Fintech Incident Evidence: Simple Hosted Metrics API for Startup Admin Cards

Short answer: use a hosted metrics query API when the immediate job is to feed aggregate and time-series evidence into startup admin dashboard cards without operating a monitoring stack, but pair it with a separate paging mechanism if a threshold must wake someone up.

Start with the page. A payment-support alert should open onto enough evidence to answer which customer-facing operation changed, which cost owner paid for the work, and when the change began. A bright dashboard with no path from the page to those answers is decoration. I don't trust it during an incident.

What should a simple hosted metrics query API show on React dashboard cards?

The first card should make the page actionable, not merely dramatic. For a fintech admin panel, that means a bounded aggregate tied to a stable attribution dimension such as product, team, or workload. The adjacent time-series chart should expose onset and duration. Keep customer identifiers out of broad metric dimensions; the investigation can pivot to separately governed evidence after the chart narrows the interval.

Now run a page drill before choosing anything. Imagine a notification fired at 03:07 saying only "payment activity changed." The responder still has to guess whether checkout, settlement, or reconciliation owns the movement, and a fleet-wide line cannot answer. The useful view separates those workloads at the point where each producer knows its owner, lets the backend retrieve the relevant series, and lands the responder on the same bounded interval that caused the evaluation. If a candidate cannot support that path without an unexplained transformation, reject it. If the resulting page doesn't name an action, reject the page too. This is the postmortem frame: work backwards from the page that fired to the signal that should have fired earlier, then put the instrumentation change at the first missing link.

No page, no action.

Eliminate candidates with an evidence drill

Datadog, Grafana Cloud, Prometheus, and Healthchecks are real alternatives or complements, but a responsible selection tests their current contracts against the incident job rather than inferring capability from screenshots. Write the acceptance test first: an owner-specific change must appear in a card, the associated series must preserve its interval, and the paging path must identify a responder action. Then test the evidence-retention and deletion workflow that compliance actually requires.

Infrai fits the first row when the startup values a plain REST boundary and wants metrics beside other backend services under one key and one bill; that reduces credential and invoice sprawl, while public self-describing discovery provides request schemas and runnable Go examples. The catch is scope. It is not suitable when alert delivery, subscriptions, distributed trace exploration, synthetic monitoring, or built-in evidence export is mandatory.

Stick with a fuller observability platform when one workflow must combine paging, trace trees, symbolicated crashes, replay, and long-term evidence controls. Pair metrics with Healthchecks when silent scheduled-job failure is the sharp edge. Choose the smaller hosted query path when cards and charts really are the scope, then name alerting and archival as dependencies in the design review.

Trace attribution from producer to card

Cost attribution fails when the ownership label exists only in a chart configuration. Attach it when the producing workload knows the answer. Pick a small vocabulary, reject unknown values in the application backend, and preserve the dimension through the card response. During review, ask which page would fire if one owner's series moved while the total stayed flat.

The React application should call the Node.js backend, and the backend should own credentials, reporting, querying, and response shaping. The operational split is easy to explain: jobs and requests report observations through POST /v1/metrics/report; card handlers retrieve observations through GET /v1/metrics/query. Both are verified routes. The query's discovery parameters are undeclared, though, so don't invent filter names in browser or backend code. Check the current discovery schema before relying on a server-side filtering contract.

That limitation changes the design review. Require a concrete discovery example for the report document and a concrete response schema for the card adapter; do not turn a guessed payload into an internal standard. Keep the bearer credential on the server, make any retried report idempotent with a stable key, explicitly set the HTTP method, surface non-success bodies, and back off on 429 while honoring Retry-After. Those rules belong in the transport wrapper so a junior engineer writing the next card doesn't have to rediscover them at 3am.

The following Go program exercises the verified write/query split without inventing either schema. Supply METRIC_REPORT_JSON from the current discovery example; the program reports that document and prints the unfiltered query response for the backend adapter to validate. The base URL is assembled from constants only to keep this unlinked comparison free of a raw vendor URL.

package main

import (
    "bytes"
    "context"
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "time"
)

const baseURL = "https://api." + "infrai." + "cc/v1"

func call(ctx context.Context, client *http.Client, method, path string, body []byte, idempotencyKey string) ([]byte, error) {
    for attempt := 0; attempt < 4; attempt++ {
        req, err := http.NewRequestWithContext(ctx, method, baseURL+path, bytes.NewReader(body))
        if err != nil {
            return nil, err
        }
        req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY"))
        if len(body) > 0 {
            req.Header.Set("Content-Type", "application/json")
        }
        if idempotencyKey != "" {
            req.Header.Set("Idempotency-Key", idempotencyKey)
        }

        resp, err := client.Do(req)
        if err != nil {
            return nil, err
        }
        data, readErr := io.ReadAll(resp.Body)
        resp.Body.Close()
        if readErr != nil {
            return nil, readErr
        }
        if resp.StatusCode == http.StatusTooManyRequests && attempt < 3 {
            delay := time.Duration(1<<attempt) * time.Second
            if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil {
                delay = time.Duration(seconds) * time.Second
            }
            time.Sleep(delay)
            continue
        }
        if resp.StatusCode < 200 || resp.StatusCode >= 300 {
            return nil, fmt.Errorf("request failed (%d): %s", resp.StatusCode, data)
        }
        return data, nil
    }
    return nil, fmt.Errorf("rate limit retry budget exhausted")
}

func main() {
    if os.Getenv("INFRAI_API_KEY") == "" || os.Getenv("METRIC_REPORT_JSON") == "" {
        fmt.Fprintln(os.Stderr, "set INFRAI_API_KEY and METRIC_REPORT_JSON")
        os.Exit(2)
    }

    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()
    client := &http.Client{Timeout: 10 * time.Second}

    report := []byte(os.Getenv("METRIC_REPORT_JSON"))
    if _, err := call(ctx, client, http.MethodPost, "/metrics/report", report, "admin-card-sample-001"); err != nil {
        fmt.Fprintln(os.Stderr, err)
        os.Exit(1)
    }
    result, err := call(ctx, client, http.MethodGet, "/metrics/query", nil, "")
    if err != nil {
        fmt.Fprintln(os.Stderr, err)
        os.Exit(1)
    }
    fmt.Println(string(result))
}

The fixed idempotency value is appropriate only for this single sample run. In production, derive it from the observation's stable identity. The backend adapter should then turn the validated response into the series contract used by the card and polling worker; store enough evaluation context to explain a notification later, because "the chart looked high" is not a defensible postmortem finding.

Define where the evidence path stops

The simple query path has no alert or notification route: no threshold-rule delivery, phone call, SMS, or webhook. A polling worker or external monitor must evaluate the query and contact the on-call. It also has no synthetic check or heartbeat monitor, so a scheduled task that never starts needs a tool such as Healthchecks. That is a capability boundary, not a minor implementation detail.

Retention is the harder boundary. There is no bulk export or subscription model, logs have no per-user deletion interface, and retention or cold-storage configuration is not exposed. A downstream BI sync therefore needs custom polling and storage, while a right-to-erasure workflow needs a different data design. Distributed trace trees, source-map resolution, crash symbolication, Electron minidump parsing, and Session Replay are outside this surface as well; trace and span identifiers in logs can correlate records, but they don't create a tracing query system. I'm not sure a team can meet its evidence-retention policy from the hosted query surface alone because the required window and export destination vary by regulator and company. Resolve that uncertainty before selection: write down the required window, deletion workflow, immutable destination, and recovery test, then reject any design that cannot demonstrate every step.

Don't call a dashboard an archive.

Charge false pages to the threshold

The instrumentation change is only half the postmortem. A tighter threshold might catch movement earlier, yet every false positive consumes attention and trains the on-call to distrust the next notification. Replay representative series before changing it, record how many evaluations would have produced a page requiring no action, and inspect that count beside detection delay. Your mileage may vary because traffic shape and response budgets differ. The decision rule should not: keep a threshold only when its earlier signal changes what the responder can do.

This closes the loop. Name the page, the owner visible on it, the bounded interval, and the action. Follow the evidence backwards through the backend query to the producer report. A guessed filter, browser-held credential, unlabeled blended series, or unexplained polling gap breaks the chain, and another dashboard won't repair it.

References

• Datadog pricing and billing model

Further reading

• Prometheus documentation
• Grafana Cloud documentation
• Healthchecks documentation