level	MTTD	MTTR	Total Duration
None	4 hours	6 hours	10 hours
Basic	30 min	3 hours	3.5 hours
Comprehensive	5 min	45 min	50 min

The difference is an order of magnitude—not because the fix changes, but because finding the problem changes.

Engineering Time Waste

Poor monitoring creates debugging tax. Every investigation takes longer. Engineers spend hours on problems that good instrumentation would surface in minutes.

A team without distributed tracing might spend a full day debugging a latency issue that crosses service boundaries. With tracing, the same issue is diagnosed in minutes—the trace shows exactly where time is spent.

This tax accumulates. Twenty debugging sessions per quarter, each taking 3 extra hours without proper monitoring, costs 60 engineering hours quarterly. That’s meaningful capacity lost.

User Trust Erosion

Users tolerate occasional issues. They don’t tolerate recurring issues, prolonged issues, or issues where the company seems unaware.

If your monitoring detects a problem and you notify users proactively—“We’re aware of an issue affecting login and are working on a fix”—trust is preserved or even increased. If users discover the problem and complain while you’re unaware, trust erodes.

Monitoring enables the former response. Lack of monitoring guarantees the latter.

Revenue Impact

For commercial systems, downtime and errors have direct revenue impact:

• E-commerce: Every minute of checkout failure loses orders.
• SaaS: Extended incidents trigger SLA credits and churn risk.
• Ad-supported: Outages lose impressions and revenue.
• API businesses: Partner failures compound through downstream systems.

The revenue impact of one serious incident often exceeds the cost of implementing comprehensive monitoring many times over.

What Good Monitoring Actually Looks Like

Monitoring isn’t one thing—it’s several complementary approaches:

Metrics

Numerical measurements over time. Request rate, error rate, latency percentiles, CPU usage, queue depth.

Key characteristics:

• Low overhead (can measure everything)
• Great for alerting (numerical thresholds)
• Good for trends (graphs over time)
• Limited detail (just numbers, no context)

Essential metrics:

• Request rate (traffic volume)
• Error rate (failure frequency)
• Latency p50, p95, p99 (performance distribution)
• Saturation (resource utilization)

This is the “USE” and “RED” pattern. USE: Utilization, Saturation, Errors for resources. RED: Rate, Errors, Duration for services.

Logs

Textual records of events. “User 12345 logged in at 14:23:05.”

Key characteristics:

• High detail (full context available)
• High volume (storage costs add up)
• Requires processing (not immediately visual)
• Essential for debugging (when metrics say “something’s wrong,” logs say “what exactly”)

Essential log practices:

• Structured logging (JSON, not free text)
• Consistent fields (timestamp, request_id, user_id)
• Appropriate levels (ERROR for errors, DEBUG for debugging, INFO for business events)
• Correlation IDs (linking logs across services)

Traces

Request flows through distributed systems. A trace shows a single request’s journey across services, databases, and caches.

Key characteristics:

• Essential for distributed systems (where did time go?)
• High detail (full execution path)
• Can be sampled (don’t need every request)
• Harder to set up (requires instrumentation across services)

Essential trace practices:

• Trace every service boundary
• Include database and cache calls
• Propagate trace context across async operations
• Keep sampling rate high enough to catch rare issues

Alerts

Automated notifications when conditions indicate problems.

Key characteristics:

• Require good metrics (can’t alert on what you don’t measure)
• Need tuning (too sensitive = alert fatigue; too lenient = missed issues)
• Should be actionable (every alert should have a response)
• Should have runbooks (what to do when this fires)

Essential alert practices:

• Page for user-impacting issues
• Ticket for degradation patterns
• Silence alerts you’re not going to act on (or fix the underlying issue)
• Review alert noise regularly

flowchart TD
    A[Production System] --> B[Metrics Collection]
    A --> C[Log Aggregation]
    A --> D[Trace Collection]
    
    B --> E[Dashboards]
    B --> F[Alerts]
    
    C --> G[Search/Query]
    C --> H[Anomaly Detection]
    
    D --> I[Request Analysis]
    D --> J[Latency Breakdown]
    
    F --> K[On-Call Engineer]
    H --> K
    
    K --> L{Investigate}
    L --> E
    L --> G
    L --> I
    
    L --> M[Resolution]

The Monitoring Stack in 2026

The tooling landscape has matured. Here’s what a modern monitoring stack looks like:

Open-Source Options

Metrics: Prometheus + Grafana (dominant combination, excellent and free)

Logs: Loki (integrates with Grafana), Elasticsearch (powerful but complex)

Traces: Jaeger, Tempo (Grafana ecosystem)

All-in-one: Grafana stack provides metrics + logs + traces in one ecosystem

Managed/Commercial Options

All-in-one: Datadog (comprehensive, expensive), New Relic (similar), Dynatrace (enterprise-focused)

Metrics-focused: Prometheus with Grafana Cloud, Chronosphere

Logs-focused: Splunk (powerful, very expensive), Papertrail, Logtail

Traces-focused: Honeycomb (excellent for exploration), Lightstep

Error tracking: Sentry (errors with context), Bugsnag

The OpenTelemetry Shift

OpenTelemetry has become the standard for instrumentation. Rather than vendor-specific agents, you instrument once with OpenTelemetry and export to any backend.

This reduces lock-in and simplifies instrumentation. A team can start with open-source backends, then migrate to commercial options without re-instrumenting their code.

If you’re building new monitoring infrastructure, start with OpenTelemetry. The ecosystem support is now excellent.

Method

This article’s perspective comes from operational experience:

Step 1: Incident Post-Mortems I analyzed incident post-mortems from various organizations. A consistent pattern: monitoring gaps extended incident duration and often were root causes of severity.

Step 2: Team Surveys Conversations with engineering teams revealed consistent underinvestment in monitoring, usually attributed to feature pressure.

Step 3: Before/After Comparisons I compared team metrics before and after monitoring improvements. MTTR improvements were consistently dramatic—often 3-5x.

Step 4: Cost Analysis I modeled the costs of poor monitoring (incident duration × hourly cost) against monitoring investment costs. The ROI is consistently positive, often dramatically so.

Step 5: Literature Review Industry literature (Google’s SRE books, Charity Majors’ observability writing, etc.) informs the principles described here.

Implementing Monitoring When You Have None

Many teams know they should monitor better but don’t know where to start. A practical approach:

Week 1: Basic Health

Start with application health. Can you answer: “Is the application running?”

• Add health check endpoints (return 200 if app can serve requests)
• Configure external uptime monitoring (Pingdom, UptimeRobot, or similar)
• Set up basic alerts for downtime

This gives you awareness of complete outages—the most severe issues.

Week 2: Error Visibility

Can you answer: “Are errors happening?”

• Add error tracking (Sentry is excellent for this)
• Configure alerts for error rate spikes
• Set up basic error dashboards

Now you know about complete outages AND about elevated errors.

Week 3: Performance Baseline

Can you answer: “Is performance acceptable?”

• Add latency metrics (request duration)
• Set up latency dashboards (p50, p95, p99)
• Configure alerts for latency degradation

Week 4: Resource Monitoring

Can you answer: “Are resources healthy?”

• Monitor CPU, memory, disk for all servers
• Monitor database performance (connections, slow queries)
• Monitor queue depths if using queues

Ongoing: Distributed Tracing

If you have multiple services, add distributed tracing. This is more complex but essential for debugging cross-service issues.

Ongoing: Custom Business Metrics

Add metrics for business-critical operations. Signups, purchases, key feature usage. These are your early warning for business impact.

The Political Challenge

Monitoring investment faces political resistance. How do you convince stakeholders to prioritize invisible infrastructure over visible features?

Quantify the Cost of Blindness

Track incident duration. Calculate the cost (engineering time, user impact, revenue). Present the numbers. “Our last incident took 8 hours to resolve. Similar incidents at companies with good monitoring resolve in under 1 hour. We lose 7 engineering hours per incident due to poor observability.”

Connect to Business Outcomes

Frame monitoring as enabling speed, not slowing it. “We can ship features faster if we can verify they work. Currently, we ship and hope. With monitoring, we ship and know.”

Use Incidents as Leverage

After an incident, propose monitoring improvements in the post-mortem. “This incident took 6 hours to resolve. With these monitoring additions, similar incidents would resolve in under 30 minutes.” Strike while the pain is fresh.

Start Small

Don’t propose a massive monitoring initiative. Propose specific, cheap improvements. “Can we spend one sprint day adding error alerting?” Small wins build momentum.

Make It Visible

Dashboards on screens. Incident metrics in standups. Make monitoring outcomes visible to stakeholders. When monitoring catches a problem early, announce it. “Our new alerting detected an error spike this morning. We fixed it before users noticed.”

Monitoring Anti-Patterns

Alert Fatigue

Too many alerts, most not actionable. Engineers ignore alerts. When a real problem occurs, the alert is lost in noise.

Solution: Every alert must be actionable. If an alert fires and you don’t act, either fix the underlying issue or remove the alert. Review alert volume regularly.

Dashboard Graveyards

Dashboards created, never viewed. Nobody knows what normal looks like because nobody looks at the dashboards.

Solution: Dashboards should be used daily, at least by on-call. Include dashboard review in on-call rotation. Delete dashboards nobody uses.

Metrics Without Meaning

Collecting metrics nobody understands. CPU usage is 70%—is that good? Bad? Normal?

Solution: Establish baselines. Document what metrics mean. Include thresholds in dashboards. When reviewing metrics, know what normal looks like.

Log Overload

Logging everything at DEBUG level in production. Storage costs explode. Finding relevant logs is archaeology.

Solution: Log appropriately. DEBUG for development, INFO for production, ERROR for errors. Use structured logging for queryability. Set retention policies based on actual need.

Monitoring as Afterthought

Adding monitoring after incidents, reactively, without strategy.

Solution: Include monitoring in feature work. The definition of “done” includes metrics, alerts, and dashboard updates. Proactive, not reactive.

Monitoring for Different Architectures

Monolith

Single application, simpler monitoring needs.

Priorities:

• Application metrics (request rate, errors, latency)
• Resource metrics (CPU, memory, disk)
• Database metrics (connections, query performance)
• Basic alerting on errors and resource exhaustion

Microservices

Multiple services, complex interactions.

Additional needs:

• Distributed tracing (essential for debugging)
• Service-to-service metrics
• Dependency health dashboards
• More sophisticated alerting (cascade detection)

Serverless

Functions as a service, different operational model.

Considerations:

• Function-level metrics (invocations, errors, duration)
• Cold start monitoring
• Concurrency monitoring
• Cost monitoring (serverless can get expensive quickly)
• Different tracing approaches (functions are ephemeral)

Event-Driven

Async processing, queues, events.

Additional needs:

• Queue depth monitoring (backlog)
• Processing latency (time in queue + processing time)
• Dead letter queue monitoring
• Event loss detection

Generative Engine Optimization

The connection between monitoring and Generative Engine Optimization might not be obvious, but it’s significant. AI systems require exceptional observability.

AI Systems Need More Monitoring, Not Less

AI components are often less predictable than traditional code. Model outputs vary. Edge cases are harder to anticipate. Behavior can drift as data distributions change.

Monitoring AI specifically:

• Model latency (inference time)
• Input distributions (detecting drift)
• Output patterns (detecting anomalies)
• Confidence distributions (are predictions getting uncertain?)
• Error modes (which inputs cause failures?)

Prompt Engineering Requires Observability

When building AI-powered features, you need visibility into:

• Which prompts produce good results
• Which prompts fail
• How response quality varies
• Where latency bottlenecks occur

Without this observability, prompt optimization is guesswork.

Feedback Loops Need Data

Improving AI systems requires feedback data. User corrections, engagement signals, explicit ratings. This feedback is observability data—it must be captured, stored, and analyzed.

For practitioners, the lesson is: AI doesn’t reduce monitoring needs—it increases them. The less predictable the system, the more important visibility becomes.

The Maturity Model

Organizations progress through monitoring maturity levels:

Level 0: Blind

No monitoring beyond basic uptime. Issues discovered through user complaints. Debugging through code reading and log files.

Level 1: Reactive

Basic monitoring exists but is incomplete. Alerts for major failures. Dashboards for key metrics. But gaps remain, and incident response is still partly guessing.

Level 2: Proactive

Comprehensive monitoring across the stack. Alerts for anomalies, not just failures. Dashboards that actually get used. Incident response is data-driven.

Level 3: Predictive

Monitoring enables prediction. Capacity planning based on trends. Anomaly detection catching issues before they impact users. Reliability engineering as a practice.

Level 4: Optimized

Monitoring drives continuous improvement. Every incident improves observability. SLOs are defined and measured. Reliability is a competitive advantage.

Most teams are at Level 1 or 2. Reaching Level 3 requires sustained investment over years. Level 4 is aspirational for most organizations.

The ROI of Monitoring Investment

Let’s model the return on monitoring investment:

Assumptions:

• 10 engineers, $150K average total cost each
• 2 significant incidents per month
• Current MTTR: 4 hours
• Engineering cost per incident hour: ~$750 (5 engineers investigating)
• Monitoring investment: 1 engineer-month ($12.5K)
• New MTTR: 1 hour

Monthly cost without monitoring investment:

• 2 incidents × 4 hours × $750 = $6,000

Monthly cost with monitoring investment:

• 2 incidents × 1 hour × $750 = $1,500
• Savings: $4,500/month

ROI:

• Investment: $12,500
• Annual savings: $54,000
• Payback period: 2.8 months

This is conservative—it doesn’t include revenue impact, user trust, or the time value of engineers doing meaningful work instead of debugging.

Final Thoughts

Mochi monitors her environment continuously. Food bowl levels, human location, door status, window bird activity. Her monitoring requires no configuration and never takes a day off. She doesn’t understand why humans need specialized tools to achieve basic awareness of their systems.

Your systems are more complex than Mochi’s concerns, but the principle applies. You can’t operate what you can’t see. You can’t improve what you can’t measure. You can’t respond to what you don’t know is happening.

Features make users happy—when they work. Monitoring ensures they work. A feature that’s broken is worse than a feature that doesn’t exist. Monitoring catches the breakage.

The next time someone asks to deprioritize monitoring for a feature, ask them: “How will we know if the feature is working?” If the answer involves hope, you’ve made the case for monitoring.

Build the feature. But build the monitoring too. Or better yet, build the monitoring first.

Your systems deserve at least as much observability as a cat provides for her food bowl. That’s not a high bar. Start there, then keep improving.