Best monitoring tools for APIs: what each category actually catches
Uptime checks, synthetic monitoring, real user data and tracing catch different failures. Which category finds your problem, what the tools cost, and the alerts worth waking someone for.
Aymeric Zhuo10 min read
On this page
- What we'll cover
- The four things people mean by monitoring
- Uptime and availability checks
- Synthetic monitoring
- Real user and production traffic monitoring
- Tracing and instrumentation
- What to actually alert on
- Choosing for your situation
- A monitoring stack that fits on one page
- Frequently asked questions
- Related reading
"Is the API up?" is four different questions, and the tool that answers one of them will not answer the others. A service returning 200 to a health check while every real request times out is up by one definition and down by every definition that matters to a customer.
So the useful way to choose an API monitoring tool is not by comparing feature lists but by working out which failure you are trying to catch. This page covers the four categories, what each genuinely detects, and where each is blind.
What we'll cover
The four things people mean by monitoring
Availability. Is the endpoint responding at all, from outside your network. Cheapest to set up, narrowest in what it sees.
Correctness. Is it responding with the right thing. A 200 carrying an empty array where twelve results should be is the failure that availability monitoring was never going to catch.
Performance. How long it takes, and for which callers. Averages hide this; percentiles reveal it.
Causation. When something is slow or broken, which part. This is tracing, and it is the only category that answers "why" rather than "whether".
Most teams buy the first, believe they have bought all four, and discover the gap during an incident. The four categories below map onto these.
Uptime and availability checks
A service somewhere requests your endpoint on a schedule and records whether it answered.
What it catches: the server being down, DNS failing, a certificate expiring, a deploy that broke routing entirely. These are real and they are the failures most likely to be noticed by someone else first.
What it misses: essentially everything subtle. An endpoint returning 200 with wrong data passes. An endpoint that is fast for you and slow from Sydney passes. An endpoint that works for unauthenticated health checks and fails for real authenticated requests passes.
Typical options: UptimeRobot and Better Stack at the accessible end, with free or inexpensive tiers that cover a handful of endpoints. Pingdom and similar for more locations and history.
What to check when choosing: how often it checks, since a five-minute interval means up to five minutes of downtime before anyone knows; how many geographic locations, because regional failures are common and invisible from one vantage point; and whether it confirms from a second location before alerting, which is what stops a flaky checker paging you at three in the morning.
Worth doing regardless of what else you run. It is cheap, it takes ten minutes, and it catches the most embarrassing category of failure.
Synthetic monitoring
A scripted request, or sequence of requests, run on a schedule against real endpoints with real authentication, checking the response content rather than only the status code.
What it catches: the important middle ground. Wrong data, broken authentication, a response schema that changed, a multi-step flow where step three fails, performance degradation on a specific operation.
What it misses: anything that only happens with real production data, and anything affecting a subset of users your script does not resemble.
Typical options: Checkly is built around this and is pleasant for API checks written as code. Postman monitors suit teams who already keep their API collections there. Datadog and New Relic both include synthetic monitoring within their larger platforms.
The thing that makes this worth the effort: assertions on content. status === 200 is availability monitoring wearing a costume. body.results.length > 0 && body.results[0].id is a genuine correctness check, and it is the line that catches the deploy that quietly broke a query.
The cost to be aware of: synthetic checks need maintaining. They hold credentials, they break when the API legitimately changes, and a suite nobody updates becomes a source of false alarms that trains people to ignore alerts.
Real user and production traffic monitoring
Rather than generating traffic, this observes the traffic you already have: every request, its latency, its status, and who it came from.
What it catches: the failures that only appear at real scale and real variety. A slow query that only triggers for accounts with many records, an error affecting one client's integration, a latency problem specific to one region.
What it misses: anything on a path nobody is currently using. An endpoint nobody has called since Tuesday could be entirely broken and this will not tell you, which is precisely why it complements synthetic monitoring rather than replacing it.
Typical options: Datadog, New Relic, and Grafana Cloud at the platform end, plus whatever your API gateway or load balancer already records, which is usually more than teams realise and already paid for.
The measurement that matters: percentiles, not averages. An average response time of 120ms is compatible with one request in twenty taking four seconds. The 95th and 99th percentiles are where your angry customers live, and a dashboard showing only the mean is actively misleading.
Tracing and instrumentation
Following a single request through every service, database call, and external API it touches, with timing at each hop.
What it catches: causation. Not that the endpoint is slow, but that it is slow because of a call to a third-party service that is itself slow, on a code path that only runs for a certain kind of account.
What it misses: nothing much, which is why it is the most valuable and the most expensive in setup effort. It requires instrumenting the application rather than pointing a tool at it from outside.
Typical options: OpenTelemetry as the vendor-neutral instrumentation standard, which is worth adopting on its own terms because it decouples your instrumentation from whoever you send the data to. Then Datadog, Grafana Tempo, Honeycomb, or others as the backend.
When to invest: once you have more than a couple of services, or once you have had an incident where the honest answer to "which component caused that" was a guess. Before then, it is a lot of work for a question you can answer by reading logs.
What to actually alert on
More monitoring tools produce more alerts, and more alerts produce less attention. A short, deliberate set is worth more than a comprehensive one.
Alert on symptoms, not causes. "Checkout API error rate above 2%" is actionable. "CPU above 80%" is a fact that may or may not matter, and paging someone for it teaches them to ignore pages.
Alert on the percentile, not the average. If your 99th percentile has tripled, something is wrong for a real group of users even though the average barely moved.
Alert on absence. An endpoint that normally serves a thousand requests an hour and is now serving nine has a problem, and no error-rate alert will fire because the nine are all succeeding. This is the alert teams most often lack.
Require confirmation before paging. Two consecutive failures from two locations, rather than one. This single rule removes most false alarms.
Route by severity honestly. Something needing action within minutes pages a person. Everything else goes to a channel or a digest. Mixing them means the channel gets muted, and then you have lost the real alerts too.
Review what fired every month and delete anything nobody acted on. An alert nobody acts on is not monitoring, it is noise with a budget.
Choosing for your situation
A small service or side project. An uptime check with a free tier, plus whatever your host already records. Ten minutes of setup, and it catches the failures that would otherwise be reported by a user.
A production API with customers. Uptime checks plus synthetic monitoring with real content assertions, plus percentile latency from your gateway logs. This combination catches the large majority of what actually goes wrong.
Several services calling each other. Add tracing, instrumented with OpenTelemetry so you are not locked to one backend. The first incident it explains will justify the setup.
A regulated or high-consequence API. All of the above, plus retention long enough to investigate something noticed late, plus alerting on absence as well as failure.
One thing worth building regardless of which tools you pick: the connective tissue between monitoring and the people who act on it. Enriching an alert with recent deploys, routing it to whoever owns that service, opening a ticket when it is not urgent enough to interrupt anyone. That is ordinary automation work, and describing it is faster than wiring it together. On CodeWords you describe what should happen in plain language and Cody, the automation builder, builds it, connects it to your monitoring and chat tools, and deploys it. Automations connect to more than 3,000 integrations. The free plan covers light use, with Pro at $39 per month and Business at $100 per month as usage grows; details are on the pricing page.
A monitoring stack that fits on one page
For a team without a dedicated platform group, this covers most of what matters and takes about an afternoon.
One uptime check per customer-facing endpoint, every minute, from at least two regions, confirming from a second location before alerting. Free or nearly free.
Three to five synthetic checks covering your most important flows end to end, with real authentication and assertions on the response body rather than the status code. Pick the flows where failure would generate support tickets.
Percentile latency from your gateway or load balancer, which you almost certainly already collect and probably do not look at. A dashboard of p50, p95 and p99 per endpoint, reviewed weekly, catches slow degradation that no alert will fire on.
One absence alert per critical endpoint: traffic dropping below a floor for that time of day. This is the check almost nobody has and it catches the failures where everything technically works and nothing is arriving.
Certificate expiry checks, which are entirely predictable, trivially cheap, and still a recurring cause of outages.
Beyond that, add tracing when you have enough services that correlating an incident across them by hand has become the slow part of your investigation. That threshold usually arrives around the third or fourth service.
Frequently asked questions
What is the difference between uptime and synthetic monitoring?
Uptime monitoring asks whether the endpoint responds. Synthetic monitoring asks whether it responds correctly, using real authentication and checking the content of the response. The gap between those two questions is where most real failures live.
How often should checks run?
Every minute for anything customer-facing, since the interval is your worst-case detection time. Every five minutes is fine for internal services. Consider what a delay actually costs before paying for higher frequency.
Should I monitor from multiple locations?
Yes, for anything with users in more than one region, and it also prevents a single flaky checker from generating false alarms. Requiring confirmation from a second location before alerting is the most effective noise reduction available.
Do I need a full observability platform?
Not at first, and the platforms are worth it once you have several services and enough incidents that correlating across them by hand has become the bottleneck. Starting with uptime plus synthetic checks costs very little and covers a great deal.
What should I do about third-party APIs I depend on?
Monitor them as if they were yours, because your users cannot tell the difference. A synthetic check against a critical third-party endpoint tells you their outage has started before their status page does, which is usually several minutes of useful warning.
How do I stop alert fatigue?
Alert only on things someone would act on immediately, require confirmation before paging, route everything else to a digest, and review monthly. The discipline that matters most is actually deleting alerts nobody acted on rather than leaving them switched on out of caution.
Is logging enough on its own?
For a single small service, often yes. Logs tell you what happened once you know to look; monitoring tells you to look. The moment nobody is reading the logs continuously, you need something watching them.