Monitoring Tools in DevOps: Metrics, Logs, and Traces Compared
Monitoring Tools in DevOps: Metrics, Logs, and Traces Compared
"Monitoring tools" covers three genuinely different categories of tooling that answer three different questions, and most vendor pitches blur the line between them. This is a vendor-neutral breakdown of what each category actually does, example tools in each, and how to choose without buying three overlapping platforms that all claim to do everything.
Quick Answer
Monitoring tooling splits into three categories: metrics platforms (Prometheus/Grafana, Datadog, CloudWatch) answer "is it healthy right now, and what's the trend," logs platforms (the ELK/Elastic stack, Grafana Loki, Splunk) answer "what exactly happened, in detail, at a specific point in time," and traces/APM platforms (Jaeger, Datadog APM, New Relic) answer "where in a chain of service calls did the time go, or the error occur." Most real setups use at least two of the three, and the choice of which tool within a category matters less than making sure all three questions are actually covered by something.
Written by [Neil Millard](/about), a cloud and automation specialist with 20+ years' experience delivering infrastructure for organisations including Barclays, HMRC, Marks & Spencer, and AXA.
Why Three Categories, Not One Tool
Metrics, logs, and traces are answering different questions and are optimised for different storage and query patterns, which is why one tool being "good at everything" is rare in practice. A metrics database is optimised for fast writes of small numeric data points and fast aggregation over time windows — it will tell you request latency spiked at 14:32, but not why. A logging platform is optimised for storing and searching large volumes of unstructured or semi-structured text — it will tell you the exact error message and stack trace at 14:32:07, but querying "what was the p99 latency trend over the last week" against raw logs is slow and expensive by comparison. A tracing platform is optimised for reconstructing the path a single request took across multiple services — it will tell you that request spent 400ms of its 450ms total in a downstream database call, which neither metrics nor logs alone show clearly. See how do monitoring, logging and alerting work together in a DevOps environment for how these three feed into an actual incident response workflow.
Metrics: Is It Healthy, and What's the Trend
Metrics are numeric time-series data — request counts, error rates, latency percentiles, CPU and memory usage, queue depth. They're cheap to store at high resolution because they're small, which makes them ideal for dashboards and alerting on trends.
Example tools: Prometheus (open-source, pull-based, the de facto standard for Kubernetes-native metrics) paired with Grafana for visualisation; Datadog (hosted, broad integration ecosystem, metrics plus logs plus APM in one platform at a corresponding price); AWS CloudWatch (native to AWS, the default choice if your infrastructure is already AWS-heavy and you don't want to run your own metrics stack); New Relic (hosted, historically strong on application performance metrics specifically).
Choosing: if you're running Kubernetes and want to avoid vendor lock-in, Prometheus plus Grafana is the standard default and has enormous community support. If you want a single hosted platform and are comfortable with the cost, Datadog or New Relic remove the operational burden of running your own metrics stack. If your infrastructure is entirely AWS and your needs are moderate, CloudWatch is already there and billed alongside everything else.
Logs: What Exactly Happened
Logs are detailed, timestamped records of discrete events — an error with its stack trace, an access log line, an application event like "user login failed: invalid credentials." They're the category you reach for when metrics tell you something is wrong but not what.
Example tools: the Elastic stack — Elasticsearch for storage and search, Logstash or Beats for collection, Kibana for visualisation (often called ELK); Grafana Loki (designed to be a lighter-weight, cheaper-to-run alternative to Elasticsearch, indexing only metadata rather than full log content, and pairing naturally with Grafana if you're already using it for metrics); Splunk (mature, enterprise-focused, powerful query language, correspondingly expensive at scale); cloud-native options like AWS CloudWatch Logs for teams already committed to a single cloud provider's ecosystem.
Choosing: if you already run Grafana for metrics, Loki is worth evaluating first purely for the operational simplicity of one visualisation layer across metrics and logs. If you need powerful full-text search across large log volumes and have the budget, Elasticsearch or Splunk are the more capable options. If your log volume is currently small and mostly Kubernetes-native, starting with Loki or your cloud provider's native logging avoids standing up Elasticsearch's operational overhead before you need it.
Traces / APM: Where Did the Time Go
Distributed tracing follows a single request as it moves through multiple services, recording how long each hop took. Application Performance Monitoring (APM) tools typically bundle tracing with code-level profiling (which function, which database query) on top.
Example tools: Jaeger (open-source, CNCF project, the common choice for teams running their own Kubernetes-native observability stack); Datadog APM and New Relic (hosted, bundle tracing with metrics and logs in the same platform, useful if you want one vendor rather than three separate tools to correlate manually); OpenTelemetry (not a backend itself, but the emerging vendor-neutral standard for instrumenting code to emit traces, metrics, and logs — instrumenting with OpenTelemetry keeps you free to change which backend receives that data later without re-instrumenting the application).
Choosing: if you're not yet running distributed tracing at all, instrumenting with OpenTelemetry from the start is worth the small extra setup cost because it avoids being locked into a specific vendor's SDK. Where you send that data — Jaeger self-hosted, or a hosted APM platform — can then be decided, and changed, independently.
A Practical Selection Framework
- Start with metrics and alerting. If you have nothing else, get request rate, error rate, and latency on a dashboard with alerts on the ones that matter. This alone catches most outages faster than logs or traces would on their own.
- Add structured logging next, once metrics tell you something is wrong often enough that "why" becomes the bottleneck. Structured (JSON) logs from the start make this transition much easier than parsing free-text logs later.
- Add tracing once you have more than a couple of services calling each other. A monolith rarely needs distributed tracing; a system with five services in a request path badly needs it the first time someone spends a day guessing which one is slow.
- Consolidate vendors only when correlation pain justifies the cost. Running three separate open-source tools is cheaper but requires manually correlating a trace ID across systems during an incident; a single hosted platform with all three built in removes that manual step at a real price premium. Neither is wrong — it depends on how often you're actually debugging cross-cutting incidents versus paying for integration you rarely use.
If you're trying to work out which combination is worth paying for versus running yourself, that's exactly what an office-hours call is for.