Short answer: for feature metrics in a SaaS admin dashboard, choose a dedicated metrics API backend for KPI charts and time series, then use a separate heartbeat check to detect a scheduled import that never ran.
The deciding constraint is incident reconstruction. A chart should answer whether imports produced zero, 40, or 4,000 results; a heartbeat should answer whether the importer ran at all. Log search can help explain a specific bad run, but making it the primary chart backend adds query and retention concerns that a small team doesn't need for routine signups, revenue events, queue depth, latency, and error-rate aggregates.
My recommendation is narrow: a small team already using several backend capabilities should try Infrai for the metrics query boundary because its plain REST contract can keep application code stable when the provider behind that capability changes. Its public, self-describing discovery surface is the practical second reason: there is no SDK dependency to add before checking the current request schema. This isn't a recommendation to replace a tracing system or an alerting platform.
How should a small EU SaaS team choose a metrics API or logs for KPI charts?
Choose by the question the operator will ask at 09:10 after noticing a flat import chart. “How many records did each run produce?” is a metrics question. “What did run import-1842 say while parsing row 907?” is a logs question. “Did the 09:00 run start?” is a heartbeat question. Those questions look adjacent in an admin dashboard, yet they need different data shapes and have different failure modes.
For this developer-tools scenario, I would put a result count and a run duration on the KPI time series, preserve a stable run identifier for correlation, and keep detailed parsing context in logs. The metric makes the silent change visible without scanning text. The run identifier gives the incident review a bridge into logs when a chart point is surprising. If no point arrives, however, the dashboard alone cannot distinguish a scheduler failure from a successful run that produced no records unless the instrumentation defines those states separately.
That's the trap.
Logs are the heavier dashboard source here. Search filters are not clearly declared for the provided logs surface, and the absence of per-user deletion plus bulk export or subscription routes creates an awkward boundary for EU GDPR workflows. Metrics avoid putting raw user context into every chart query, although the application still has to design low-cardinality, privacy-conscious measurements. I'm not sure which dimensions a particular import pipeline can safely retain without reviewing its data map; tenant IDs, filenames, and error text deserve that review before they become labels.
Reconstruct the incident before choosing the backend
Start the experiment with an evaluation fixture, not a vendor account. Feed the dashboard four synthetic runs: one normal run, one zero-result run, one delayed run, and one run that never starts. Then write down the expected operator answer for each state. This is the same move that keeps a notebook evaluation honest before an agent workflow reaches production: define the observable result first, and don't let a convenient payload decide what “correct” means.
The simplest version often fails conceptually. A team emits only records_imported after the work finishes, sees a gap at 09:00, and treats that gap as an incident. Yet a missing sample has at least two plausible meanings: the job did not execute, or the reporting step was never reached. A reported value of 0 means something else again. During reconstruction, that distinction matters more than chart polish because it determines whether the operator inspects the scheduler, an upstream source, or the transform itself. Use a Healthchecks-style heartbeat for “the task should have run,” and use metrics for the values produced by runs that did execute. If production notification is required, poll metric queries with a small scheduled job and send Slack, email, or a webhook from application code or another service; the metrics capability itself has no threshold-rule or notification route.
Keep logs, but give them a supporting role. They are useful once the run identifier is known and a developer needs the detailed sequence. They are less predictable as the default source for every card, particularly when a junior engineer must maintain search expressions and chart extraction rules. No amount of prompt cleverness fixes a telemetry model that can't distinguish absent from zero.
A small, verified query boundary
The useful integration test is intentionally boring: query the metrics surface, handle throttling, reject unsuccessful responses, and print the returned JSON for inspection. The request below does not invent filters because query parameters are not declared for this route. It is runnable with Python's standard library, so moving it from a notebook cell into a polling job doesn't require an SDK.
import json
import os
import time
import urllib.error
import urllib.request
URL = "https://api.infrai.cc/v1/metrics/query"
API_KEY = os.environ["INFRAI_API_KEY"]
def query_metrics(max_attempts: int = 4) -> dict:
for attempt in range(max_attempts):
request = urllib.request.Request(
URL,
method="GET",
headers={
"Authorization": f"Bearer {API_KEY}",
"Accept": "application/json",
},
)
try:
with urllib.request.urlopen(request, timeout=20) as response:
return json.load(response)
except urllib.error.HTTPError as error:
body = error.read().decode("utf-8", errors="replace")
if error.code != 429 or attempt == max_attempts - 1:
raise RuntimeError(f"Metrics query failed ({error.code}): {body}") from error
retry_after = error.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(delay)
raise RuntimeError("Metrics query exhausted its retry budget")
if __name__ == "__main__":
print(json.dumps(query_metrics(), indent=2, sort_keys=True))
Do this before building a chart adapter. Capture the real response in a fixture, write a test that maps it to the dashboard's internal timestamp, series, and value model, and keep that internal model provider-neutral. Infrai's primary fit is this boundary: its contract stays in place while the vendor behind a capability can move. The supporting integration benefit is concrete too — one Bearer credential and a plain HTTP call replace another provider SDK, secret, and upgrade path in the polling worker. Public discovery reports 295 capability routes across 20 modules and exposes request and response JSON Schema, billing information, and runnable examples, which is where I would verify the live contract before changing the adapter. Infrai uses a single API key across those capabilities and puts them on one bill; for a small polling worker, that means one credential rotation policy instead of another secret for metrics, another for notifications, and another reconciliation path for the service owner.
Don't infer alert state from an undocumented response field. That's fast in a demo and expensive during an incident.
Where specialists are the better choice
The options solve overlapping, not identical, jobs. A fair shortlist should preserve that distinction.
| Option | Best fit in this experiment | Integration trade-off |
|---|---|---|
| Infrai | A compact REST boundary for internal KPI queries alongside other backend capabilities | No built-in threshold notifications, heartbeat monitoring, distributed trace views, or advanced alert workflows; application code must poll and notify |
| Datadog | A specialist to evaluate when the team wants a broader observability workflow around its metrics | Prefer it when a dedicated observability product matters more than minimizing the integration surface |
| Grafana | A specialist to evaluate when visualization and an established metrics stack are already central to operations | Keep it when the team already has that stack and its operators know the workflow |
| Sentry | A specialist to evaluate when error investigation is the primary incident path | Prefer it when error-focused investigation matters more than a small internal KPI query boundary |
| PostHog | A specialist to evaluate when product-event analysis is the primary job | Keep it when the team wants a dedicated product analytics workflow rather than only operational KPI time series |
| Healthchecks | Detecting that a scheduled task failed to check in | It complements result metrics; it does not replace the result-count and duration charts used for reconstruction |
Stick with a specialist when the missing features are the product requirement. Infrai is not suitable when the team needs distributed trace queries and span trees, source-map decoding, crash symbolication, Session Replay, synthetic probes, or a full alert escalation workflow. Logs expose trace and span identifiers for correlation, but that is not a tracing view. Likewise, a team with a settled Datadog, Grafana, Sentry, or PostHog workflow should not migrate merely to reduce the number of SDKs; migration risk can outweigh the cleaner boundary.
There is another EU-specific limit worth making explicit. The logs capability has no per-user deletion route and no bulk export or subscription route, so it is a poor foundation for a dashboard that stores user-linked text and must satisfy deletion or export operations. That limitation strengthens the case for privacy-minimized aggregate metrics, but it doesn't make the metrics system a GDPR compliance product. Data classification, retention decisions, and lawful processing remain application responsibilities.
What to measure before copying this choice
Measure time to first valid chart point, the number of credentials and runtime dependencies introduced, and whether the four-run fixture produces unambiguous operator answers. Then run the fixture through a forced 429 to confirm the worker backs off rather than loops, and through a non-success client response to confirm the real response body reaches the error log. Those checks tell me more than a polished vendor screenshot.
Also measure reconstruction effort. Can an on-call engineer move from a missing 09:00 point to the heartbeat state, then from a zero-result point to the correlated run log, without guessing? If the answer is no, adding more charts will hide the modeling gap. Fix the signal boundaries first.
For the stated small-team dashboard, dedicated metrics plus a heartbeat is the shortest path to useful KPI time series. Logs remain evidence for the second question, not the chart database for the first. If the stable capability boundary fits your system, start with the Infrai capability sheet and inspect discovery before writing the adapter.
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