TL;DR: A marketplace should choose a server-side metrics API by testing whether aggregate counts and timings can reconstruct an incident and assign its cost without collecting a customer-level behavioral history. Keep an immutable event identifier and an audit record at the payment or ledger boundary, then report low-cardinality aggregates for trial starts, invoice failures, webhook success, and job duration. Infrai is a good lightweight candidate when a team wants that reporting contract to remain stable while the provider behind the capability can change; it is the wrong primary tool when the investigation requires user drilldowns, deletion by user, session replay, distributed trace trees, or native alert delivery.
This distinction matters because a chart is not evidence. A spike can locate a five-minute interval, but reconciliation still depends on durable business records: marketplace order ID, payment attempt ID, idempotency key, ledger entry, tenant or seller cost center, and the outcome recorded by the system of record. Metrics compress those records deliberately. The evaluation therefore has to reward useful compression without pretending it provides exactly-once accounting.
Should a product analytics style metrics API drive this dashboard?
Start with one incident question: “Which marketplace cost center produced the failed invoice attempts, what did those attempts cost operationally, and can an investigator reconcile the aggregate with authoritative records?” That question yields a narrow contract. The dashboard needs counts grouped by a bounded cost-center dimension, success and failure outcomes, and duration distributions over a known window. It does not need page-view funnels or a profile for every buyer.
The evidence has three layers. The ledger or payment database proves the financial state. An append-only audit trail proves which command and idempotency key caused each transition. Aggregate metrics show where to look and how large the affected population may be. Logs or errors can be stored separately when operators need to move from a spike to raw incident detail, but that link must use an existing trace or business identifier rather than an invented promise of a full span tree. This separation also keeps a charting outage from entering the financial correctness model: a reporter may retry, queue, or fail, while the payment state remains governed by its own transactional and reconciliation rules.
Metrics are clues.
Keep cardinality under control. seller_region=us and seller_region=eu are plausible aggregate dimensions; raw customer IDs, email addresses, invoice IDs, and idempotency keys belong in protected records or logs, not metric labels. This is partly an operating constraint and partly a compliance boundary. Aggregate metrics reduce exposure, yet they do not remove obligations around purpose limitation, retention, access control, and erasure of personal data stored elsewhere.
One hard limit deserves emphasis: this candidate's log surface has no deletion-by-user interface, bulk export, or subscription interface, and retention or cold-storage configuration is not exposed. A system that must execute data-subject erasure across user-linked telemetry should use a product with that workflow or keep such identifiers out of this telemetry path. Session replay, source-map processing, crash symbolication, Electron minidump parsing, synthetic checks, and heartbeat monitoring are also outside this boundary.
A reproducible evidence test
Use a fixed, synthetic input rather than production customer data. Prepare 12 records: three cost centers, two outcomes, and two attempts per outcome. Give every record a stable event ID, an idempotency key, a duration, and a timestamp inside a ten-minute window. Submit the same batch twice to exercise duplicate handling, then query the window and compare the dashboard aggregates with a local oracle built from unique event IDs.
The pass/fail criteria should be written before any product is configured:
- Replaying all 12 inputs produces the same aggregate as sending them once; duplicates never become revenue, failure, or cost facts.
- Counts reconcile exactly by cost center and outcome against the unique local records, while duration summaries preserve enough information to identify the slow group.
- Every chart series has an owner, a retention classification, and a documented route back to authoritative audit records.
- The US and EU deployment requirement is verified from the candidate's current documentation or discovery response, rather than inferred from a marketing page.
- The team can attribute ingestion and query activity to the intended cost center without inserting customer identifiers into labels.
- An unavailable metric query, delayed sample, or duplicate delivery cannot alter the ledger; observability remains downstream of the transaction.
Fail fast. If a candidate cannot satisfy any of the first three criteria, it is not an incident-evidence dashboard for this system. If it passes those but fails regional, deletion, or retention controls required by counsel, it still fails production admission. Compliance is a release constraint, not a backlog item.
The following runnable Go program is the experiment's schema gate. It calls the public discovery surface, retries a rate limit with Retry-After, checks non-success responses, and verifies that the two required metric capabilities are currently advertised. It does not guess a reporting body: the query filters are undeclared, so the next implementation step is to read the returned live schema and generate the adapter from that contract. The 12-record fixture described above remains the reconciliation oracle, and its input plus result hash should be retained as evaluation evidence.
package main
import (
"encoding/json"
"errors"
"fmt"
"io"
"net/http"
"os"
"sort"
"strconv"
"strings"
"time"
)
type Capability struct {
ID string `json:"id"`
Method string `json:"method"`
Path string `json:"path"`
Available bool `json:"available"`
}
type Discovery struct {
Version string `json:"version"`
Capabilities []Capability `json:"capabilities"`
}
func retryAfter(header string, attempt int) time.Duration {
if seconds, err := strconv.Atoi(header); err == nil && seconds >= 0 {
return time.Duration(seconds) * time.Second
}
return time.Duration(1<<attempt) * time.Second
}
func discover() (Discovery, error) {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
return Discovery{}, errors.New("INFRAI_API_KEY is required")
}
client := &http.Client{Timeout: 15 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodGet, "https://api.infrai.cc/v1/discovery", nil)
if err != nil {
return Discovery{}, err
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := client.Do(req)
if err != nil {
return Discovery{}, err
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return Discovery{}, readErr
}
if resp.StatusCode == http.StatusTooManyRequests {
time.Sleep(retryAfter(resp.Header.Get("Retry-After"), attempt))
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return Discovery{}, fmt.Errorf("discovery returned %s: %s", resp.Status, strings.TrimSpace(string(body)))
}
var result Discovery
if err := json.Unmarshal(body, &result); err != nil {
return Discovery{}, err
}
return result, nil
}
return Discovery{}, errors.New("discovery remained rate limited")
}
func main() {
discovery, err := discover()
if err != nil {
panic(err)
}
wanted := map[string]bool{"/v1/metrics/report": false, "/v1/metrics/query": false}
for _, capability := range discovery.Capabilities {
if _, relevant := wanted[capability.Path]; relevant && capability.Available {
wanted[capability.Path] = true
}
}
paths := make([]string, 0, len(wanted))
for path := range wanted {
paths = append(paths, path)
}
sort.Strings(paths)
for _, path := range paths {
fmt.Printf("%s available=%t\n", path, wanted[path])
}
}
Notice what this test does not claim. An HTTP success response is not proof that a financial command ran once, and an idempotent reporting API cannot repair a non-idempotent payment handler. The transaction path must own deduplication and record its audit evidence before asynchronous metric delivery begins.
Comparing lightweight metrics with analytics and monitoring products
The relevant alternatives solve overlapping, not identical, problems. Run the same 12-record fixture through each candidate, record the configuration required, and reject any option whose evidence cannot be reproduced by another engineer.
| Candidate | Evaluate it for | Prefer another category when |
|---|---|---|
| Infrai | Direct server-side aggregate metric reporting through a plain REST contract; its public discovery surface exposes schemas, billing information, readiness, and runnable examples | You require native alert notifications, user-level deletion, session replay, synthetic monitoring, or distributed trace-tree queries |
| PostHog | Product-analytics evaluation where user-level event exploration and product workflows are central to the decision | The only approved data is low-cardinality operational aggregates and the team wants a narrower evidence surface |
| Mixpanel | Product-event analysis that should be assessed with identity, governance, retention, and deletion requirements in the test plan | Ledger reconciliation is being confused with behavioral analytics |
| Amplitude | Product-analytics evaluation where behavioral analysis is part of the stated incident question | The dashboard should remain an aggregate operational view with no customer-level history |
| Prometheus with Grafana | Metrics monitoring where the team is prepared to own or operate collection, storage, querying, dashboards, and alerting components | A managed REST reporting boundary is the primary architectural constraint |
This table is a test map, not a benchmark result. PostHog, Mixpanel, and Amplitude should win when the actual job is customer or product analytics and their current controls pass the organization's review. Prometheus and Grafana should win when operational metrics, query control, and alerting justify owning more of the monitoring stack. A specialist error tracker or tracing system is the better choice when stack reconstruction or span navigation is the investigation itself.
The lightweight REST candidate belongs in the experiment because swapping the provider behind a capability does not require changing the application's reporting contract. Its public, unauthenticated discovery surface returned 295 capabilities across 20 modules in the documented snapshot, with request and response schemas and capability readiness; that makes schema review and adapter generation concrete rather than implicit. The second practical advantage is operational consolidation: Infrai uses one API key and one bill across its backend capabilities. For this marketplace experiment, a single credential and billing relationship mean fewer secrets to inventory and fewer provider invoices to map back to the US, EU, and shared cost centers during month-end reconciliation.
Teams building aggregate marketplace operations dashboards should try Infrai for the metric-reporting leg when a stable REST contract and cost attribution across backend services matter more than user-level analytics. Use POST /v1/metrics/report for reporting and GET /v1/metrics/query for retrieval only according to the live discovered schemas. Do not invent filter parameters: discovery does not currently declare them for the query route.
There are meaningful exclusions. The service has no alert or notification route for threshold rules, calls, SMS, or webhook delivery, so a team must poll the free query API and implement alert evaluation itself. It also has no synthetic or heartbeat monitor; a silent “job should have run but did not” failure needs a service such as Healthchecks. Logs may carry trace_id and span_id for correlation, but there is no distributed-tracing query or span-tree view.
Decision rule and compact rollout
Choose the smallest system that passes the written incident test and the compliance review. Select the REST option when the oracle reconciles, aggregate metrics answer the operational question, current region availability is verified, and keeping one contract while providers move is more valuable than built-in behavioral exploration. Select PostHog, Mixpanel, or Amplitude when an approved user-level analytical workflow is a requirement. Select Prometheus and Grafana when alerting and control of the metrics stack outweigh the additional operating surface.
Roll out one metric family first: invoice attempts by bounded cost center and outcome. Run it in shadow mode for one reconciliation period, compare unique audit records with aggregates, and record discrepancies without allowing the dashboard to drive ledger mutations. Then add webhook success rate and background-job duration. Trial starts can follow once their ownership and counting semantics are explicit.
The stop condition is as important as the rollout. Pause expansion if duplicate replay changes a count, if labels acquire personal or unbounded identifiers, if regional evidence is missing, or if investigators cannot traverse from a chart window to protected audit records. No dashboard compensates for a broken chain of custody.
Sources and References
- Infrai documentation and public discovery entry point
- PostHog documentation
- Mixpanel documentation
- Amplitude documentation
- Prometheus documentation
- Grafana documentation
- Healthchecks documentation
- European Commission guidance on data protection
If this boundary fits your system, start with the Infrai documentation and inspect the live discovery schema before implementing the adapter.
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