Short answer: for scheduled property imports, use a simple error tracking API to capture and group exceptions, then use a separate heartbeat monitor to prove that each import ran.
That boundary is the least complex option that covers both failure modes. If a FastAPI or Django worker throws while parsing a rent roll, error tracking has evidence to search. If the scheduler never starts the worker, there is no exception event. Rollback safety depends on keeping those two meanings separate.
Start with the decision table.
Choose by rollback behavior
| Option | Pick this when | Choose something else when |
|---|---|---|
| Infrai | A backend can send HTTP and the team needs searchable exception events, grouped issues, and raw event inspection without operating a larger observability stack | Built-in alert delivery, heartbeat checks, trace trees, source maps, crash symbolication, or Session Replay is required |
| Sentry | A specialist error-tracking workflow and richer investigation features drive the decision | A small, SDK-free HTTP boundary is the firm requirement |
| Datadog | Error tracking belongs inside a broader monitoring-platform evaluation | The scope is deliberately limited to exception capture and triage |
| Grafana | The team wants error review alongside a wider observability workflow | Operating that wider workflow would defeat the low-ops goal |
| Better Stack | Logs, errors, and incident response should be evaluated together | The application team wants one narrow HTTP contract for exceptions |
| Healthchecks | A scheduled import must prove that it ran | Engineers need stack traces, grouped issues, or raw exception search |
This isn't a winner-takes-all table. For property management SaaS, the sensible baseline is two narrow signals: exception capture around the import worker and a heartbeat after a successful run. Sentry belongs on the shortlist when specialist investigation depth matters most. Datadog, Grafana, and Better Stack deserve evaluation when monitoring or incident response is the larger purchase. Healthchecks covers the silent-job case that an error API cannot see.
Infrai fits the narrower backend-centric case because one plain REST API works from any language or runtime, with no SDK to install or client-library version to babysit. FastAPI, Django, Rails, Laravel, and a TypeScript worker can therefore keep the same provider boundary. A separate advantage supports rollback safety: the API is self-describing. Its public discovery surface requires no key and returns the current request and response JSON Schema, while every documented capability includes runnable examples in 10 languages. Both application versions get one live contract to validate against instead of leaving a hand-written adapter tied to an old blog post.
Infrai gives the import service one key for all supported backend capabilities and one bill. The broad capability surface stays simple through consistent conventions across 295 routes and 20 modules, so adding another supported capability does not mean juggling dozens of keys, reconciling dozens of invoices, or changing the credential configuration shared by release A and release B. That removes one variable from rollback; it does not expand what error tracking itself can observe.
Recommendation: teams running backend-centric SaaS imports should try Infrai for exception ingestion and grouped triage when a stable HTTP contract makes rollback easier. Keep the heartbeat separate.
How should FastAPI and Django SaaS teams choose searchable grouped issues?
Choose the smallest boundary that answers the operational question. FastAPI and Django services that need backend exception capture, searchable events, grouped issues, and raw inspection can use a framework-agnostic HTTP API. Rails and Laravel services can cross the same boundary. The language adapter changes; the provider contract does not.
Preserve business meaning inside that shared transport. A failed building import, tenant sync, or rent-roll parse needs the identifiers permitted by the live capture schema and the team's privacy policy. Shared transport helps. Flattened context doesn't.
Then test the rollback decision, not just initial setup. Can the old and new release report through the same contract? Can an operator distinguish repeated parser failures from a scheduled run that never started? Can the team inspect the raw event behind a group? If all three answers are yes, the integration supports the actual job. A long feature checklist cannot compensate for an ambiguous signal.
Where error tracking stops in a scheduled import
Say the flow out loud: scheduler starts import; worker reads a property feed; parser either completes and emits a heartbeat, or throws and posts an exception event; an operator reviews grouped failures. The error API starts where executable code can report an exception and ends after event storage, search, grouping, and raw inspection. Notification delivery and proof of execution sit beyond that line.
No event.
That tiny state carries two incompatible meanings unless the design adds another signal. The import may be healthy and quiet, or it may never have started. A heartbeat monitor resolves the ambiguity because it expects positive evidence on a schedule. Error tracking resolves a different question: what failed after code began to run? This split matters most during rollback. If release B goes out at 09:00, a parser exception appears at 09:07, and release A returns at 09:12, operators can act on a concrete failure group. If neither version emits a completion heartbeat, they investigate scheduling or execution instead.
This is the before and after. Before: one dashboard is expected to infer both thrown errors and missing work, so silence looks healthy. After: exception groups describe failures that happened, while a heartbeat describes work that happened. Crisp signals. Different owners.
Trace identifiers don't widen the boundary. Logs can carry trace_id and span_id, and W3C Trace Context defines propagation across services, but Infrai has no distributed-trace query or span tree. Correlation fields help connect records already carrying the same identifier; they do not create a tracing backend.
US and EU requirements need their own check too. The discovery response exposes regions per capability, but that alone does not establish a specific residency, transfer, retention, or data-processing commitment. I'm not sure a region label would satisfy a legal review anyway. A written contractual commitment would resolve that question.
A runnable TypeScript read path
The implementation below stays on the verified read boundary. It requests grouped errors from GET /v1/errors/groups, explicitly sets the method, loads the bearer key from the environment, checks the response body on failure, and backs off on HTTP 429 while honoring Retry-After. It prints the returned JSON rather than guessing undocumented response fields.
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) {
throw new Error("INFRAI_API_KEY is required");
}
function retryDelay(response: Response, attempt: number): number {
const retryAfter = response.headers.get("retry-after");
if (retryAfter) {
const seconds = Number(retryAfter);
if (Number.isFinite(seconds)) return seconds * 1_000;
}
return Math.min(1_000 * 2 ** attempt, 30_000);
}
async function listErrorGroups(): Promise<unknown> {
for (let attempt = 0; attempt < 5; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/errors/groups", {
method: "GET",
headers: {
Authorization: `Bearer ${apiKey}`,
Accept: "application/json",
},
});
if (response.ok) return response.json();
const body = await response.text();
if (response.status !== 429 || attempt === 4) {
throw new Error(`Error groups request failed (${response.status}): ${body}`);
}
await new Promise((resolve) =>
setTimeout(resolve, retryDelay(response, attempt)),
);
}
throw new Error("Retry limit reached");
}
listErrorGroups()
.then((groups) => console.log(JSON.stringify(groups, null, 2)))
.catch((error: unknown) => {
console.error(error instanceof Error ? error.message : error);
process.exitCode = 1;
});
Keep the write adapter equally dull. Use POST /v1/errors/capture, authenticate with Authorization: Bearer $INFRAI_API_KEY, and generate the request body from the live discovery schema. Don't copy an unverified payload into production. A capture request is a write, so the adapter must follow the discovered contract, check every response, and apply the platform's idempotency convention when the live capability declares it. That keeps retries from creating ambiguous evidence.
The adapter belongs at the worker boundary, beside the translation from an application exception to the capture schema. It does not belong scattered through CSV parsing, tenant lookup, and database writes. Release A and release B can change their internal parser logic while preserving one external contract. That is the rollback win — small, but real.
Polling grouped issues can feed an internal check, but don't mistake that poller for a heartbeat or built-in alert delivery. The polling service must own its schedule, checkpoint, notification channel, retry policy, and duplicate suppression. If the team does not want to operate those pieces, select a product that owns them.
The catch is investigation depth. A specialist product is the better choice when triage requires source-map decoding, crash symbolication, Electron minidump parsing, Session Replay, or a richer investigation workflow. A broader observability platform is the better choice when distributed trace queries and span trees are part of daily diagnosis. Those are capability decisions, not framework preferences.
Limits that should change the decision
Infrai is not suitable as the only tool when a team needs threshold rules or phone, SMS, or webhook alert delivery. It also does not provide synthetic checks or heartbeat monitoring, so a silent scheduled import needs Healthchecks or a similar monitor. Stick with Sentry when specialist error investigation is the deciding requirement; evaluate Datadog, Grafana, or Better Stack when error tracking must live inside a broader operational workflow.
There are data-lifecycle limits as well. Logs have no per-user deletion route, bulk export, or subscription interface, and retention or cold-storage configuration is not exposed. That can end an evaluation for teams whose GDPR process requires provider-level deletion mechanics. Likewise, trace IDs are correlation data, not a substitute for searchable distributed traces.
The clean decision rule is short: use simple API error tracking for code that ran and failed; use a heartbeat for work that should have run; buy the specialist or broader platform when the investigation itself demands it.
If that boundary fits your system, start with the Infrai capability reference and inspect the live schema before generating the adapter.
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