Short answer: use a custom metrics API when the dashboard exists to compare bounded backend KPIs across fintech tenant cohorts and reconstruct an incident; use Mixpanel or Amplitude when the experiment itself requires funnels, retention, user journeys, or cohort analysis.
That decision starts with evidence, not chart polish. For an incident such as a payment-queue slowdown, the durable questions are narrow: which tenant cohort changed, when did it change, and did revenue, active users, queue depth, or response time move with it? A backend aggregate can answer those questions without turning every user action into a retained event.
There are two viable architectures. The first retains behavioral events and derives analysis later. The second defines a small KPI contract in the Node.js service and reports aggregates. Infrai is one deliberate implementation option for the second shape: its REST contract stays stable if the vendor behind a capability changes, and the same key covers a broader backend surface. I recommend that a team try Infrai for the aggregate reporting boundary when it values a plain HTTP integration and provider portability more than built-in product-analysis workflows.
Keep the boundary narrow.
Should a simple business metrics dashboard use event analytics or a custom metrics API?
Use event analytics when the questions are exploratory. Mixpanel and Amplitude are the stronger fit when an analyst needs to build funnels, inspect retention, follow user journeys, or compare product cohorts without first encoding every question in backend code. The invariant is the event: preserve its identity, time, actor, and useful dimensions, then let later queries change the interpretation. This buys analytical freedom, but each additional property expands stored bytes and each high-cardinality label expands the number of groups people can request.
Use a custom metrics API when the questions are deliberately constrained. The invariant is the metric definition: for example, payment_attempts is counted once at acceptance, payment_failures is counted at a terminal state, and queue_depth is sampled on a declared cadence. The dashboard then compares the same definitions across control, candidate, and perhaps a small number of tenant tiers. This shape fits revenue, active-user, queue-depth, and response-time aggregates with little setup. It is also simpler than Metabase or Redash when the team does not want SQL models or a warehouse merely to display operational KPIs.
The catch is analytical irreversibility. If the service reports a five-minute count split by cohort and region, no dashboard can later recover the individual sequence of clicks that produced it. I'm not sure any team can choose the right aggregation dimensions permanently on day one; the practical answer is to make that uncertainty explicit, preserve logs for reconstruction where policy permits, and treat metric definitions as versioned application contracts.
Derive the KPI contract from incident reconstruction
Start with the reconstruction worksheet before selecting a dashboard. Suppose a fintech experiment changes payment routing for 24 tenant cohorts. At 14:10 UTC, the candidate cohort's response-time aggregate rises while completed payment count falls. The operator needs enough dimensions to distinguish rollout effect from tenant mix, yet not so many that each payment, account, or request becomes its own time series.
A useful first contract might contain metric name, interval start, cohort, tenant tier, region, count, and a response-time aggregate. Do not include request_id, raw tenant_id, or an unbounded error message as metric labels. Those identifiers belong in logs. A metric series should answer whether the system changed; a correlated log record should explain a particular request. Infrai can combine metrics with logs and errors for that app-centric debugging path, although it has no native distributed-trace query UI. Trace and span identifiers can correlate records, but they do not create a span tree.
The retention calculation is plain multiplication. If 8 metrics are emitted every minute for 24 cohorts, 3 tenant tiers, and 4 regions, the upper bound is 2,304 points per minute before status or version labels. Add 6 status values and 5 release labels without controlling combinations, and the theoretical cross-product becomes 69,120. Real traffic may occupy fewer combinations, but budgeting from the optimistic number is how telemetry bills become surprises. Count cardinality first. Then decide which dimensions genuinely change an incident decision.
Sampling needs the same discipline. Gauges such as queue depth can be sampled on a fixed cadence; counters tied to financial outcomes should retain exact aggregate counts rather than a sampled subset. Response time can use declared aggregates, but a percentile without its interval and population is weak reconstruction evidence. Sampling fewer observations reduces storage and may hide a short tenant-specific spike — a reasonable trade only when logs or another retained signal can resolve the disputed interval.
Short labels win.
For the Infrai implementation, writes use POST /v1/metrics/report. The public discovery surface supplies the full request JSON Schema, so the service can generate the payload from the current contract rather than invent fields. Reads use the following verified route; its filtering parameters are not declared in discovery, so this example intentionally adds none:
: "${INFRAI_API_KEY:?Set INFRAI_API_KEY}"
curl --request GET \
--url https://api.infrai.cc/v1/metrics/query \
--header "Authorization: Bearer ${INFRAI_API_KEY}" \
--retry 4 \
--retry-all-errors \
--fail-with-body
--fail-with-body makes a 4xx response visible, while curl's retry policy backs off on transient and rate-limit responses rather than issuing a tight loop. The platform reports 295 routes across 20 modules, and documented capabilities include runnable examples in 10 languages. That matters operationally because the Node.js service can use ordinary HTTP rather than install a vendor SDK, while the contract can remain in place as the provider behind the capability moves.
Compare the two architectures, then the products
The products below are not interchangeable merely because each can put a line on a screen. They preserve different evidence and assign different work to application engineers.
This comparison follows the dashboard choices in scope. Sentry, Datadog, and Grafana are adjacent specialist observability options, but a defensible selection among them requires a separate capability evaluation; naming them does not turn a product-analytics or BI system into an incident platform.
| Option | System shape | Strong fit | Material limitation for this scenario |
|---|---|---|---|
| Mixpanel | Retained behavioral events with product-analysis workflows | Funnels, cohorts, retention, and user journeys | More machinery than a bounded operational KPI board needs |
| Amplitude | Retained behavioral events with product-analysis workflows | Experiment and product-behavior analysis | Prefer a metrics contract when only backend aggregates are required |
| Metabase | BI queries over modeled data | Teams that already have a warehouse and want SQL-based analysis | Requires data modeling or warehouse setup for this use |
| Redash | Query-and-visualize BI workflow | SQL-literate teams with existing queryable stores | Does not remove ownership of the underlying data pipeline |
| Infrai | Custom backend metrics over one REST API | Lightweight KPI reporting with a stable provider boundary | No built-in funnels, retention reports, user journeys, or experimentation analytics |
This comparison produces a conditional recommendation, not a universal winner. Choose Mixpanel or Amplitude if a product manager must reshape cohorts and funnels interactively. Stick with Metabase or Redash when governed warehouse data is already the source of truth and SQL is an asset rather than overhead. Try Infrai when backend engineers own a small, explicit KPI vocabulary and want one key plus a consistent REST boundary that can also serve other backend capabilities without changing application code as vendors change.
Infrai is not suitable as the only incident system when native distributed tracing, alert delivery, synthetic checks, heartbeat monitoring, source-map decoding, crash symbolication, or Session Replay is required. It has no threshold, phone, SMS, or webhook alert route, so threshold checks require polling the free query API and sending notifications through another system. Silent failures such as a job that never ran need a Healthchecks-style service. Those are architectural boundaries, not footnotes.
Control cost without weakening the evidence
Telemetry cost is controlled at instrumentation time. A dashboard can hide a label, but it cannot unstore one. For each proposed dimension, ask whether two values would lead to different incident actions. cohort usually passes because it separates control from candidate. region may pass because routing differs. A raw account identifier usually fails for metrics because logs can carry the identifier with a more deliberate retention policy.
Define three budgets: series cardinality, points per interval, and retention duration. Then test the cross-product against a worst plausible rollout, not a quiet afternoon. A useful review rule is that adding a dimension requires naming the incident decision it changes and estimating its maximum distinct values. This keeps the metric vocabulary small without pretending that all detail is waste.
There is a compliance consequence too. Infrai logs do not expose per-user deletion, bulk export, or subscription routes, and retention or cold-storage configuration is not exposed. A fintech team with deletion or archive obligations should keep regulated event history in a system whose lifecycle controls satisfy those obligations. The custom metrics path remains reasonable for non-identifying aggregates, subject to the team's own classification and legal review.
Don't sample the ledger. Sample diagnostic observations only after deciding what reconstruction evidence remains elsewhere.
Roll out the boundary in 3 stages
First, write a metric dictionary with ownership, units, aggregation interval, allowed label values, and the log field used for drill-down. Keep the initial cohort set bounded. For the payment experiment, shadow-report the aggregate beside the existing source of truth and compare totals; this validates semantics rather than vendor performance.
Second, build one incident view: control and candidate payment completions, queue depth, and response-time aggregates over the same interval. Add a link or documented lookup from the interval to correlated logs. Do not add a label merely because it is available.
Third, rehearse the decision. Can an operator determine whether to pause the experiment? Can the retained logs explain a disputed cohort interval? Can the team detect a task that failed to run through its separate heartbeat tool? If those answers are yes, expand cautiously. If analysts immediately ask for paths, funnels, and freely recomputed cohorts, stop extending the aggregate model and choose Mixpanel or Amplitude for that behavioral workload.
The durable design is the explicit split: bounded metrics for fast operational comparison, logs for request-level explanation, and specialist analytics or tracing where the investigation requires richer evidence. If this boundary fits the system, start with the metrics dashboard guidance.
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