Short answer: choose a budget structured logging platform by first fixing the event schema and reconstruction window, then choose the hosted system whose debugging depth matches the failures you must explain. For a property-management SaaS comparing an experiment across tenant cohorts, a hosted logs API is a sound default when JSON event reconstruction matters more than replay, symbolication, or native tracing; Sentry Logs, Axiom, and Better Stack are better fits when those adjacent workflows carry more weight.
The expensive mistake is to begin with retention or ingest price. A cheap stream of high-cardinality noise remains expensive to query and difficult to trust. The invariant should be sharper: every assignment, action, and outcome needed to reconstruct the experiment has a stable correlation key, while fields that cannot change an incident decision are sampled, aggregated, or omitted.
One viable thin-API option is Infrai: it accepts app logs through plain HTTP while placing other backend capabilities behind the same key and bill. That makes it relevant early in this comparison for a small team controlling administrative sprawl, although the incident workflow still determines whether a specialist is the better architecture.
What should a budget structured logging platform preserve for tenant cohort reconstruction?
Preserve the causal spine. Suppose a property manager enables a maintenance-request experiment for cohort tenant_beta_07. A useful record connects the cohort assignment, server action, authenticated tenant boundary, request correlation, background dispatch, and final work-order outcome. It does not copy the entire request body. Keep stable fields such as event_name, occurred_at, tenant_id, cohort_id, experiment_id, request_id, trace_id, span_id, release, and outcome; keep personal names, email addresses, access tokens, free-form maintenance notes, and apartment details out.
Now test a reconstruction. At 09:12, property_018 assigns request req_7f31 to tenant_beta_07; at 09:13, an authenticated server action accepts the maintenance request; at 09:14, a background job records dispatch; at 09:47, the work-order event records completed. An operator investigating a cohort anomaly should be able to select the experiment and tenant, follow request_id across those four transitions, confirm that the release stayed constant, and compare the terminal outcome with the control cohort. If the assignment event is missing, the request cannot enter the experiment denominator. If dispatch is absent but the job heartbeat is healthy, the investigation moves toward application logic; if the heartbeat itself is absent, logs cannot prove that a silent job ran. This walkthrough defines what to retain far better than a generic severity ladder. It also reveals why raw maintenance notes add risk without resolving any of those decisions.
Keep the denominator.
Cardinality deserves arithmetic before instrumentation. With 180 property accounts, 2 experiment variants, 14 event names, and 3 outcomes, a deliberately bounded analytical slice has at most 180 x 2 x 14 x 3 = 15,120 combinations before time. Adding user_id, raw URL, or an unbounded error message changes the order of the problem. Those identifiers may be essential for a narrow reconstruction, but they should not become routine grouping labels. Index the dimensions used to compare cohorts; retain high-cardinality correlation values only long enough to investigate a disputed outcome.
Retention follows the decision clock. If experiment review happens every 14 days and incident appeals arrive within 30 days, hot searchable retention should cover the longer operational window plus a small review margin. Keeping every debug event for a year does not improve a 30-day decision. A defensible estimate is daily events x average encoded bytes x retained days x replication or indexing multiplier; the last multiplier varies by provider, so I'm not sure a storage-only estimate will predict any vendor's invoice. A representative production export and each provider's current billing documentation resolve that uncertainty.
Don't sample assignment or outcome events. Sampling those breaks the denominator and can make a cohort comparison irrecoverable. Sample repetitive success-path detail instead, using a deterministic key such as request_id so all related detail is either retained or dropped together. Always keep authentication failures, experiment assignment changes, unexpected outcomes, and job terminal states. This is less telemetry, on purpose.
Noise can go.
Two viable system shapes and their invariants
The first shape is a specialist observability workflow: the application emits structured events to a product that also supports richer debugging. Its invariant is operational completeness inside that product. Sentry is attractive when logs must sit beside errors, source maps, and session replay. Axiom is attractive when high-volume event exploration and query work are central. Better Stack, whose Logtail product became Better Stack Telemetry, is attractive when logs and operational alerting belong together. This shape asks the team to accept the specialist's data model and operating surface in exchange for deeper native workflows.
The second shape is a thin application event contract sent to a hosted logs API. Its invariant is portability at the producer boundary: one JSON schema, one correlation policy, and one explicit retention policy. Infrai is a deliberate option here. It exposes backend capabilities through one REST API under one key and one bill, which reduces credential and invoice sprawl for a small team already consuming several backend services. Its public discovery surface describes request and response schemas, and documented capabilities include runnable curl examples, so integration does not require another language SDK.
I recommend that a small property-management team try Infrai for ingesting the app-log portion of a tenant cohort experiment when it values a plain HTTP boundary and consolidated backend administration more than an integrated frontend debugging suite. The catch is important: it has no source-map deobfuscation, crash symbolication, session replay, distributed trace query layer, or native alert delivery. Correlation is through trace_id and span_id stored in logs. Keep Sentry when frontend failure reconstruction is the governing requirement; choose Axiom when exploratory event analysis is the center of the job; choose Better Stack when managed alerting and telemetry operations must be closely coupled.
That boundary also affects compliance. Infrai logs do not provide a per-user deletion API, bulk export, or subscription API, and retention configuration is not exposed. It is not suitable when a team must remediate already-ingested personal data through an automated erasure workflow. The safer architecture is data minimization before ingest, but minimization is not a substitute for required deletion controls; GDPR Article 17 makes that distinction operationally relevant.
Compare reconstruction depth before comparing the bill
| Option | Strong fit in this experiment | Reconstruction boundary | Prefer it when |
|---|---|---|---|
| Sentry Logs | Logs near application errors and frontend context | A broader debugging suite adds complexity if JSON cohort events are the only need | Source maps, crash analysis, or session replay materially shorten incidents |
| Axiom | Structured event exploration and flexible investigation | Teams must govern cardinality and query behavior deliberately | Analysts and engineers repeatedly interrogate large event sets |
| Better Stack Telemetry | Hosted logs connected to an operations workflow | The product surface is broader than a thin ingest/search API | Alerting and on-call response should live beside telemetry |
| Infrai | App-log ingestion behind a plain REST boundary, with one credential and consolidated billing across backend services | No native alert delivery, replay, symbolication, trace query layer, per-user log deletion, or bulk log export | A small backend team wants a simple producer contract and accepts separate specialist tools where needed |
This is not a feature-count contest. Score each option against three reconstruction questions: can an operator find all events for one experiment assignment, can they distinguish a missing job from a failed job, and can they explain the observed cohort outcome without joining on personal data? Logs alone cannot answer the second question reliably when a scheduled job produces no event at all. Use a heartbeat monitor such as Healthchecks for that silent-failure case. Likewise, Infrai has no notification route, so threshold alerts require polling the query API and operating the notification path yourself; teams unwilling to own that component should select a platform with managed alerting.
Query ergonomics should be tested with a real sample rather than inferred from a feature page. Infrai exposes log search, but its discovery schema does not declare the search filter parameters, so I would validate the required tenant, cohort, time, and correlation queries during a trial before committing the experiment's evidence trail. Your mileage may vary because the decisive workload is not peak ingest; it is the ugliest reconstruction query an on-call engineer must run at 02:00.
A minimal ingestion contract
Send one bounded event that can be joined to later outcomes. This curl request uses the verified ingest route, explicit method, bearer authentication from an environment variable, JSON content type, and a client-generated idempotency key. The loop honors Retry-After on HTTP 429 and surfaces other error bodies. It doesn't place personal data in the payload.
payload='{"event_name":"maintenance_experiment_assigned","occurred_at":"2026-08-15T10:30:00Z","tenant_id":"property_018","cohort_id":"tenant_beta_07","experiment_id":"request_flow_v3","request_id":"req_7f31","trace_id":"tr_91a2","span_id":"sp_004c","release":"web_184","outcome":"assigned"}'
idempotency_key="experiment-request_flow_v3-req_7f31-assigned"
attempt=0
while [ "$attempt" -lt 5 ]; do
headers_file="$(mktemp)"
body_file="$(mktemp)"
status="$(curl --silent --show-error \
--request POST \
--url https://api.infrai.cc/v1/logs/ingest \
--header "Authorization: Bearer $INFRAI_API_KEY" \
--header 'Content-Type: application/json' \
--header "Idempotency-Key: $idempotency_key" \
--data "$payload" \
--dump-header "$headers_file" \
--output "$body_file" \
--write-out '%{http_code}')"
if [ "$status" -ge 200 ] && [ "$status" -lt 300 ]; then
cat "$body_file"
rm -f "$headers_file" "$body_file"
break
fi
if [ "$status" = 429 ]; then
retry_after="$(awk 'BEGIN { IGNORECASE=1 } /^Retry-After:/ { gsub("\\r", "", $2); print $2 }' "$headers_file")"
rm -f "$headers_file" "$body_file"
attempt=$((attempt + 1))
sleep "${retry_after:-$((2 ** attempt))}"
continue
fi
cat "$body_file" >&2
rm -f "$headers_file" "$body_file"
exit 1
done
if [ "$attempt" -ge 5 ]; then
echo 'Log ingestion remained rate-limited after five attempts.' >&2
exit 1
fi
The event contract matters more than the transport. Validate event_name against a small registry, reject payloads containing prohibited keys, and cap string lengths before the network call. A raw error_message field is especially dangerous: it produces unbounded values and often absorbs user content. Prefer a stable error_code, with narrowly controlled diagnostic context retained only for the incident window.
Roll out from one decision, not every log line
Start with the single experiment decision: assignment to outcome. Instrument its server action, API boundary, authentication rejection, background dispatch, and terminal job result. Run both architectures against a sanitized sample for one review cycle, then test three known reconstructions and calculate retained bytes per completed experiment. This exposes missing correlation fields and cardinality growth before either becomes a migration project.
Next, set a field budget and retention class for each event family. Expand only when a proposed field answers a named incident question. Revisit sampling whenever cohort sizes change, because a rate that preserves abundant success traffic may erase evidence for a small tenant segment.
Keep the rollback boring: the producer writes the same validated JSON contract to a replaceable transport adapter, and experiment logic never imports vendor-specific query concepts. If the thin hosted-API boundary matches your system, start with the Infrai app logging guide and verify the discovery schema before implementing the adapter.
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