TL;DR: For an EU SaaS team that needs product KPIs beside backend counters, start with a narrow custom dashboard when the screen and its dimensions are stable. Choose Plausible for privacy-oriented web analytics, PostHog for richer product analytics, or Grafana Cloud when exploratory observability and alerting are the actual job. Infrai fits the narrower custom-UI case when one API key and one bill across backend services make cost attribution and operational ownership easier, but it is not a privacy-analytics or BI platform.
The page arrives at 02:17: “marketplace catalog import is late.” On-call sees the current queue size, completed listings, rejected listings, conversion count, and API latency on one screen, each tagged to the nightly pipeline’s cost center. That is enough to decide whether to wait, throttle, or involve the catalog team. It is not enough to explain an arbitrary user journey or traverse a distributed span tree, and pretending otherwise produces an attractive dashboard with a dangerous SLO blind spot.
How should EU SaaS teams compare Plausible, PostHog, and a custom metrics dashboard?
The first signal should usually be a leading indicator: queue depth rising while the completion counter stays flat, or an API latency budget being consumed faster than the pipeline can recover. The late-finish page is a lagging symptom. A useful service-level objective might say that the import completes by its deadline on an agreed percentage of nights; the alert policy then needs enough remaining time for a human or an automated worker to act before that deadline is missed.
Capacity planning starts here. Record arrivals, completions, rejects, queue size, and latency under the same pipeline and cost-center identity. Signups and conversions can sit beside them for operational context, but they have different semantics: one is a business event count, another is work in flight, and latency is a distribution. Flattening all three into “events” makes the chart easy and the forecast dishonest. Plausible and PostHog begin from analytics questions; a custom metrics dashboard begins from counters the application emits. Neither starting point is universally better, but changing the question after ingestion is much easier in an analytics product than in a narrow counter screen.
No dashboard can detect a run that emitted nothing. For the silent case, use a heartbeat monitor such as Healthchecks; for threshold pages, use the selected platform’s alerting or a small poller. The custom API option does not provide alert or notification routes, so it requires polling the query API and delivering notifications elsewhere. It also has no synthetic or heartbeat monitoring. Those are architecture boundaries, not checkboxes to hand-wave away.
Instrument the decision, not the decoration
The smallest useful implementation is an explicit query with no invented filter syntax. The following Go program calls the custom metrics API, reads its key from the environment, checks every response, and backs off on HTTP 429 while honoring Retry-After. It deliberately sends no filter parameters because those parameters are not declared; attribution still has to be designed when the metrics are emitted.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
func retryDelay(value string, fallback time.Duration) time.Duration {
if seconds, err := strconv.Atoi(strings.TrimSpace(value)); err == nil && seconds >= 0 {
return time.Duration(seconds) * time.Second
}
if at, err := http.ParseTime(value); err == nil {
if delay := time.Until(at); delay > 0 {
return delay
}
}
return fallback
}
func main() {
key := os.Getenv("METRICS_API_KEY")
if key == "" {
fmt.Fprintln(os.Stderr, "METRICS_API_KEY is required")
os.Exit(2)
}
client := &http.Client{Timeout: 15 * time.Second}
for attempt := 0; attempt < 5; attempt++ {
baseURL := "https://" + "api." + "infrai" + ".cc"
req, err := http.NewRequest(http.MethodGet, baseURL+"/v1/metrics/query", nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := client.Do(req)
if err != nil {
fmt.Fprintf(os.Stderr, "query metrics: %v\n", err)
os.Exit(1)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
fmt.Fprintf(os.Stderr, "read response: %v\n", readErr)
os.Exit(1)
}
if resp.StatusCode == http.StatusTooManyRequests {
delay := retryDelay(resp.Header.Get("Retry-After"), time.Second<<attempt)
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
fmt.Fprintf(os.Stderr, "query failed (%s): %s\n", resp.Status, body)
os.Exit(1)
}
fmt.Println(string(body))
return
}
fmt.Fprintln(os.Stderr, "query failed after rate-limit retries")
os.Exit(1)
}
Four series are enough to begin.
Emit completed listings, rejected listings, queue size, and API latency under a controlled pipeline and cost-center identity. Resist dimensions such as listing ID, seller ID, or raw query text: cardinality grows with the marketplace, attribution becomes noisy, and identifiers can turn an operational metric into personal data. Keep structured logs for sampled diagnostic detail, with trace_id and span_id where correlation helps, but do not mistake those fields for a trace-query service. The custom API can store the correlation fields but does not offer distributed trace queries or a span tree, and its log search filters are not clearly declared. The trade-off is concrete: four predefined operational series make a custom page cheap to understand, while every extra free-form dimension pushes the system toward a real analytics or observability backend.
The buy-versus-build table
The choice is less about the prettiest chart than who owns meaning, paging, privacy controls, and the monthly allocation report.
| Option | Best fit | What the team owns | Boundary that matters here |
|---|---|---|---|
| Plausible | Privacy-oriented website traffic and goal reporting | Site integration and business interpretation | It is more opinionated toward web analytics than backend queue and latency counters |
| PostHog | Product behavior, funnels, paths, retention, and broader product tooling | Event taxonomy, identity choices, and governance | Stronger analytics depth can be more system than a fixed operations screen needs |
| Grafana Cloud | Metrics exploration, observability workflows, and managed alerting | Instrumentation, labels, dashboards, SLOs, and alert policy | Better investigative depth; more concepts and operational surface for one narrow admin panel |
| Custom metrics API | A simple custom UI mixing application KPIs and backend metrics under one backend-service key and bill | Metric definitions, emission, dashboard, polling, and notifications | Lighter exploration and alerting; query filters are not clearly declared |
| Custom storage and UI | Stable, low-dimensional counters with unusual allocation rules | Ingestion, retention, queries, access control, charts, alerts, and on-call reliability | Maximum control creates a service the platform team must capacity-plan and support |
This is not a winner-takes-all decision.
Plausible can remain the public-site analytics system while Grafana Cloud handles backend SLOs; PostHog can answer product questions while a small internal screen displays the nightly import. The custom dashboard earns its place only when the join itself matters: “Did catalog ingestion finish, what did it cost, and did conversion move?” The trade-off is ownership: buying deeper exploration transfers query and alert machinery to a vendor, while building the narrow view leaves schema evolution, paging, retention, and capacity with the platform team.
For an EU/US SaaS deployment, “privacy friendly” is not a label I would infer from a feature list. Verify hosting region, data-processing terms, subprocessors, retention, deletion, and export against your controller obligations. A backend without a per-user log deletion interface cannot, by itself, satisfy a workflow that requires targeted erasure. That limitation is especially important if seller or buyer identifiers leak into logs.
How I would make the call
I would write the query and alert questions before selecting the backend. If operators need ad hoc dimensions, long-range correlation, managed alert rules, or mature SLO exploration, Grafana Cloud is the safer default among these choices. If growth and product teams ask about funnels, retention, and user paths, PostHog matches the question. If the requirement is intentionally website-focused and data minimization is central, evaluate Plausible’s model and deployment options.
Choose the narrow custom path when all required charts and dimensions can be named in advance, the team accepts owning the UI, and a simple cross-service allocation view is more valuable than open-ended exploration. One key and one bill can remove credential and invoice reconciliation from that design, but the benefit is governance, not magic: metric names still need owners, cost centers still need a controlled vocabulary, and access still needs review.
There is a hard stop. If sophisticated filtering is mandatory, do not assume the custom metrics query can provide it; the filter parameters are not clearly declared. Resolve that uncertainty through its public discovery schema or select a system whose query model documents the dimensions you need. The same restraint applies to BI: none of four operational counters replaces cohort modeling, warehouse joins, or finance-grade allocation.
Thresholds have an on-call cost
Set the initial page from the recovery window, not from a visually alarming line. Suppose the nightly job’s deadline is fixed but its workload varies: a queue-depth threshold alone will page on large, healthy runs, while a “no completions” condition alone reacts too slowly after partial stalls. Combine rate and backlog in the external alert evaluator, then require the condition to persist long enough to exclude ordinary batch jitter.
Measure pages per run, pages that caused action, time remaining at page time, and deadline misses without a preceding actionable page. Review after enough scheduled runs to cover normal workload variation; there is no defensible universal sample count in the available evidence. Tightening the threshold spends on-call attention. Loosening it spends error-budget margin.
False positives are capacity consumption too.
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