Your logs are not just for debugging after something breaks. The same records your backend already emits can be watched and alerted on continuously: errors and success rates, payments, slow routes, jobs that should finish every record, the numbers your users see, cost. That is log monitoring, and most small teams use a fraction of what their logs can already tell them. Here is what is actually in there.
Analytics vs monitoring: what your logs are for
People lump all of this under "analytics," so it helps to separate it properly. There are really two kinds of analytics, and then there is monitoring. The cleanest way to draw the line: product analytics is what your user does; monitoring is what your system does.
- Business analytics is the high-level view of how the company is doing: revenue, growth, retention, the numbers that go in a board deck.
- Product analytics is what users see and do. Page views, clicks, funnels, what converts. Tools like Google Analytics, Mixpanel, and Amplitude. It captures the user side of the story.
- Monitoring is what your system does, and why it behaves the way it does. Not just "is it up." Which requests were slow and why, what errored, whether a charge went through, whether the nightly job ran end to end. It mostly comes from the backend, since that is where your system's behavior is recorded, but it does not have to: you can send frontend events into monitoring too when you want to watch them as system behavior rather than user behavior.
Two assumptions trip people up. First, that monitoring is only technical health, when you can monitor business logic just as easily: did sign-ups complete, did orders get charged, did billing run last night. Second, that monitoring is strictly backend; it usually is, because that is where the richest signal lives, but it is not a hard rule. The real line is the question you are asking, what did the user do versus what did the system do, not where the event happened to fire. And the system's side is the part that mostly goes unused: your backend records what it is doing and, through its logs, effectively what it is thinking, then ships it to a store that gets read only when something is already on fire.
This is not just a framing point. In Grafana's 2025 observability survey of more than 1,200 teams, 87% already collect logs, yet most still only open them after something breaks.1 The idea that you can detect real problems from the log stream automatically is not new,2 but it has changed shape recently: 2025 surveys catalog how large language models now parse, search, and flag anomalies in logs,3 and systems shipped in 2024 pair an LLM with a smaller model to spot anomalies across real production log datasets.4 The signal has always been there; what changed is that reading it no longer takes a dedicated data team.
A log is just a timestamped record of something that happened: a request came in, a query ran, a payment failed. Logs are one of the three standard telemetry signals, alongside metrics and traces, that OpenTelemetry defines for understanding a running system.5 The question this post is really about is not "where do I put my logs," it is "what can I get out of them," and the answer is a lot more than debugging.
What you can actually monitor from your backend
Monitoring is, at its core, watching whether your system behaves the way you expect, and catching the moment it stops. That is broader than errors. It covers the things that are supposed to work: payments succeeding, sign-ups completing, a job finishing every record, the numbers you show users matching what the backend actually computed. If you run a backend with real logic, a handful of services, a database, some background jobs, and a few outbound calls, your logs and metrics already describe most of this. You do not need a special integration for each one. You decide what "as expected" means, and get told when reality drifts from it.
| Signal in your logs | What it tells you | Worth an alert when |
|---|---|---|
| Error rate | Something is throwing more than usual | A service jumps above its normal baseline |
| Success rate | Whether the thing that should work, works | Payment or signup success drops below its normal level |
| Business-logic outcomes | Did the work actually happen, and fully | A lead scraper finishes fewer jobs than expected, or a pipeline silently drops records |
| Failed payments | Revenue is leaking right now | Charge failures spike or a provider starts timing out |
| Auth errors | Login or token problems, or an attack | A sudden run of failed auth on one account or route |
| Latency on a route | A critical path is getting slow | Checkout or login p95 crosses a threshold |
| Background jobs | Work that silently did not happen | A job stops finishing or its queue keeps growing |
| Outbound calls | A third-party API is degrading | A dependency starts erroring or slowing down |
| Cost and usage | Spend is about to surprise you | Token or resource use climbs out of its normal range |
If you want a shorter answer to "what should I even watch," the canonical technical baseline is the four golden signals from Google's Site Reliability Engineering book: latency, traffic, errors, and saturation.6 Together they cover whether requests are slow, how much load you have, what is failing, and how close to full you are. But monitoring is not only about errors. The job is to watch whether the system behaves as expected, so it equally covers the things that should be working: success rates, the business outcomes you care about, and cost and usage, which is the clearest non-error example, a number that is fine right up until it quietly is not. Squeezing a system that already works to be faster is performance optimization, a separate level on top of this. For most small backends, alerting on errors and latency for your few critical routes, plus the handful of business signals and costs that matter, covers the large majority of what actually pages you at 2am. The point is that all of this is derivable from telemetry you already produce. The work is not collecting more data, it is choosing the signals and getting told when they move. The trap is the opposite extreme, where everything alerts and you stop reading any of it, which we cover in alert fatigue is killing your engineering team.
How logs go from stored to monitored
Two things turn a pile of stored logs into something you can monitor.
- Structure. A line like
payment failed for user 4821is hard to watch, but the same event as machine-readable fields,{ "event": "payment_failed", "user": "4821" }, is something you can count, filter, and threshold. OpenTelemetry treats structured logs as a first-class signal for this reason,7 and turning messy logs into structured events that machines, increasingly LLMs, can analyze is an active research area.3 - A rule that runs continuously. Monitoring is just a structured query plus a condition, evaluated over and over, so that you find out when something crosses a line instead of discovering it later by reading. That is the whole difference between storing logs and monitoring them.
Log monitoring without the enterprise setup
The reason most small teams under-use their logs is not that they do not care, it is that the tools built for this were built for big operations teams. Datadog and the like expect you to instrument heavily, live in dashboards, and learn a query language before you get anything back. That is a lot of overhead for a four-person team that just wants to know its app is up and to be told, in words, when it is not.
Fixter is built for that team, and it flips the old model on its head. Instead of building dashboards and remembering to check them, you drop in the SDK, your logs, traces, and metrics are stored, and you define what to watch and alert on in plain language. It reads your codebase and infrastructure to help you pick alerts that actually matter, rather than leaving you to hand-write rules in a query language. When something breaks it runs the first pass of the investigation and tells you the cause and who is affected, so you get a conclusion instead of a graph to babysit. And you can just ask it about your backend in plain language, from your editor, over MCP. We make the broader case for that shift in observability without dashboards.
That is the whole idea: you should be able to monitor whatever matters in your backend without standing up an operations practice to do it. Think of it as closer to dropping in an error tracker than to deploying Datadog, except it watches your whole backend and not just exceptions. If you are weighing the alternatives directly, see Datadog alternatives for small teams and the broader case for APM for small teams. The investigation side, reading logs to find a root cause, is covered in log analysis.
Key takeaways
- Product analytics is what your user does; monitoring is what your system does, including business logic, not just errors, and usually but not only from the backend
- Monitoring is watching whether the system behaves as expected: success rates, business outcomes like leads scraped, payments, latency, jobs, and cost, not only failures
- Structured logs plus a rule that runs continuously is all that separates stored logs from monitored ones
- The four golden signals (latency, traffic, errors, saturation) are a good default answer to what to watch
- You do not need Datadog or a dashboard practice; Fixter lets you monitor your backend in plain language
Frequently asked questions
What is the difference between analytics and monitoring?
Analytics comes in two forms: business analytics (high-level health like revenue and retention) and product analytics (what users see and do). Monitoring is what your system does, and why. The cleanest line is the question you ask, what did the user do versus what did the system do, not technical versus business: you can monitor business logic too, like whether payments succeed or a job processed every record. Monitoring usually comes from the backend, since that is where the richest signal lives, but you can feed frontend events into it as well.
What is log monitoring?
Log monitoring is watching the stream of log records your backend emits continuously, and alerting when something in it changes, instead of only reading logs by hand after a problem. It turns a write-only store of events into a live signal: payment failures, error spikes, slow routes, broken background jobs.
What can you monitor from your backend?
Almost anything your services record, and not just failures. Error rates and failed payments, yes, but also success rates, whether a background job finished every record, whether a number you show users matches what the backend actually computed, request latency on key routes, third-party calls degrading, and cost or usage drifting. Monitoring is really about whether the system behaves as expected, so if it shows up in a log, a metric, or a trace, you can watch it and be told when it moves.
Do I need Datadog to monitor my logs?
No. Datadog is one option, built for large operations teams, and it expects you to live in dashboards and learn a query language. Small teams can get the same outcome from a lighter, SDK-based setup that watches the signals that matter and tells you in plain language when one breaks.
What are the four golden signals?
Latency, traffic, errors, and saturation. Google's Site Reliability Engineering book defines them as the four signals worth measuring for any user-facing system. They are a good default answer to the question of what to actually monitor, because together they cover whether requests are slow, how much load you have, what is failing, and how close to full you are.
Sources
- Grafana Labs, 2025 Observability Survey (1,255 respondents): 87% collect logs, 37% cite cost as a top concern
- Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael I. Jordan, "Detecting Large-Scale System Problems by Mining Console Logs," SOSP 2009 (the early result that logs can be mined automatically)
- Siraaj Akhtar, Saad Khan, Simon Parkinson, "LLM-based event log analysis techniques: A survey," arXiv:2502.00677, February 2025 (LLMs for parsing, search, and anomaly detection in logs)
- Wei Guan, Jian Cao, Shiyou Qian, Jianqi Gao, Chun Ouyang, "LogLLM: Log-based Anomaly Detection Using Large Language Models," arXiv:2411.08561, November 2024 (LLM-based anomaly detection on real log datasets)
- OpenTelemetry, signals (logs, metrics, and traces as the telemetry signals)
- Google, Site Reliability Engineering, the four golden signals (latency, traffic, errors, saturation)
- OpenTelemetry, logs as a signal (structured logs)