Choose a straightforward error capture, grouping, and search service for a small API, but reject it if your incident process requires per-user deletion, bulk export, browser source-map reversal, or built-in paging.
TL;DR: For an e-commerce API, I would test five things with one deliberately broken checkout: capture, event inspection, grouping, search, and deletion. A simple service is enough when the job is reconstructing backend exceptions. It is the wrong foundation for privacy-heavy evidence handling or frontend-heavy debugging.
That distinction matters more than a feature count. During a customer incident, the useful question is not "did we collect an exception?" It is "can we connect the failed request, customer-safe context, stack, and related failures without wading through noise?"
How should you choose an error tracking service for an API?
Start with the reconstruction, not the vendor. A failed checkout needs a timestamp, environment, release, route, exception type, stack trace, and a correlation value shared with application logs. Record an internal incident-safe subject reference only if policy permits it. Do not casually turn an email address, shipping address, payment detail, or session token into an error tag.
The constraint that changes my choice is deletion. If the organization must locate and erase every log record tied to a person, an error service with no per-user log deletion API cannot satisfy that workflow by itself. A missing batch export or subscription interface also makes data portability and downstream evidence processing an architecture problem. GDPR basics are not a region label or a checkbox; they are lifecycle requirements that the whole system must meet.
Keep the payload narrow. More context can improve diagnosis, but it also increases privacy exposure and creates noisy groups when volatile values leak into messages. Normalize dynamic identifiers before capture, keep correlation IDs searchable, and put richer business evidence in a store with the retention and deletion controls the organization actually requires.
This is the first hard gate. Fail it, and stop evaluating developer experience.
No exceptions.
Build the smallest useful proof
I benchmark error tools by time-to-first-useful-incident, not time-to-first-200-response. The proof below calls the capture route directly. Its payload comes from a JSON environment variable because the verified material does not specify the capture schema; guessing field names would make the example look complete while teaching the wrong contract.
import crypto from "node:crypto";
const apiKey = process.env.INFRAI_API_KEY;
const apiBaseUrl = process.env.ERROR_API_BASE_URL;
const rawPayload = process.env.ERROR_EVENT_JSON;
if (!apiKey || !apiBaseUrl || !rawPayload) {
throw new Error("Set INFRAI_API_KEY, ERROR_API_BASE_URL, and ERROR_EVENT_JSON");
}
const payload: unknown = JSON.parse(rawPayload);
const idempotencyKey = crypto.randomUUID();
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch(
`${apiBaseUrl}/v1/errors/capture`,
{
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
"Idempotency-Key": idempotencyKey,
},
body: JSON.stringify(payload),
},
);
if (response.status === 429 && attempt < 3) {
const retryAfter = Number(response.headers.get("Retry-After"));
const delayMs = Number.isFinite(retryAfter)
? retryAfter * 1_000
: 2 ** attempt * 500;
await new Promise((resolve) => setTimeout(resolve, delayMs));
continue;
}
if (!response.ok) {
throw new Error(`Capture failed (${response.status}): ${await response.text()}`);
}
console.log(await response.json());
break;
}
Use the request schema returned by public discovery to build ERROR_EVENT_JSON. The same public discovery surface provides full request and response JSON Schema without a key, and every documented capability has runnable examples in 10 languages. That removes guesswork from the first call without forcing an SDK into the application.
Run one failure through each candidate, then inspect the individual event, its group, and a search for the correlation ID. Repeat with two order IDs and the same normalized failure. They should land in a useful group. Then change the exception type and confirm it does not disappear into that group. This is a tiny benchmark, but it exposes grouping quality faster than a polished dashboard tour.
Search deserves its own timer. Measure from opening the tool to finding the exact event from only the correlation ID, then repeat with route, release, and exception type. Five minutes of realistic retrieval tells me more about operational fit than fifty settings pages.
Use a concrete fixture. Send two checkout failures whose messages differ only by an order ID, one inventory failure with the same correlation ID, and one checkout failure from a second release. The first pair tests normalization and grouping. The inventory event tests whether search can reconstruct one customer-facing failure across application boundaries without merging unrelated exceptions. The release change shows whether a deploy dimension remains useful during triage. Next, search using only the correlation ID, then only the normalized message. Finally, attempt the deletion and export workflow your policy requires. Write down every manual join. A tool that captures all four events but makes the third one hard to find has high event volume and low signal quality, which is precisely the outcome this bake-off is meant to reject.
Measure it.
Compare the real options on their strongest axis
Sentry, Rollbar, and Bugsnag are real specialist error-monitoring products. They belong on the shortlist when error diagnostics are the main purchase, particularly when a team needs to investigate browser JavaScript rather than only backend exceptions. Their official documentation should be used to verify current SDK, grouping, source-map, privacy, export, and regional controls before selection; those details change.
Infrai provides one REST API and one key for 295 routes across 20 backend modules, with no SDK to install. Error capture, event inspection, group review, and search cover the basic backend loop. Public, keyless discovery returns full request and response schemas, so a CLI or SDK generator can inspect the contract before authentication. In this workflow, that means the error adapter uses the same authentication and HTTP conventions as other backend modules instead of adding another package, credential pattern, and client lifecycle.
The limitations are substantial. It is not suitable when the tracker itself must provide per-user log deletion, batch export or subscription, source-map reversal, alerts and notifications, a distributed trace query with a span tree, Session Replay, crash symbolication, Electron minidump parsing, or heartbeat monitoring. Choose a specialist instead when those workflows dominate. This is the central trade-off, not a footnote.
| Candidate | Put it on the shortlist when | Verify before committing |
|---|---|---|
| Sentry | Error diagnostics are a primary workflow, including frontend investigation | Current data controls, grouping behavior, export path, and regional setup |
| Rollbar | The team wants a dedicated error-monitoring workflow | Current deletion, export, grouping, and notification controls |
| Bugsnag | Release-oriented error investigation is central | Current source-map, privacy, export, and regional controls |
| Broad REST platform | A small backend team values capture and search plus a broad API surface under one contract | External alerting, privacy lifecycle, export, and frontend debugging needs |
This is not a ranking. It is a fit test. The specialists deserve a deeper trial for frontend production debugging because lack of source-map reversal is a decisive weakness there. The broad API surface is more compelling for backend operational bugs when low integration overhead matters.
Do not infer compliance from marketing language. Ask each vendor to demonstrate the exact deletion and export path against your event schema, retention policy, processor agreement, and deployment requirements. Legal review still applies.
Where does a simple service stop being simple?
It stops at the first required workflow it cannot own.
No built-in alerting means capture is passive. Polling a search API can bridge that gap, but then your team owns scheduling, deduplication, escalation, retries, and alert state. For serious on-call work, use a service with the required notifications or pair error capture with a proper alerting system. Do not call a polling loop "done."
Logs that carry trace_id and span_id can be correlated, yet fields are not a distributed tracing query or a span tree. If the checkout crosses inventory, payment, and fulfillment services, use a tracing system for causal analysis. Error grouping answers a different question.
Silent jobs are another boundary. An exception tracker sees a crash that happened; it cannot prove a scheduled settlement job ran when expected. Add a Healthchecks-style heartbeat monitor for "the task never started" failures. This catches a class of incident that stack traces cannot represent.
Noise is the final tax. Grouping reduces repeated events, but unstable messages, raw identifiers, and inconsistent release tags still sabotage it. Fix instrumentation before buying more retention.
What I would change at scale
At modest volume, the adapter and five-part bake-off are enough. At scale, I would split operational error evidence from privacy-governed customer records, define retention by data class, and route correlation through an organization-wide convention. I would also add an independent log pipeline and distributed tracing where cross-service reconstruction is required.
Then I would test failure behavior: HTTP 429 backoff, dropped-network retries, ingestion latency, malformed payload rejection, and search lag. Any write retry must be idempotent where the service supports it. Dashboards look fine when everything works. Incidents do not happen then.
The decision rule stays blunt: choose the smallest system that reconstructs the incident and meets the evidence lifecycle. Pick a specialist when frontend decoding, rich alerting, or dedicated error workflows dominate. Pick the broader REST surface when backend capture and search are sufficient and reducing SDK, key, and integration sprawl has real value. If per-user deletion or bulk portability is mandatory, add architecture that supplies it or choose a platform that can demonstrate it directly.
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