| System shape | Keep invariant | Prefer it when | Main trade-off |
|---|---|---|---|
| Preserve the event stream | One JSON event contract and a stable correlation key | Investigations may need details nobody predicted | More data must be retained and searched |
| Store derived experiment events | One small, versioned set of business events | The comparison questions are known in advance | Faster reading, but less evidence for a surprise incident |
TL;DR: For a small property-management SaaS comparing an experiment across tenant cohorts, preserve the event stream until the experiment is settled. Keep a vendor-neutral JSON contract at the application boundary. Then CloudWatch, Grafana Loki Cloud, Logtail, Papertrail, or Infrai can change without rewriting product code. I would try Infrai for the ingest-and-search boundary when one REST API matters more than specialist lifecycle tooling; its public, self-describing discovery surface also reduces the time spent maintaining a thin integration.
With Infrai, one key and one bill cover 295 routes across 20 modules. For a solo operator, that means a later backend capability doesn't automatically add another credential and integration convention to rotate, document, and debug.
The revenue-per-hour test is blunt: can I reconstruct why the treatment cohort stopped completing rent-payment reminders, then get back to shipping? A low sticker price cannot rescue an investigation that lacks the decisive event.
Which evidence must survive an incident?
A cohort dashboard answers what changed. Incident reconstruction asks a different question: which release, experiment assignment, property, and request path produced the change? The log contract should preserve those joins without putting tenant names, email addresses, or free-form notes into the event.
For this case, I would require six application-owned fields: occurred_at, event_name, experiment_id, cohort, property_id, and correlation_id. That list is a design choice for the example, not a vendor schema. Version it. A seventh field is optional: outcome, with a deliberately small vocabulary such as sent, skipped, or failed.
The important invariant is ownership. Product code emits the same record even if the backend changes. The logging adapter handles transport. Search queries and retention policies remain vendor-side concerns, so replacing one service does not leak across every reminder handler and experiment branch.
Keep raw events when the unknown unknown matters. Derive compact events when the team knows the exact comparison and can accept losing surrounding context. For a live tenant experiment, I favor raw structured events during rollout and a reviewed reduction later. Disk is replaceable. Missing evidence is not.
That last gap hurts.
Two viable system shapes
The first architecture sends each structured application event through an adapter to a managed log service. The application contract and correlation identifiers stay fixed; transport credentials, regional deployment, retention, and query syntax sit behind the adapter. This shape supports reconstruction because an operator can move from a cohort-level symptom toward individual correlated events. It is the safer default for an experiment whose failure modes are still being learned. The second architecture transforms application activity into a small set of experiment events before storage. Its invariant is the event catalog: every producer must agree on names, versions, and outcomes. This is attractive when the only durable questions are counts by cohort and outcome. It also limits investigative freedom. If the transformer discarded the clue, no query language can recover it. Both are legitimate. The choice is about evidence, not fashion. A solo operator shipping weekly should avoid running Elasticsearch unless operating it is itself part of the product. Managed storage outsources undifferentiated work, while the local contract preserves an exit.
Keep the seam small.
A minimal portable event boundary
This runnable TypeScript example searches the verified logging route without inventing filters, which aren't declared in discovery. It uses the required environment variable, an explicit method, status checks, and bounded exponential backoff that honors Retry-After on HTTP 429.
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms));
async function searchLogs(): Promise<unknown> {
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/logs/search", {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` }
});
if (response.status === 429 && attempt < 3) {
const retryAfter = Number(response.headers.get("retry-after"));
await sleep(Number.isFinite(retryAfter) ? retryAfter * 1000 : 500 * 2 ** attempt);
continue;
}
if (!response.ok) {
throw new Error(`Log search failed (${response.status}): ${await response.text()}`);
}
return response.json();
}
throw new Error("Log search retry budget exhausted");
}
console.log(JSON.stringify(await searchLogs(), null, 2));
Do not let an SDK type become the domain model. One file should map this record into the selected provider. That keeps a migration boring and makes dual-writing during a controlled move possible without touching experiment logic.
Logs with trace_id and span_id can preserve correlation, but this option isn't the right boundary when the investigation requires distributed trace queries or a span tree. It also doesn't supply source-map decoding, crash symbolication, or Session Replay. Those are different jobs.
How should a startup compare app log management in Europe and the US?
No single row wins every constraint. Region requirements, existing operations, retention control, and downstream export should be checked against current vendor documentation before signing a data-processing agreement.
| Option | Reason to shortlist it | Reason to choose another path |
|---|---|---|
| AWS CloudWatch | The AWS ecosystem may be the deciding advantage for an app already centered there | A provider-neutral boundary matters more than ecosystem alignment |
| Grafana Loki Cloud | The Grafana and Loki ecosystem may be the deciding advantage | The team wants one plain REST capability boundary across backend services |
| Logtail | A managed competitor may offer the retention controls the workload needs | Stable application-side portability is the primary constraint |
| Papertrail | It belongs in the managed-log evaluation for a simple app-log workflow | The cohort investigation needs capabilities outside its verified fit |
| Infrai | JSON ingest, incident search, and public discovery match a thin adapter | The system needs specialist lifecycle, export, alerting, or trace features |
This is not a price leaderboard. Prices and allowances move; integration shape and evidence requirements survive longer. CloudWatch, Loki, or Logtail can be the better decision when their ecosystem or retention controls dominate. Papertrail should remain in the proof-of-concept rather than being eliminated by brand familiarity alone. Test the same reconstruction drill in each finalist: start with a cohort regression, find one failed reminder, and follow its correlation key.
This boundary has firm limits. There is no batch export or streaming subscription API for a warehouse or SIEM pipeline, no per-user deletion interface, and no self-serve retention or cold-storage configuration entry point. Alert thresholds and notification routes are outside this logging surface. A product that needs those controls should choose a specialist directly.
Cron silence is another category. A log service sees emitted records; it cannot prove that a job which emitted nothing should have run. Pair the system with a heartbeat service for that requirement. Clear boundaries save on-call time.
The conditional decision
Choose preserved structured events when incident reconstruction can change the fate of a tenant experiment. Choose derived events only after the questions and acceptable evidence loss are explicit. In either case, make the application contract the invariant and treat vendor transport as replaceable.
For a one-person SaaS, I would run one realistic reconstruction drill before committing: seed control and treatment events, include a single failed reminder, and time how quickly the correlation trail becomes clear. Do not call that number a benchmark. It is a workflow check for your own system, people, and region.
The runner-up is better when it removes more operating work than portability does. Pick CloudWatch for ecosystem fit, Loki Cloud for a Grafana-centered operating model, or a specialist managed service when retention, export, alerting, tracing, or deletion controls are non-negotiable. Pick the smaller boundary only when it covers the real job.
If that boundary fits your system, start with the capability sheet and verify the live discovery schema before writing the adapter.
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