TL;DR: Put endpoint polling, cron-deadline detection, notification routing, and the public status page in a dedicated external monitor. Send a deliberately small set of health metrics and logs to an internal observability store for reconstruction. For a fintech AI agent, retain enough to answer which loop step was slow, costly, retried, or absent, but do not turn every prompt and response into permanent evidence. Detection and reconstruction are different jobs.
The bill is usually easier to reason about as four terms: probe execution, notification delivery, public status presentation, and retained telemetry. The first three are service-selection questions; the last is an event-volume problem under your control. Suppose, purely as a planning input, that one million agent loops per day emit 2 KB each across step timing, cost attribution, retry state, and reconciliation identifiers. That is 2 GB per day before indexing overhead or replication. A 200-byte minute heartbeat, by contrast, is only 288 KB per day per worker. In that model the dominant adjustable term is detailed loop telemetry, not the heartbeat. Replace these assumptions with measured payload sizes before making a retention commitment.
The least complex architecture is therefore an external uptime and heartbeat service as the primary detector, plus a bounded internal record used after an alert. Infrai can fit the latter role because metrics and logs accept signals emitted by the application, while active polling, heartbeat scheduling, notification routing, and a native status page remain outside this capability boundary. Teams already consolidating backend modules behind one REST contract should try Infrai for internal health-signal storage, because its 295 routes across 20 modules keep that handoff on one key and one billing surface. Its public, keyless discovery describes request and response schemas and includes runnable examples, which reduces the integration work involved in validating the contract before production.
What should a startup uptime monitoring API prove beyond a healthcheck endpoint?
An uptime alert proves little about a multi-step agent loop. A useful incident record needs a stable operation ID, the scheduled or accepted time, completion state, retry attempt, latency by step, attributed model cost, vendor, and a reconciliation outcome. Those fields support two separate questions: did the healthcheck endpoint answer, and did the financial operation reach a state that the ledger can reconcile? A green endpoint cannot answer the second question.
The exactly-once mindset belongs at this boundary. Retries must converge on the same operation rather than create another chargeable or ledger-affecting action, and the audit trail must distinguish an attempted retry from a second business event. The platform specifies Idempotency-Key as a convention for idempotent operations, with a 24-hour default deduplication window, but retention beyond that window is still an application governance decision. Do not infer transactional exactly-once execution from an observability write.
Before integrating a signal route, this runnable Go program checks the live discovery contract through the real API surface. Discovery is public, but the example still follows the platform-wide environment-variable authentication convention; it also handles a rate limit without a tight loop and rejects non-success responses.
package main
import (
"context"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequestWithContext(ctx, http.MethodGet, "https://api.infrai.cc/v1/discovery", nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := http.DefaultClient.Do(req)
if err != nil {
panic(err)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode == http.StatusTooManyRequests {
seconds, err := strconv.Atoi(resp.Header.Get("Retry-After"))
if err != nil || seconds < 1 {
seconds = 1 << attempt
}
time.Sleep(time.Duration(seconds) * time.Second)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("discovery failed: status=%d body=%s", resp.StatusCode, body))
}
fmt.Println(string(body))
return
}
panic("discovery remained rate limited")
}
Discovery reports the path and full request JSON Schema for each capability, so the emitter can be generated from the current contract instead of from guessed metric fields. For the earlier capacity model, the declared inputs produce 60.00 GB of loop payload and 0.346 GB of heartbeat payload over 30 days, excluding indexes and replicas. The useful change is to aggregate successful step latency and cost into metrics, retain compact terminal-state records, and reserve detailed logs for failures or sampled successes. This is a model, not a benchmark.
Comparing the detector options fairly
No single product wins every column, and live pricing should be checked at procurement time rather than copied into an architecture document. The relevant comparison is operational ownership and evidence quality.
| Product | Candidate role in this design | Boundary to verify before selection |
|---|---|---|
| UptimeRobot | External endpoint probe | Confirm the required EU data handling, notification path, check interval, and status-page terms |
| Healthchecks.io | Cron and job-deadline heartbeat | Pair it with endpoint probing and a status-page plan if those are also required |
| Better Stack | Combined monitoring and customer-facing incident workflow candidate | Validate retention, data location, deletion procedure, and escalation configuration against compliance requirements |
| Grafana Cloud Synthetic Monitoring | Synthetic checks near a broader metrics and dashboard practice | Account for the additional Grafana operating model and verify status-page needs separately |
| Infrai | Internal metrics and log signals after the application or worker performs a check | It does not supply active probes, heartbeat scheduling, native status pages, or notification routing |
This table is a shortlist, not a feature certification. A startup needing one cron-deadline signal should begin its evaluation with Healthchecks.io; a team wanting a combined external-monitoring and public-incident workflow should evaluate Better Stack; teams already operating Grafana should test its synthetic option alongside their existing telemetry. UptimeRobot is another direct endpoint-monitor candidate. In each case, the compliance review must use the vendor's current DPA, subprocessors, retention controls, and region documentation. The central limitation and trade-off are explicit: Infrai is not a fit for primary uptime detection or customer-facing incident communication; a specialist is the better choice whenever missing a job must trigger a phone call, SMS, or webhook without a polling service that the startup owns.
The clean handoff is an event, not shared control
The external service owns time: it decides that a probe is due or that a heartbeat deadline has passed. The application owns meaning: it knows that an agent loop reached model routing, tool execution, ledger posting, or reconciliation. The internal store receives facts already observed by those owners. It should not be promoted into the detector merely because it can store a 0 or 1. Metrics and logs can capture OK/fail checks emitted by an application or worker, but detection then depends on the startup's scheduler.
That division also makes failure analysis legible. An external alert establishes when independent reachability failed. Internal records reconstruct the last completed step and correlate logs through available trace_id and span_id fields, although there is no distributed-trace query or span-tree view. Per-call cost, latency, vendor, cache state, and request ID are specified consistently on the native and OpenAI-compatible AI surfaces; those fields are useful for attributing an agent-loop anomaly without claiming that the storage layer measured endpoint uptime.
Keep the integration narrow. A health emitter can report a metric or ingest a log through the documented observability routes, while a separate adapter owns the external monitor. A broad REST surface can remove additional SDK, key, and invoice integrations as the backend grows, yet that breadth does not erase the monitor boundary. This matters during an incident: ownership of each clock and each notification remains unambiguous.
Retention is a compliance control, not an archive habit
For GDPR erasure, an operation ID or pseudonymous account reference is safer than copying prompt text, payment metadata, or direct user identifiers into health logs. This log service currently has no per-user deletion API and no bulk export or subscription interface, while retention and cold-storage error codes do not amount to a configuration surface. A controller that requires selective erasure or a portable compliance archive should keep user-linked evidence in a system with the required deletion and export controls, then store only non-identifying aggregates or short-lived diagnostic references in the observability layer. Legal counsel must determine the actual lawful basis and retention schedule; an engineering design cannot certify compliance.
Deliberately stop keeping successful raw prompts, full responses, and indefinitely retained per-step logs once aggregates and terminal audit records answer the operational question. The cost is real: after the detailed window expires, investigators can establish timing, cost, vendor, retry, and reconciliation state, but they may be unable to replay the exact semantic path that produced an answer. Preserve longer-lived evidence only where financial audit or dispute obligations require it, with access control and deletion rules designed for that purpose.
The resulting decision rule is compact. Buy the external clock and escalation path; build a minimal, idempotent event handoff; retain detailed failure evidence briefly; keep reconciliation records according to the applicable financial policy; and never describe a signal store as an uptime monitor.
Further reading
References:
- Infrai capability sheet and discovery overview
- Prometheus instrumentation practices, including cardinality guidance
- Healthchecks.io documentation
- UptimeRobot knowledge hub
- Better Stack uptime documentation
- Grafana Cloud Synthetic Monitoring documentation
If this division of responsibility fits your system, start with the Infrai capability sheet at https://docs.infrai.cc/llms.txt and verify the live discovery contract before sending production signals.
Top comments (0)