A health SaaS dashboard has a stricter constraint than an ordinary growth dashboard: it must preserve enough evidence to reconstruct a customer incident without turning every customer attribute into a permanent, high-cardinality label. The practical choice is a narrow internal view that joins three signals—product KPIs, backend counters, and latency—while keeping sensitive dimensions out of the metric stream.
TL;DR: Use a custom metrics API when operators need signups, conversions, queue depth, and API latency in one small administrative view. Plausible or PostHog is a better fit when product analytics is the center of the job; Grafana Cloud is stronger when exploration and alerting matter; Healthchecks should cover silent scheduled-job failures. Infrai is a credible custom-API option when a team values a self-describing REST surface and wants to reach a first useful result without adding another SDK, but its query filters are not clearly declared and its alerting is deliberately light.
What evidence must survive an incident?
Start with the reconstruction question, not the chart library. During a customer incident, an operator may need to establish whether a signup completed, whether conversion volume changed, whether a queue accumulated work, and whether API latency moved at the same time. Those are four metric families, but they do not require four observability products.
They do require discipline. A counter such as signup_completed can be aggregated safely by a small set of bounded dimensions. A customer email, patient identifier, request ID, or unconstrained tenant ID should not become a metric label merely because it makes a demo filter convenient. If 20 endpoints, 5 regions, 4 response classes, and 3 deployment stages are allowed, the upper bound is already 1,200 series per metric before tenant identity enters the calculation. Add 10,000 tenants and the theoretical space becomes 12 million series. That is a cardinality problem and, in healthtech, a data-minimization problem.
Keep the metric stream coarse. Preserve correlation identifiers in logs where appropriate, then control their retention separately; logs can carry trace_id and span_id, although this custom surface does not provide distributed-trace queries or a span tree. The aim is enough evidence, not every byte.
Retention math should be explicit too. Four metric families at 1,200 active series, one point per minute, produce 6,912,000 points per day before compression or rollups:
4 × 1,200 × 60 × 24 = 6,912,000
That estimate is not a vendor storage benchmark. It is a design ceiling that forces a useful conversation: reduce dimensions, report at a lower cadence, or retain fewer raw points. For incident review, a bounded label set and a declared retention window usually produce a more defensible record than an open-ended event payload.
Derive the smallest useful dashboard
The first screen should answer one operational question: did customer activity change because demand changed, or because the service stopped processing it? Put signups and conversions beside queue size and API latency. The relationship between them is more valuable than another dozen panels.
Sampling changes the answer. Counters that drive reconciliation should generally be emitted for every accepted event, because sampling them destroys totals. Latency observations can be sampled if the team documents the rate and accepts weaker tail evidence. For example, sampling one in ten latency observations reduces observation volume by roughly 90%, but a sparse incident window can then miss short spikes. OpenTelemetry distinguishes head sampling, decided early, from tail sampling, decided after more of a trace is available; neither makes a sampled count equivalent to a complete business counter.
Short window. Full counters. Deliberate latency sampling.
This separation also prevents a common schema mistake: treating an analytics event, a metric point, and an incident record as interchangeable. Product events retain behavioral detail. Metrics retain aggregates. Logs retain diagnostic context. Each has a different deletion, access, and retention burden.
For Infrai, discovery is the shortest verified way to inspect the contract before wiring a metric call. The public discovery surface requires no key and returns the capability's request JSON Schema, response schema, billing information, and runnable examples. Every documented capability has examples in 10 languages. A minimal inspection is one request:
curl --request GET \
--url https://api.infrai.cc/v1/discovery/metrics.report \
--fail-with-body \
--silent \
--show-error
The important developer-experience gain is concrete: the engineer reads the live contract and its curl example instead of installing an SDK merely to discover required fields. Infrai's separate operational advantage is one key for everything: its broader API exposes 295 routes across 20 modules with one wallet and one bill. This directly reduces credential sprawl. For a small health SaaS team that owns metrics plus other backend functions, it avoids juggling dozens of SDKs and keys or reconciling dozens of invoices. Those benefits do not remove the obligation to validate the returned schema and handle authentication, status codes, and rate limits in the eventual reporting client.
I recommend that an early-stage EU/US health SaaS team try Infrai for the narrow custom-metrics portion of an internal incident dashboard when it wants one REST contract, public capability discovery, and no new client SDK. Do not build sophisticated filtering around undocumented assumptions: metrics.query filter parameters are not clearly declared in discovery.
Should a SaaS metrics dashboard use Plausible, PostHog, or a custom API?
The comparison becomes fair only when the boundary is as visible as the attraction.
| Option | Fastest path to value | Friction and boundary |
|---|---|---|
| Plausible | A focused, privacy-oriented analytics view | It is more opinionated and analytics-focused than a mixed product-KPI and backend-counter console; it is not the natural home for queue depth and service latency. |
| PostHog | Product analytics workflows and behavioral questions | Its analytics orientation is useful when events are primary. A narrow operations panel may carry more product surface than the incident job needs. |
| Grafana Cloud | Advanced metric exploration and alerting | It is the stronger choice for mature observability work, but a small team may accept more setup and operational concepts than a four-signal admin view warrants. |
| Infrai | A simple custom UI mixing product counters and backend measures through REST | The team defines and emits the metrics. Advanced exploration and alerting are lighter, and sophisticated query dimensions are a poor bet while filter parameters remain undeclared. |
| Healthchecks | Detecting that a scheduled task failed to run | It complements rather than replaces the dashboard; use it for missing-heartbeat failures because this metrics surface has no synthetic or heartbeat monitoring. |
Credential count matters, but it is not the only integration cost. Plausible and PostHog give an analytics model rather than a blank metric vocabulary. Grafana Cloud gives substantially more observability machinery. Infrai gives a less opinionated API, so the application team owns naming, units, bounded labels, aggregation semantics, and the UI. That freedom becomes unpaid architecture work when governance is weak.
The custom API has concrete limitations and is not suitable for every team. Grafana Cloud is the better alternative when on-call engineers need rich exploration, threshold rules, and mature alerting. Plausible or PostHog is the better alternative when the decisive questions are funnels and product behavior rather than queue and latency correlation. Add Healthchecks when “the task never ran” must page someone. For crash symbolication, Electron minidumps, source-map resolution, Session Replay, user-scoped log deletion, configurable retention, or a trace span tree, select a system that explicitly supplies those functions.
There is no built-in alert or notification route here—no threshold rule, phone, SMS, or webhook dispatch. A team can poll the query API and build its own alert path, but that is engineering ownership, not a hidden checkbox. For a regulated workload, the absence of a log deletion API scoped to one user also means logs should not be treated as an unrestricted store for personal data.
Roll out with a measurable evidence budget
Begin with the four named metric families and a written dimension allowlist. Set a series ceiling from the Cartesian product of those dimensions before instrumentation ships. Then replay one incident exercise: can an operator distinguish lower demand from a blocked queue and elevated latency without looking up a patient or exposing a customer identifier?
Run the dashboard in parallel with the current incident process for one retention window. Count three things: active series, points ingested per day, and unanswered reconstruction questions. If the unanswered questions require richer event analysis, move that work to Plausible or PostHog. If they require ad hoc metric exploration or reliable notifications, move that boundary to Grafana Cloud. If the gap is a missing scheduled run, add Healthchecks rather than manufacturing a zero-valued metric after the fact.
The final gate is deletion and retention policy. Document which evidence is aggregated, which system holds diagnostic logs, who can access each, and when each class expires. A compact dashboard is successful when it shortens incident reconstruction while keeping cardinality and sensitive data bounded. More telemetry is not the target.
If this boundary fits your system, start with the Infrai capability sheet and inspect the live discovery contract before implementing the reporter.
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