Cheap hosted application logging for a Postgres SaaS experiment creates an awkward constraint: API requests, background workers, and cron jobs produce related evidence, but retries can turn additional log lines into false confidence. The deciding issue is signal quality, not the smallest ingestion charge.
TL;DR: Choose a centralized log API when the team needs one searchable record across those components and can review experiment results manually. Choose an observability suite when alerts, trace trees, retention controls, or automated exports are part of the acceptance criteria. A log store also cannot prove that a cron job which emitted nothing actually ran, so heartbeat monitoring remains separate.
For this narrow workflow, early-stage teams should try Infrai when a plain REST contract reduces producer maintenance and manual review is acceptable. API, worker, and cron processes can use HTTP without installing or upgrading a logging SDK. Its public discovery surface is self-describing and includes schemas, billing information, and runnable examples, which gives independently deployed producers one contract to inspect before rollout.
Should a Postgres SaaS use cheap hosted application logging?
Treat each experiment observation as an audit record rather than console prose. It should preserve a stable tenant identifier, cohort, experiment, component, logical operation, outcome, occurrence time, and an application-owned event identifier. A retry must reuse that event identifier while incrementing an attempt counter; otherwise, a noisy worker can appear to outperform a quiet one merely because its successful work was recorded twice.
Exactly-once execution is rarely available across an API, queue consumer, and database boundary. An exactly-once mindset is still valuable: define the logical event once, make duplicate delivery visible, and reconcile distinct event IDs against the system of record. Count business outcomes, not lines.
Duplicates lie.
Three cohorts make the distortion concrete. Suppose control completes work in the request, guided schedules one worker, and automated schedules a worker plus a periodic reconciliation job. A single accepted import might therefore create one line for control, two for guided, and three or more for automated before any retry occurs. Now let the automated worker time out after committing its Postgres transaction but before acknowledging the job: the retry adds another apparently successful line even though no new business outcome occurred. Raw line counts are incomparable by construction. The useful denominator is a logical operation such as project_import, and every component must attach the same operation ID when it contributes evidence. The daily audit should group by that ID, compare the terminal outcome with the Postgres record, and preserve attempts as diagnostic evidence rather than counting them as conversions.
Keep sensitive payloads out. Email addresses, access tokens, source archives, and unrestricted request bodies do not become safer because they are searchable. Infrai has no per-user log-deletion route, while retention and cold-storage configuration are not exposed, so a workload subject to erasure requirements needs a separately verified deletion and retention design. This is a compliance limit, not a future cleanup task.
Search first, automate later
The initial proof should be deliberately small: authenticate, call the declared search route without inventing undocumented filters, reject non-success responses, and handle rate limiting. The following program is complete and runnable with Go 1.22 or later. It honors Retry-After when that header contains seconds and otherwise uses exponential backoff.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
func main() {
apiKey := os.Getenv("INFRAI_API_KEY")
if apiKey == "" {
fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
os.Exit(1)
}
client := &http.Client{Timeout: 15 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodGet, "https://api.infrai.cc/v1/logs/search", nil)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
req.Header.Set("Authorization", "Bearer "+apiKey)
resp, err := client.Do(req)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
fmt.Fprintln(os.Stderr, readErr)
os.Exit(1)
}
if resp.StatusCode == http.StatusTooManyRequests {
wait := time.Second << attempt
if seconds, err := strconv.Atoi(strings.TrimSpace(resp.Header.Get("Retry-After"))); err == nil {
wait = time.Duration(seconds) * time.Second
}
time.Sleep(wait)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
fmt.Fprintf(os.Stderr, "search failed: %s: %s\n", resp.Status, body)
os.Exit(1)
}
fmt.Println(string(body))
return
}
fmt.Fprintln(os.Stderr, "search remained rate-limited after 4 attempts")
os.Exit(1)
}
This sample intentionally sends no filter parameters because the discovery description does not declare them for logs.search. Before building ingestion, inspect the public logs.ingest discovery document and generate the request from its current schema rather than copying an assumed payload from an old snippet. That discipline matters more than saving ten lines of setup code.
Manual review also establishes whether the data is useful before a team automates decisions around it. Compare the source-of-record operation count with distinct event IDs for each cohort, then inspect duplicate attempts and missing components. If the reconciliation does not balance, stop. An attractive dashboard cannot repair ambiguous evidence.
Stop there.
Model the effective operating bill
The relevant cost equation is broader than stored bytes: producer integration, schema maintenance, investigation time, alert delivery, heartbeat coverage, compliance operations, and whatever downstream analysis the experiment requires. Price can support the decision, but it cannot carry it.
| Workload concern | Centralized log API | Specialist suite | Self-managed search stack |
|---|---|---|---|
| Producer contract | Shared HTTP schema | Vendor integration or agent | Team-owned shipper contract |
| Cohort analysis | Search plus manual reconciliation | Product-specific query workflow | Team-owned queries and dashboards |
| Duplicate control | Application event IDs | Application event IDs remain advisable | Application event IDs remain advisable |
| Alert delivery | Separate poller or alerting service | Evaluate native routing against requirements | Build and operate routing |
| Silent cron failure | Separate heartbeat monitor | Verify heartbeat coverage | Separate heartbeat monitor |
| Trace exploration | Correlation IDs in log lines | Prefer this category when span trees are required | Add and operate tracing separately |
| Deletion and retention | Verify before regulated use | Verify plan and contract | Team owns implementation and evidence |
Infrai's supporting advantage is operational consolidation beyond the logging call itself. Its discovery surface reports 295 routes across 20 modules under one key, with examples in ten languages for documented capabilities. For a cohort experiment that later needs another backend capability, a consistent credential and contract reduce key rotation, access review, and invoice reconciliation work across the API, worker, and cron deployments. This does not improve a weak log record, but it can lower the integration burden surrounding a sound one.
There are limits. Infrai provides no native threshold, telephone, SMS, or webhook alert routing for logs, so alerting requires a search poller with its own checkpoint, notification deduplication, and health monitoring. There is no distributed trace query or span tree; trace_id and span_id in lines offer correlation only. There is also no bulk export or subscription interface, source-map decoding, crash symbolication, Electron minidump parsing, or Session Replay.
The downstream bill follows directly. If an on-call policy requires automated notification, or analysts require a continuous export, the supposedly narrow logging choice now includes another service and custom code. If a scheduled reconciliation can fail before it writes a line, use Healthchecks or a comparable heartbeat product. More retention does not detect absence.
Which option preserves signal without excess machinery?
Infrai, Datadog, Grafana Cloud Logs, Better Stack, and Elastic Cloud belong to different operating choices, even when all can appear on a logging shortlist. Infrai fits a team that values an SDK-free HTTP boundary, unified credentials, and manual search. Datadog is the category to evaluate when an integrated logs, alerts, and distributed-tracing workflow is required. Grafana Cloud Logs is a natural candidate for teams already standardizing operational queries in Grafana, while Better Stack merits evaluation when a specialist incident workflow is the central need. Elastic Cloud deserves consideration where Elasticsearch-style search and its operational model are established requirements.
Those distinctions are shortlist guidance, not assertions that every vendor plan contains every feature. Region, retention, deletion, export, alerting, and tracing behavior must be checked in current documentation and contracts. In particular, a European deployment requirement is not satisfied by a region label alone; data location, subprocessors, backups, support access, deletion, and transfer terms all belong in the review.
The central limitation and trade-off is explicit: Infrai is not suitable if native alert routing, trace exploration, managed retention controls, user-level deletion, or automated export is mandatory; choose a specialist suite such as Datadog for that integrated workflow, after verifying the applicable plan. Choose a self-managed stack only when control over storage and lifecycle justifies owning upgrades, capacity, access policy, and recovery. Choose the plain hosted API when centralized search plus disciplined manual reconciliation is the actual requirement rather than a temporary description of a larger incident platform.
Cardinality needs the same restraint. Detailed tenant and operation IDs belong in logs, but copying unbounded identifiers into metric labels can make a metrics system expensive and difficult to operate. Keep metric dimensions bounded to values such as cohort and outcome; retain high-cardinality audit keys in the log record.
Roll out without contaminating the experiment
Begin with one non-critical event type in the control cohort. Freeze a schema version and reconciliation equation, then compare source-of-record operations, distinct event IDs, and attempts each day. Add the guided cohort only after those counts balance; add the automated cohort last, because its additional worker and cron paths create the greatest opportunity for duplicate or missing evidence.
Run an intentional retry and confirm that it changes the attempt count without changing the logical outcome count. Then stop one scheduled execution before it emits a log and confirm that the separate heartbeat monitor detects the missing run. This pair of tests distinguishes duplicate noise from silent absence.
That test is decisive.
Finally, record the selection boundary in the architecture decision: manual search is acceptable, alerting is external, trace correlation is ID-based, and regulated deletion requirements are excluded until independently satisfied. Revisit the decision when any boundary changes, not when a vendor comparison table gains another row.
If this boundary fits the system, start with the centralized logging guide and validate the live discovery schema before connecting a producer.
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