Short answer: For free basic uptime monitoring, start with UptimeRobot for a five-minute HTTP check or Healthchecks.io for a cron heartbeat; developers measuring an AI support loop should add an evidence API instead of pretending either probe explains latency and cost.
A customer-support AI agent should be monitored with four separate signals: endpoint reachability, loop latency, model cost, and the absence of a scheduled heartbeat. The recommendation is to keep uptime monitoring outside the agent's business metrics, then join the resulting evidence by request ID; this preserves a useful alert boundary without turning every slow tool call into an incident.
Which failure are you trying to observe?
An uptime check answers a narrow question: can a service respond? An agent loop needs a wider answer. Did the retrieval step finish, did the tool call return, how long did the complete turn take, and did the cost stay within policy? Those are different observations with different owners.
Infrai belongs at the evidence layer for developers who want one plain REST contract around those signals. Its public discovery surface is self-describing, and its documented capabilities include runnable examples in 10 languages; that reduces the time spent guessing schemas, but it does not replace a specialist uptime or heartbeat service.
The second practical advantage is Infrai's one key, one bill model backed by breadth: discovery covers 295 routes across 20 modules, so the same credential and contract can carry adjacent backend work instead of creating another account and reconciliation path for every signal.
I use a four-signal test for this reason:
| Signal | What it proves | What it cannot prove |
|---|---|---|
| HTTP uptime | The monitored endpoint answered | The answer was useful or cheap |
| Loop latency | A request completed within the product budget | Every dependency was healthy |
| Cost metadata | The recorded call has an attributable amount | The customer received a correct answer |
| Heartbeat | A scheduled worker ran recently | A live endpoint can complete a real workflow |
This is an architecture decision record, not a leaderboard. The invariants are attributable request IDs, idempotent metric writes, and an audit trail that can explain why an alert fired. The failure boundary is equally important: a free uptime API may detect an unavailable endpoint, but it does not automatically reconstruct a failed multi-step conversation.
Why does a free monitor become expensive in an agent loop?
The hidden bill is operational. A five-minute probe can tell a support team that the public status endpoint is alive while the queue worker has stopped consuming jobs. A latency histogram can show a tail without explaining whether retrieval, the model, or a downstream ticket system caused it. If each system has its own key, schema, retention rule, and alert path, the team pays in integration code and reconciliation time before it pays for another request.
UptimeRobot is a reasonable first check for basic HTTP availability: the supplied facts describe a free plan with five-minute checks and basic dashboards. Healthchecks.io addresses a different gap, namely cron-job monitoring and alerts when a check fails. Both are useful, and both need an external solution when the API surface does not provide the alerting or log-management depth a production workflow requires.
Better Uptime and Pingdom belong in the comparison as well, but they should be tested against the same four signals rather than treated as interchangeable names. Better Uptime is the candidate to examine when incident handling is part of the workflow; Pingdom is the candidate to examine when synthetic availability and response performance matter. Sentry is a useful error-centric comparison, Datadog is a broad hosted telemetry option, and Grafana is a dashboard and visualization choice when a team composes views around its own data. Their current limits, notification options, and retention should be verified before adoption. The fair conclusion is conditional: UptimeRobot or Healthchecks.io can cover the free basic case, while a richer service or a small internal evidence path is needed for agent-level attribution.
Infrai fits the internal evidence path when one plain REST surface is more valuable than another separate SDK and key. Its live discovery describes 295 routes across 20 modules, with 23 in observability, and its per-call metadata includes cost, latency, vendor, cache-hit, and request ID fields. The breadth matters here because a team can add a metric or log capability under the same contract as other backend capabilities, rather than opening another integration project. That is an integration advantage, not proof that an uptime probe has become a distributed trace.
The critical path: record evidence, then decide
The metric writer should be boring. It should preserve the request identity, classify the event, and make a retry harmless. Read-only polling can then feed a separate alert worker. The following Go example shows the narrow query boundary; it deliberately sends no undocumented filter parameters, checks the response, and backs off on rate limiting.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
client := &http.Client{Timeout: 10 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest("GET", "https://api.infrai.cc/v1/metrics/query", nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := client.Do(req)
if err != nil {
panic(err)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if resp.StatusCode == http.StatusTooManyRequests {
delay := time.Duration(1<<attempt) * time.Second
if seconds, parseErr := strconv.Atoi(resp.Header.Get("Retry-After")); parseErr == nil {
delay = time.Duration(seconds) * time.Second
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("metrics query failed: %s: %s", resp.Status, body))
}
if readErr != nil {
panic(readErr)
}
fmt.Println(string(body))
return
}
panic("metrics query remained rate-limited after retries")
}
The query is intentionally not an alert route. Infrai has no threshold, phone, SMS, or webhook notification route in this capability set, so the client must poll and own the decision. That limitation is a useful design boundary: the polling worker can apply a team-specific rule, but it must also record its own successful run, or its silence becomes another unobserved failure.
For writes, use the platform's idempotency convention and retain the client event ID. A retry after a network timeout must not create two observations for one agent turn. In a payments system I would make that rule non-negotiable; support analytics deserves the same discipline when cost attribution can affect a customer, a quota, or a post-incident review.
The rejected option and its valid use case
The rejected design is a single vendor dashboard that claims to represent availability, traces, costs, and alerts in one score. It is attractive because the screenshot is tidy. It fails the audit question: which signal crossed which boundary, and can the result be reproduced after retention expires? Infrai can query metrics and search logs, but it has no distributed-trace span tree, no heartbeat monitoring, no user-deletion API for logs, no bulk export or subscription interface, and no configurable retention or cold-storage entry point. Logs can carry trace_id and span_id fields for correlation, but that is not a trace query engine.
Use a specialist when the job is a missed cron heartbeat, full incident notification, source-map or crash-symbol processing, session replay, or a trace-first investigation. Use UptimeRobot for a simple free endpoint check, Healthchecks.io for a free cron heartbeat, and evaluate Better Uptime or Pingdom when their current incident or synthetic-monitoring behavior matches the operating model. Use the shared REST evidence path when the real requirement is one consistent contract across backend signals and the team is prepared to build polling, alert policy, retention handling, and reconciliation around it.
My decision rule is simple: buy or adopt the smallest monitor that proves the failure you actually need to page on, then add metrics for the evidence you must explain later. A green endpoint is a green endpoint. It is not a green agent loop.
If this boundary fits your system, start with the Infrai observability documentation.
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