Most teams buy complexity before they buy clarity
The observability conversation usually starts in the wrong place.
A team launches an API, adds a worker, maybe plugs in Redis, and the first question becomes: “Should we set up logs, metrics, or traces?” The more honest question is simpler: when production feels wrong at 2:13 a.m., which signal will actually help you recover first?
My view is not fashionable, but it is practical: most small teams should start with metrics first, add structured logs second, and only invest seriously in traces when the request path has become distributed enough to justify the extra instrumentation, storage, and mental overhead.
That is not an argument against tracing. Tracing is powerful. In some architectures, it becomes essential. But the industry has a bad habit of turning “all three matter” into “you need all three immediately.” Those are not the same statement.
At Raff, when we think about default operability for teams running on VMs, we bias toward telemetry that does three things well: it tells you something is wrong early, it narrows the blast radius quickly, and it stays understandable to a lean team without a dedicated observability engineer. That bias changes the order.
What each signal is actually good at
Before talking about order, it helps to strip the jargon out of the terms.
Metrics tell you that something is changing over time. CPU climbs. Memory gets tight. Error rate jumps. Queue depth grows. P95 latency drifts upward. Metrics are compact, cheap to aggregate, and ideal for dashboards and alerts.
Logs tell you what happened at a specific moment. A user failed authentication. A database connection timed out. A deploy changed an environment variable. A payment provider returned an unexpected response. Logs are rich, descriptive, and often messy. They explain events in detail, but they can drown you if you treat them like a primary alerting system.
Traces tell you where time was spent across a request path. A trace follows a single transaction as it moves through your application and dependencies. It becomes especially useful when one request touches multiple services, queues, databases, and third-party APIs.
That distinction matters because these signals solve different problems.
Metrics answer: Is the system healthy?
Logs answer: What happened?
Traces answer: Where in the path did it break or slow down?
If you try to use one signal for every question, you end up with a noisy stack and very little clarity.
Why metrics should usually come first
If I had to choose one signal for a small team starting from zero, I would choose metrics without hesitation.
The reason is simple: metrics are the fastest route from “something feels off” to “this area needs attention.” They are lightweight enough to collect continuously, structured enough to alert on automatically, and broad enough to describe system health before you know the exact failure mode.
That makes them your best first line of defense.
A small team usually needs answers to a short list of questions before anything more advanced:
- Is the server under resource pressure?
- Is the application error rate rising?
- Is request latency trending up?
- Is the queue backing up?
- Is the database responding more slowly than normal?
- Did a deploy correlate with a sudden change?
Metrics answer those questions cleanly. They also create the habit that matters most in operations: defining what “normal” looks like before something breaks.
This is where a lot of teams go wrong. They install logging, skim a wall of text, and call that observability. It is not. It is exhaust.
Without metrics, you are waiting to notice a problem reactively. With metrics, you can detect it proactively. That difference matters more than people think, especially when your infrastructure is still simple.
If your application is running on one or two Linux VMs, one database, and a worker process, good metrics already cover a surprising amount of ground. CPU saturation, memory pressure, disk I/O, error rate, response time, queue depth, and restart frequency will tell you where to look long before a tracing system becomes necessary.
Why logs come second — but not as raw noise
Logs are the second layer because once metrics tell you where to look, logs usually tell you why.
But there is a catch: logs only become useful at scale when they are structured and intentional.
A lot of teams say they “have logs” when what they actually have is a terminal full of inconsistent strings written by different developers in different moods over six months. That is not observability. That is archaeology.
If you want logs to work for you, you need to make them queryable.
That means logging in a structured format, usually JSON, and attaching a consistent set of fields such as:
- timestamp
- level
- service
- environment
- version
- request ID
- correlation ID
- job ID where relevant
Once you do that, logs stop being random narrative and start becoming evidence.
This is also why I put logs after metrics, not before. Logs are rich, but they are expensive in every sense: storage, cardinality, indexing, retention, and human attention. They scale badly when your first question is “Is the system healthy?” They scale well when your first question is “What exactly happened inside the component metrics just pointed me toward?”
That is the right relationship.
Metrics tell you the API error rate doubled after the deploy. Logs tell you the payment adapter is throwing a null reference only in the eu-west environment. Metrics tell you queue latency spiked. Logs tell you one worker version is failing to deserialize a job payload. Metrics tell you the database is slow. Logs show the ORM suddenly issuing an N+1 query pattern.
If metrics are your radar, logs are your incident notebook.
When traces become worth the effort
Now the controversial part.
Tracing is powerful, but for many small teams it is not the first missing piece. It is the third one.
If your system is still mostly one application, one worker tier, and one database, traces can absolutely help — but they are often not the thing standing between you and operational clarity. In that stage, poor metrics and poor logs are usually the bigger problem.
Tracing becomes worth the effort when the system stops being legible as a single unit.
That usually happens when requests begin crossing multiple boundaries:
- service-to-service network calls
- async queues and background jobs
- third-party APIs with variable latency
- separate ownership across domains
- retries, fallbacks, and partial failure paths
- multiple databases or datastores in one request lifecycle
That is when traces stop being nice to have and start becoming operationally important.
The reason is not just that traces are “cooler” or more modern. It is that distributed systems create failure paths that logs and metrics alone explain poorly. Once one user action fans out across multiple services, you need request-level visibility to understand where time went and which dependency introduced failure or latency.
This is why tracing shines in microservices, queue-heavy applications, and integration-heavy systems. It is also why I do not recommend forcing it too early onto every small app. You end up instrumenting for a topology you do not actually have yet.
There is another practical point here: tracing gets much better when it is correlated with logs. Once trace IDs, service names, environment tags, and version fields are shared consistently, traces and logs stop competing and start reinforcing each other. Until then, teams often blame tracing when the real problem is inconsistent telemetry design.
The order I recommend for small teams on Raff
If you are running a lean application on Raff today, this is the sequence I would recommend.
1. Start with infrastructure and application metrics
Get basic host and app health visible first.
Track CPU, RAM, disk, network, error rate, response latency, restarts, queue depth, and database latency if you can. Build a small dashboard. Add a few alerts that you will actually respect. Not fifty. A few.
The goal is not “full observability.” The goal is knowing when the system moves outside normal behavior.
2. Add structured logs with correlation IDs
Once metrics tell you where pain is appearing, structured logs let you investigate without guessing.
Make logs machine-readable. Add request IDs. Make sure deploy version and environment are included. Keep log levels disciplined. Do not dump every debug detail into production forever and call that maturity.
3. Add traces when request paths earn them
When the system starts crossing enough boundaries that cause-and-effect becomes blurry, add tracing deliberately.
Not because a vendor told you the stack is incomplete without it. Because you now have a real problem tracing is good at solving.
4. Correlate the signals instead of treating them as separate products
This is the maturity step that matters. Metrics should lead you to the incident. Logs should explain the event. Traces should expose the path. If they do not connect cleanly, you do not really have observability yet. You just have three storage systems.
The mistake behind most observability waste
Most waste in observability does not come from buying the wrong tool.
It comes from solving the wrong stage problem.
A team with one service, one queue, and one database buys a tracing-first stack because it sounds advanced. Meanwhile, they still have no reliable latency alert, no deploy markers in dashboards, no structured error logs, and no agreement on what “healthy” means. That team does not need more telemetry types. It needs clearer fundamentals.
This is the same pattern we see in infrastructure more broadly. Teams often jump to the advanced version of the solution before the simpler version has actually failed.
They start with Kubernetes when a VM would do. They split into microservices when a modular monolith would do. They instrument traces everywhere when metrics and logs would answer most incidents faster.
I am not against any of those tools. I am against using them to skip the discipline of asking what problem exists right now.
What This Means for You
If your team is deciding what to monitor first, keep the sequence simple.
Start with metrics because they tell you when the system leaves the healthy path. Add structured logs because they explain what happened inside that unhealthy window. Add traces when your architecture becomes distributed enough that request flow is no longer obvious from metrics and logs alone.
That order is not anti-modern. It is pro-clarity.
If you are building on Raff, begin with a lean VM setup you can understand, instrument, and improve over time. Our Linux VM plans and public pricing page make that staged approach straightforward. Then pair this post with Single-Server vs Multi-Server Architecture and Dev vs Staging vs Production, because observability quality is tightly tied to how you structure environments and failure domains.
My rule of thumb is simple: do not instrument for the architecture you imagine you might have next year. Instrument for the one that can wake you up tonight.