Logs vs. Metrics vs. Traces: How PepsiCo Cut Incident Resolution Time by 30%

Logs vs. Metrics vs. Traces: How PepsiCo Cut Incident Resolution Time by 30%

PepsiCo was running over 55 separate observability tools across its digital infrastructure, logs in one place, metrics in another, traces scattered across a third, with no single view connecting them. When something broke, engineers spent real time just stitching the story together before they could even start fixing it. After consolidating metrics, logs, and traces into one unified platform, PepsiCo cut its mean time to resolution by 30%, reduced its tool count from 55 to under 20, and saved up to 25% in annual hardware costs along the way.

That number didn't come from a smarter alerting rule or a faster server. It came from treating logs, metrics, and traces as three parts of one system instead of three separate problems. Most engineers learn each of these signals in isolation, if they learn traces at all, and never build a clear mental model of what each one is actually for. This guide breaks down what each signal tells you, where each one falls short on its own, and how real teams wire them together into something that actually shortens an incident.

If observability concepts are still new ground, our Observability course covers the fundamentals this comparison builds on.

This post originally appeared on the Ciphemic Academia blog.

The Short Version

• Logs are detailed, timestamped records of discrete events, the richest source of context, but the hardest to search at scale without structure.
• Metrics are numeric measurements aggregated over time (CPU usage, request count, error rate), cheap to store and great for spotting that something is wrong, fast.
• Traces follow a single request as it travels through a distributed system, showing exactly where time was spent across every service it touched.

If you want one default: you need all three, not one. They answer different questions, and real observability platforms, including the one behind PepsiCo's 30% improvement, exist specifically to connect them, not to replace one with another.

What All Three Are Actually Trying to Solve

Before the differences, the shared goal, since it's easy to lose sight of when people argue "logs vs. metrics" as if it's a single choice:

• Detecting that something is wrong: all three can surface an anomaly, though some do it faster than others
• Diagnosing why it's wrong: all three contribute context, but at very different levels of detail and cost
• Reconstructing what happened: after an incident, you need to explain the timeline, and each signal captures a different slice of that timeline
• Doing this at scale without drowning the team: raw data from any of the three, unfiltered, becomes noise instead of signal past a certain volume

The real skill isn't picking a favorite. It's knowing which signal answers which question, and building a system where they connect.

Logs

What it actually is: a timestamped, detailed record of a discrete event, a request came in, an error was thrown, a job finished, usually written as structured or semi-structured text by your application code directly.

A small example:

2026-01-15T09:42:11Z ERROR order-service: Failed to charge card for order=482, customer=c91, reason=insufficient_funds

Where it shines:

• Maximum detail: logs capture exactly what happened, in plain language, including context a metric or trace alone would never show
• Flexible and ad hoc: you can log anything, at any point in your code, without planning a schema in advance
• Essential for root-cause investigation: once you know roughly where a problem is, logs usually hold the specific detail that explains why

Where it struggles:

• Expensive at scale: storing and indexing every log line from a high-traffic system gets costly fast, which is exactly the kind of overhead PepsiCo's consolidation was fighting
• Slow to search without structure: unstructured, free-text logs are genuinely hard to query efficiently across millions of lines
• No inherent sense of "normal": a log line doesn't tell you whether an error rate is unusual, just that one error happened

Who this suits: deep, specific investigation once you already have a rough idea of where to look, and the layer most teams reach for last, not first, during an incident.

Metrics

What it actually is: numeric measurements, aggregated over time, that summarize system behavior: requests per second, average latency, error rate, CPU usage, memory consumption. Metrics are typically sampled or counted at regular intervals and stored efficiently as time-series data.

A small example:

http_requests_total{service="order-service", status="500"} 142
http_request_duration_seconds{service="order-service", quantile="0.99"} 1.8

Where it shines:

• Cheap to store and query at scale: a single number per time interval is far lighter than a full log line or trace, which is part of why metrics are the natural backbone of dashboards and alerts
• Great for detecting anomalies fast: a sudden spike in error rate or latency is immediately visible on a metric dashboard, often before a single customer complains
• Natural fit for alerting: thresholds on metrics (error rate above 5%, latency above 2 seconds) are the standard way teams get paged in the first place

Where it struggles:

• No individual context: a metric tells you error rate spiked to 12%, but nothing about which specific requests failed or why
• Aggregation can hide real problems: an average latency metric can look fine while a specific subset of requests is badly broken, a well-known blind spot in metrics-only monitoring
• Doesn't explain causation on its own: metrics are excellent at telling you that something changed, and far weaker at telling you why without other signals alongside them

Who this suits: the first layer of detection in almost every incident, dashboards, alerting, and getting a fast, high-level read on system health.

Traces

What it actually is: a record of a single request's journey through a distributed system, broken into "spans," each representing the time spent in one service or operation. A trace shows you the full path, including exactly where time was spent, across every microservice the request touched.

A small example (a simplified trace):

Trace: GET /checkout (total: 842ms)
 ├─ auth-service        (12ms)
 ├─ order-service        (430ms)
 │    └─ payment-service (390ms)  ← most of the time is here
 └─ notification-service (25ms)

Where it shines:

• Pinpoints exactly where time went: in a system with a dozen microservices, a trace tells you precisely which one is the bottleneck, something logs and metrics alone genuinely struggle to show
• Reveals the shape of distributed systems: traces expose real dependency chains and latency sources that can be invisible from any single service's perspective
• Essential for microservice debugging: as the Irdeto case study on reducing MTTR put it, going "from logs to traces" was specifically what let their team stop guessing and start seeing the actual request path

Where it struggles:

• More setup required: tracing needs instrumentation across every service a request touches, which is real upfront engineering work compared to logs or metrics
• Sampling trade-offs: at high volume, many systems sample only a fraction of traces to control cost, risking missing the exact trace that mattered
• Less useful in isolation: a trace tells you where time was spent, but you'll often still want a log from that specific span to understand exactly why

Who this suits: microservice and distributed-system debugging specifically, and the layer that turns "something in our system is slow" into "this exact service call is slow, and here's why."

Side-by-Side Comparison

How This Fits a Career Path

• Backend Engineer (general): structured logging is close to a baseline expectation, and understanding what a metric dashboard is actually telling you is assumed in nearly every on-call rotation
• SRE or platform engineer: this is close to the center of the job; our Observability course covers building the instrumentation and dashboards that make incidents like PepsiCo's resolvable in minutes instead of hours
• Microservices or distributed-systems engineer: tracing becomes non-negotiable once a request crosses more than two or three services, since logs and metrics alone genuinely can't show you the path
• System design interviews: be ready to explain how you'd instrument a new service with all three signals, and why one alone wouldn't be enough, a common way interviewers probe real operational experience

How to Choose Without Overthinking It

1. Don't choose. Layer them. Metrics tell you something's wrong fast. Traces show you where. Logs tell you exactly why. Removing any one layer leaves a real gap, which is precisely what PepsiCo's fragmented 55-tool setup was suffering from before consolidation.
2. Start with metrics and alerting if you have nothing yet. They're the cheapest to implement and give you the fastest path to knowing something broke.
3. Add tracing once you have more than a couple of services talking to each other. A single-service app rarely needs it; a system with five or more collaborating services genuinely does.
4. Invest in structured logging early, not as an afterthought. Structured logs (consistent fields, not free text) are dramatically easier to search and correlate with traces and metrics later.

A note on honesty: the specific improvement numbers above, PepsiCo's 30%, Irdeto's reported MTTR drop, redBus's 50% MTTR reduction, all came from consolidating and correlating signals, not from adopting any single one of the three. The lesson isn't "traces are the best signal." It's that disconnected tools, even good ones, cost real time during an incident.

Common Mistakes When Learning Observability

• Treating this as "pick one." Logs, metrics, and traces answer genuinely different questions; a team that only has logs, or only has metrics, is missing real diagnostic capability, not just a nice-to-have.
• Logging everything without structure. Free-text logs at scale become expensive and hard to search; structured logging pays off the moment you need to correlate a log with a trace.
• Adding tracing too late. Retrofitting distributed tracing onto an already-complex microservices system is real work; instrumenting from early on is far cheaper than doing it under pressure during an incident.
• Alerting on raw metrics without context. A threshold alert with no linked trace or log tells you something's wrong but forces engineers to start the investigation from zero every time.
• Ignoring tool sprawl. PepsiCo's starting point, 55 separate tools, is a cautionary example: more observability tools doesn't automatically mean better observability if they don't talk to each other.

Frequently Asked Questions

Should a beginner learn logs, metrics, or traces first?

Logs, since nearly every application already produces them and they require the least new infrastructure to start with. Move to metrics and alerting next, then traces once you're working with more than a couple of interacting services.

Do I need all three for a small application?

Not urgently. A small, single-service application can get a long way with good logging and a handful of key metrics. Tracing earns its place specifically once requests start crossing multiple services.

What's the difference between monitoring and observability?

Monitoring typically refers to watching known metrics and alerting on them. Observability is the broader ability to ask new questions about your system's behavior after the fact, which is exactly why logs, metrics, and traces working together, rather than in isolation, define the term.

Why did PepsiCo's MTTR improve by consolidating tools rather than adding new ones?

Because the bottleneck wasn't a lack of data, it was disconnected data. Engineers were manually correlating signals across 55 separate tools; a unified platform let them move from symptom to root cause in one workflow instead of many.

Is distributed tracing only useful for microservices?

It's most valuable there, but even a handful of services calling each other (an API calling a database and a third-party service) can benefit from tracing to see where latency actually originates.

How do interviewers evaluate this topic?

Less about definitions, more about whether you can describe a real incident-response workflow: which signal you'd check first, how you'd narrow down from "something's wrong" to a specific root cause, and why no single signal would have been enough alone.

Build Observability That Actually Shortens Incidents

The real lesson from PepsiCo's 30% improvement isn't which signal to prioritize, it's that logs, metrics, and traces only pay off fully when they're connected. Explore the Observability course to learn how to instrument, correlate, and actually use all three signals together, not just collect them.