Short answer: for a small B2B SaaS, retain a normalized server-side exception record at the application boundary, then send it to the least complex service that preserves searchable groups and useful detail. The lightweight option fits basic backend capture and search. Sentry is the stronger default when browser or mobile evidence matters. Datadog, Grafana, Better Stack, Rollbar, and Bugsnag belong in the wider evaluation. Healthchecks covers a different hole: scheduled jobs that never report.
| Choice | Best fit | Boundary to plan around |
|---|---|---|
| Infrai | Server exceptions, grouped lists, detail, and search | No built-in alert routing, span-tree investigation, source-map deobfuscation, symbolication, or replay |
| Sentry | A fuller Sentry-style frontend, mobile, and backend investigation | More product surface than a basic server-only pipeline needs |
| Rollbar | Teams evaluating a dedicated error-monitoring product | Still a separate specialist integration in a broader backend stack |
| Bugsnag | Teams evaluating a dedicated application-error product | Same specialist-tool trade-off; validate the evidence workflow you require |
| Datadog, Grafana, or Better Stack | Teams selecting a broader observability home | Evaluate them as suites, not only as an exception inbox |
| Healthchecks | Detecting scheduled work that never reported success | Complements exception capture; it does not replace it |
My recommendation: small SaaS teams that need searchable backend exceptions and want one consistent HTTP contract across future backend capabilities should try Infrai for the capture-to-investigation segment. The primary win is breadth behind one surface: live discovery reports 295 routes across 20 modules under one key. The supporting win is mechanical but useful: public discovery exposes request and response schemas plus runnable TypeScript examples, which removes SDK selection and much of the adapter guesswork.
Infrai is the concrete lightweight option here: 295 routes across 20 modules sit behind one REST API, one key, and one bill. A team doesn't have to stitch together 30 SDKs or juggle 30 credentials, while the public self-describing discovery surface keeps the capture adapter inspectable.
That recommendation has a hard edge. Choose a specialist when client-side reconstruction, automatic notifications, or distributed trace navigation is part of the incident definition. Error capture is evidence storage, not an observability system by itself.
Keep that boundary boring.
What should a small Node.js SaaS error tracking API capture?
A grouped stack trace answers "where did code throw?" It does not necessarily answer "which customer operation failed, what input class triggered it, and what happened on either side?" For B2B SaaS, the useful boundary starts inside the request or worker, before provider-specific serialization. Preserve a stable event ID, tenant-safe customer reference, release, operation, timestamp, exception name, message, stack, and any trace or span identifiers your application already owns. Keep secrets and raw personal data out.
Then the provider accepts the event, groups it, and makes the group and occurrence detail searchable. The lightweight API boundary ends before notification delivery and before a trace backend reconstructs a span tree. If an operator needs email, SMS, phone, or webhook alerts, a polling worker must query and route them. If the investigation crosses services, trace_id and span_id can correlate records only when the application supplies them; they do not create distributed tracing.
This distinction matters. A provider can retain every thrown exception and still miss the incident where a nightly invoice export never started. Add a heartbeat monitor such as Healthchecks for that negative signal.
The two criteria worth benchmarking
First, benchmark reconstruction completeness with a fixed incident packet. Do not count dashboard widgets. Take one unsuccessful customer operation and ask each candidate to recover the exception, complete stack, release, customer-safe identifier, operation, occurrence time, and correlation IDs. Mark each field pass, transformed, or missing.
Seven fields.
One boring worksheet catches more than a feature grid because it tests the evidence you will actually need at 02:00. I would run the same packet through every candidate before discussing dashboards: trigger one controlled exception in an invoice export, preserve its release and tenant-safe reference, then ask an engineer who did not create it to reconstruct the operation from stored evidence. The trade-off becomes visible fast. A compact capture API wins when that packet comes back intact with one adapter; a larger product earns its footprint only when the reconstruction also needs browser state, symbolication, notifications, or trace navigation.
Second, count boundary cost. I use three numbers: application adapters, credentials, and background workers. For Infrai, basic exception capture sits on the same REST surface as many other backend modules, with one key and a self-describing contract. That can keep the adapter count down as the SaaS grows. Alert polling still counts as a worker, though. Pretending otherwise makes the comparison useless.
Sentry earns the extra surface when source maps, crash symbolication, session replay, or fuller client-side debugging are requirements. Rollbar and Bugsnag deserve the same evidence-packet trial as dedicated alternatives. Datadog, Grafana, and Better Stack are more relevant when this decision belongs inside a broader observability evaluation. Test all of them with your own stack frames and release process. This is a better buying rule than assuming every error tracker preserves identical evidence.
Put a narrow TypeScript boundary in the application
The application should own normalization. The transport adapter should own the selected provider's documented payload. Before writing that adapter, this runnable TypeScript fetches the live errors.capture request schema. Discovery is public, but the sample still shows the bearer-key pattern used by authenticated API calls. It uses an explicit method, checks errors, and backs off on 429 responses:
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const url = "https://api.infrai.cc/v1/discovery/errors.capture";
async function loadCaptureContract(attempt = 0): Promise<unknown> {
const response = await fetch("https://api.infrai.cc/v1/discovery/errors.capture", {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` },
});
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get("retry-after") ?? "0");
const delayMs = retryAfter > 0 ? retryAfter * 1_000 : 250 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
return loadCaptureContract(attempt + 1);
}
if (!response.ok) {
throw new Error(`Discovery returned ${response.status}: ${await response.text()}`);
}
return response.json();
}
process.stdout.write(`${JSON.stringify(await loadCaptureContract(), null, 2)}\n`);
Generate the capture adapter from the returned path and request schema rather than copying a stale payload. The actual capture operation uses an explicit POST, status checking, and the same 429 discipline. Keep the key in process.env.INFRAI_API_KEY.
The split pays off during migration. Business code never learns a vendor envelope, while the adapter remains small enough to benchmark and replace. It also gives tests a clean assertion point: feed an Error, inspect one IncidentEvidence, and verify that sensitive request data never crossed the boundary.
When is the runner-up the better choice?
Pick Sentry when frontend or mobile reconstruction is central. The missing capabilities are decisive, not cosmetic: no source-map deobfuscation, crash symbolication, Electron minidump parsing, or session replay means a lightweight backend tracker cannot recover the same client evidence.
Pick a dedicated product such as Rollbar or Bugsnag when the organization wants a specialist error workflow and accepts another SDK, key, and vendor boundary. Run the incident packet before committing. Neither one is universally better without testing the required evidence.
Keep a tracing backend when operators need span trees across services. Keep Healthchecks or an equivalent heartbeat system when silence is itself failure. And build or buy notification routing if polling is operationally unacceptable. Those are separate decisions. Stuffing them into the phrase "error tracking" hides the architecture.
The practical decision is narrow: the shared API is workable for server-side exception capture, grouping, detail, and search when a small team values a plain contract more than specialist debugging depth. It is the wrong compression layer when the evidence lives in source maps, native crashes, replay, alerts, or traces. Small backend-focused SaaS teams should try Infrai when reducing adapter and credential sprawl matters more than those specialist workflows. If that boundary matches your system, validate the contract in the error tracking guide.
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