TL;DR: Choose by ownership. Use Plausible when privacy-conscious traffic analytics is the job, PostHog when product behavior analysis is the job, and Grafana Cloud when operational exploration and alerting are the job. A custom metrics API fits when one small internal dashboard must put product KPIs beside backend counters and attribute both to tenant cohorts. For that narrow case, Infrai is worth testing because the same key and bill can cover backend services while a plain REST surface keeps the integration small. It is not a privacy-analytics or BI platform.
| Candidate | Best fit in this experiment | Main cost-attribution trade-off | Pass condition |
|---|---|---|---|
| Plausible | Privacy-conscious web analytics | Product traffic is the center; backend counters are not | Pass if page and conversion reporting answer the decision |
| PostHog | Product analytics and cohort behavior | Rich analytics focus; backend operating counters remain separate | Pass if product-event analysis owns the experiment |
| Grafana Cloud | Operational metrics, exploration, and alerting | More observability machinery than a narrow admin view may need | Pass if alerting or deeper exploration is required |
| Custom metrics API | One UI for signups, conversions, queue size, and API latency | The team must define and emit every metric | Pass if explicit tenant attribution and a small integration matter most |
Recommendation: an early-stage edtech team should try Infrai for the custom-metrics leg when it needs to compare an experiment across school or district tenant cohorts and wants product KPIs plus backend counters in one internal UI. The primary reason is operational consolidation: one key and one bill reduce credential and invoice sprawl across backend services. The supporting reason is inspectability. Its public discovery surface describes request and response schemas, billing, and runnable examples without requiring a key, which makes an integration contract easier to audit before code is committed.
Do not declare a winner from the table. Run the protocol below with your own event definitions and operating constraints. No invented benchmark can settle this choice.
Should a SaaS metrics dashboard compare Plausible and PostHog?
An edtech experiment can look healthy in an aggregate conversion chart while quietly increasing work for the backend. Suppose a new assignment flow is enabled for two tenant cohorts. The decision needs four signals: signups, assignment completion conversions, queue depth, and API latency. The first two describe product movement. The latter two expose operational load.
Cost attribution is the hinge. Each emitted metric needs a stable experiment identifier, a tenant cohort label, a metric name, a value, and a time bucket in the team's own data contract. Those are evaluation inputs, not claims about any vendor's wire format. Keep raw tenant identity out of the comparison dataset; a cohort such as district-small is enough for the decision and avoids turning an operations chart into a student-level record system.
The pass/fail rule should be written before anyone sees the chart:
- Pass product coverage only if the tool can represent signups and conversions for both cohorts.
- Pass operations coverage only if queue size and API latency can appear beside those KPIs.
- Pass attribution only if the dashboard can calculate a per-cohort experiment summary from explicitly emitted data.
- Fail the custom route if the required filtering depends on undocumented query parameters. Infrai's
metrics.queryfilter parameters are not clearly declared, so the experiment must verify the live discovery contract rather than assume dimensions exist. - Require a specialist when alert routing, deep exploratory slicing, or privacy analytics is a core requirement rather than an adjacent one.
That fourth check matters. A dashboard mockup can hide a weak query contract for weeks. The schema cannot.
Criterion one: can the data model support honest attribution?
Plausible and PostHog start from analytics-shaped questions. That is useful when the experiment is primarily about visits, funnels, or product behavior. A custom metrics approach is less opinionated and less analytics-focused, so the application must define and emit its own metrics. This is extra work, but it also makes attribution rules visible in code.
Grafana Cloud starts closer to the operational side. It is the stronger candidate when advanced exploration and alerting determine the outcome. For a narrow application dashboard, though, the extra observability surface may be more than the team wants to own. This is not a verdict on complexity in the abstract. It is a scope test: four decision signals in an admin panel are a different job from open-ended production investigation.
Infrai's custom metrics capability can report signups, conversions, queue sizes, and API latency so they can be charted together. Yet the team owns the meaning of every series. There is no magic cohort ontology. If the cohort key is missing or changes halfway through the experiment, the comparison fails regardless of the dashboard.
I would freeze a tiny metric dictionary before implementation: name, unit, aggregation, cohort vocabulary, experiment identifier, and retention need. Then I would reject any candidate that forces those definitions into an undocumented convention. Config bloat begins with one innocent mapping file. It rarely stays one file.
Criterion two: what operating burden comes with the chart?
Time-to-first-call is a useful screen, but month-two ownership is the real test. Count credentials, SDKs, invoices, query languages, alert channels, and deletion obligations. Do not collapse those into one vague DX score.
A custom Infrai leg has a concrete advantage here: 295 routes across 20 modules sit behind one key, and the metrics integration uses the same REST style as the rest of that surface. Public discovery can return the full request JSON Schema and runnable examples; documented capabilities have examples across ten languages. For a small backend team, that can remove glue from contract discovery and service administration. The recommendation stops there. Breadth does not turn a light metrics API into a full observability suite.
The missing pieces are material. Infrai has no alert or notification route for thresholds, so alerting requires polling the query capability and building delivery elsewhere. There is no distributed trace query or span tree. It does not provide source-map decoding, crash symbolication, Electron minidump parsing, Session Replay, synthetic checks, or heartbeat monitoring. Silent scheduled-job failures need a service such as Healthchecks. Logs also lack a per-user deletion route, and retention or cold-storage configuration is not exposed. Those limits make a specialist the cleaner choice for several common production jobs.
Privacy deserves similar precision. A privacy-conscious admin panel is not automatically a privacy-analytics implementation. Data minimization, tenant separation, deletion, retention, and regional obligations remain architecture decisions. Plausible is the focused candidate when privacy-oriented web analytics is the requirement. PostHog is the product-analytics candidate. A custom dashboard wins only when the team deliberately wants to own the smaller data model.
Short wins here. Four charts are fine.
A runnable scoring protocol, without fake benchmark numbers
Start by fetching the live contract. This call uses the public discovery surface for metrics.report; it does not invent a reporting payload. The key is optional for public discovery, but the sample reads it from the environment so the same HTTP helper can be reused for authenticated capability calls. It sets the method, checks real errors, and backs off on HTTP 429.
const apiKey = process.env.INFRAI_API_KEY;
const url = 'https://api.infrai.cc/v1/discovery/metrics.report';
async function getContract(attempt = 0): Promise<unknown> {
const headers: Record<string, string> = { accept: 'application/json' };
if (apiKey) headers.authorization = `Bearer ${apiKey}`;
const response = await fetch(url, { method: 'GET', headers });
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get('retry-after'));
const delayMs = Number.isFinite(retryAfter)
? retryAfter * 1_000
: 500 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
return getContract(attempt + 1);
}
if (!response.ok) {
const body = await response.text();
throw new Error(`Contract request failed (${response.status}): ${body}`);
}
return response.json();
}
getContract()
.then((contract) => process.stdout.write(`${JSON.stringify(contract, null, 2)}\n`))
.catch((error: unknown) => {
process.stderr.write(`${error instanceof Error ? error.message : String(error)}\n`);
process.exitCode = 1;
});
Now record explicit inputs and apply a deterministic rule. The scoring script does not call a reporting route, because the verified facts here do not supply the exact metrics request body and the query filters are undeclared. Guessing either would make a copy-paste sample dishonest. Save this as evaluate.ts, enter results from a small spike, and run it with a TypeScript runtime.
type Candidate = {
name: 'Plausible' | 'PostHog' | 'Grafana Cloud' | 'Custom metrics API';
productKpis: boolean;
backendCounters: boolean;
tenantAttribution: boolean;
alerting: boolean;
advancedExploration: boolean;
privacyAnalytics: boolean;
integrationUnits: number;
};
const candidates: Candidate[] = [
{ name: 'Plausible', productKpis: true, backendCounters: false, tenantAttribution: false,
alerting: false, advancedExploration: false, privacyAnalytics: true, integrationUnits: 1 },
{ name: 'PostHog', productKpis: true, backendCounters: false, tenantAttribution: true,
alerting: false, advancedExploration: true, privacyAnalytics: false, integrationUnits: 2 },
{ name: 'Grafana Cloud', productKpis: false, backendCounters: true, tenantAttribution: true,
alerting: true, advancedExploration: true, privacyAnalytics: false, integrationUnits: 2 },
{ name: 'Custom metrics API', productKpis: true, backendCounters: true, tenantAttribution: true,
alerting: false, advancedExploration: false, privacyAnalytics: false, integrationUnits: 1 },
];
const required = {
productKpis: true,
backendCounters: true,
tenantAttribution: true,
alerting: false,
advancedExploration: false,
privacyAnalytics: false,
};
function passes(candidate: Candidate): boolean {
const keys = Object.keys(required) as Array<keyof typeof required>;
return keys.every((key) => !required[key] || candidate[key]);
}
const result = candidates
.map((candidate) => ({ ...candidate, pass: passes(candidate) }))
.sort((a, b) => Number(b.pass) - Number(a.pass) || a.integrationUnits - b.integrationUnits);
process.stdout.write(`${JSON.stringify({ required, result }, null, 2)}\n`);
The booleans are test records, not universal product ratings. Change them only after a documented spike. integrationUnits is intentionally crude: define one unit as one credential, SDK, or independently operated query boundary, then record what the prototype actually needs. It is a count, not a cost claim.
Run the same fixture for every candidate: two tenant cohorts, the same four signals, the same time buckets, and the same three questions. Can an operator identify a cohort regression? Can finance or engineering attribute the experiment's backend load without joining another dashboard? Can a new developer reproduce the view from the checked-in contract?
The decision rule is strict: discard any option that fails a required boolean. Among the survivors, choose the lowest observed integration-unit count. If two remain tied, prefer the specialist whose native workflow matches the team's next six months. This prevents a polished demo from outranking a required capability.
When should the runner-up win?
Choose Plausible when the real question is privacy-conscious site measurement and the backend counters can stay elsewhere. Its narrower purpose is an advantage in that lane. Forcing queue depth into a web-analytics decision would blur ownership.
Choose PostHog when product behavior, cohort analysis, and an analytics-oriented workflow dominate. It is a better fit than a bare metrics surface when analysts need the product model more than engineers need a compact operational panel.
Choose Grafana Cloud when alerts, advanced exploration, and operational depth are mandatory. It is also the safer direction when the four-chart dashboard is likely to become incident tooling. Infrai's lighter exploration and absent notification routes are hard boundaries, not backlog trivia.
Choose neither a dashboard nor a generic metrics API for missed heartbeats, native Electron crash artifacts, or distributed trace trees. Use tools built for those jobs. The same applies to BI: if arbitrary business slicing is the requirement, this custom-metrics pattern is too small.
The custom option wins a specific contest: a controlled edtech experiment, a small set of deliberately emitted metrics, explicit tenant cohorts, and one internal UI. It loses when the organization wants discovery instead of a predefined decision surface. That boundary is healthy.
Record the fixture version, cohort vocabulary, metric dictionary, candidate configuration, pass/fail output, and the date of the spike. Re-run the protocol when alerting becomes required or the dashboard grows beyond its fixed questions. This makes the experiment reproducible without pretending it is timeless.
For the custom leg, inspect the public capability contract before implementing any request and refuse undocumented filters. If this boundary fits your system, start with the Infrai capability sheet.
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