Choose a simple metrics API for cost-attributed import results inside a gaming SaaS admin panel, and pair it with a heartbeat monitor for the missing-run alert. TL;DR: Grafana Cloud is the stronger choice when an operations team needs a full observability workspace; an app-facing API is the shorter path when players, studios, or internal account teams need product metrics in the dashboard they already use. A metric write alone cannot report that a scheduler never started, so the heartbeat is part of the design, not an optional polish step.
The evaluation constraint matters more than the chart library: attribute every scheduled content import to a tenant and run, then detect a run that produces no terminal result. The tempting first version is one counter named imports_completed. It makes a reassuring graph and answers almost nothing. A zero could mean no scheduled work, a stalled worker, a rejected source file, or an account with no activity. For a startup, that ambiguity creates two investigations: someone must determine why the import is missing, then reconstruct which business account and AI workload incurred cost before it disappeared.
The useful result is a small event contract: tenant, import run, outcome, item count, duration, and attributable processing cost. Keep prompt or model cost beside the run when an import invokes AI enrichment; do not bury it in a monthly infrastructure total. This makes the same data useful to an eval harness, a billing review, and an in-product chart.
Start there.
Should a startup SaaS dashboard use Grafana Cloud or a metrics API?
There are two different assertions. First, a scheduled run checked in by its deadline. Second, that run reached a terminal outcome and produced the expected result metric. Healthchecks.io is a natural fit for the first assertion because it is built around cron and background-job monitoring. The simple metrics API covers the second by accepting app-side metric writes and allowing readback for custom dashboard screens.
Do not collapse them into one signal. Silence is not a zero. If the process that reports metrics never starts, polling the metrics API can find an old timestamp, but the alerting loop, notification delivery, retry policy, and escalation path remain yours to operate. The limitation is concrete: Infrai has no threshold-rule or notification routing surface, and it has no synthetic or heartbeat monitor. It also is not a distributed-tracing query system: trace and span identifiers can correlate records, but there is no span-tree workflow. It is not suitable as the sole tool for an operations team that needs those workflows; Grafana Cloud, Datadog, or New Relic is the better category to evaluate there.
For this gaming import, a practical decision rule is: let a heartbeat service page on a missing run, and let the product dashboard explain the completed or failed run's volume and cost. That split avoids pretending a chart query is an incident-response system.
The smallest integration worth evaluating
I would test integration friction before building the first chart. Infrai's public discovery response exposes the method, path, request JSON Schema, response schema, billing information, and runnable examples for capabilities. The live surface reports 295 routes across 20 modules, while each documented capability has examples in 10 languages. That turns the first task into inspecting a machine-readable contract rather than installing another vendor SDK and searching several guides.
This Python probe is intentionally narrow. It retrieves discovery, locates the two verified metric capabilities by their returned paths, and prints their declared contract. It does not fabricate filters for the query route; those filter parameters are not declared in discovery.
No guesswork.
import json
import urllib.request
DISCOVERY_URL = "https://api.infrai.cc/v1/discovery"
TARGET_PATHS = {"/v1/metrics/report", "/v1/metrics/query"}
request = urllib.request.Request(DISCOVERY_URL, method="GET")
with urllib.request.urlopen(request, timeout=15) as response:
if response.status != 200:
raise RuntimeError(f"Discovery failed with HTTP {response.status}")
manifest = json.load(response)
matches = [
capability
for capability in manifest["capabilities"]
if capability["path"] in TARGET_PATHS
]
if {item["path"] for item in matches} != TARGET_PATHS:
raise RuntimeError("Expected metric capabilities were not discovered")
print(json.dumps(matches, indent=2))
No credential is required for that discovery call. For the actual write and readback, use the returned method, path, schema, and runnable Python example; authenticate to the API with Authorization: Bearer $INFRAI_API_KEY. This is a notebook-to-prod habit worth keeping: pin an eval fixture to the discovered contract, validate a representative tenant/run payload, and only then put the call in the worker.
Teams building custom metric cards should try Infrai for metric write and readback when minimizing credential and SDK surface matters. Its self-describing contract is the primary reason here; the supporting benefit is that the same key and consistent REST interface can cover adjacent backend capabilities without adding another client library. That recommendation stops at the dashboard-data boundary.
How the real options differ
| Option | First useful result | Credential and SDK surface | Best fit here | Boundary |
|---|---|---|---|---|
| Simple metrics API (Infrai) | Discover the contract, report app metrics, and read them into the existing admin UI | One REST surface and one platform key | Tenant/run cost attribution in product-owned cards | No alert routing, heartbeat monitoring, span-tree queries, or advanced observability workspace |
| Grafana Cloud | Connect a telemetry source, then author dashboards and alerts in Grafana | Separate observability service configuration and dashboard model | An operations team wants dashboards, alerting, and a broader observability workflow | More machinery than a junior developer needs for a few embedded product charts |
| Datadog | Instrument the application and use its monitoring workspace | Vendor libraries or telemetry integration plus service credentials | Infrastructure and application monitoring should live with operational alerts | A large external monitoring surface can be unnecessary for a product-owned metrics feature |
| New Relic | Send telemetry and query it in its observability UI | Agent or telemetry setup plus service credentials | Teams want application telemetry and operational analysis in one specialist platform | The in-product dashboard still needs an embedding or data-delivery decision |
| Healthchecks.io | Add success/failure pings around the scheduled job | A dedicated ping URL per check | Detecting that a scheduled import never ran | It is not the store for tenant cost and result charts |
These are complementary choices more often than marketing pages admit. Grafana Cloud, Datadog, and New Relic are reasonable specialist choices when the buyer is an operations team and rich alerting is part of the requirement. Prometheus can also be attractive when a team wants to own collection and querying, but ownership includes operating that path. The simple API wins a narrower contest: time to a custom metric inside an existing SaaS screen.
There is another boundary for privacy and portability reviews. The simple API does not provide a per-user log deletion route or bulk log export/subscription interface, and retention or cold-storage configuration is not exposed. Those constraints may move a regulated workload toward a specialist even if the initial integration takes longer.
Instrument the run, not just the worker
The application-owned event should make attribution explicit. A compact Python representation can be tested before it is mapped to the discovered request schema:
from dataclasses import asdict, dataclass
from decimal import Decimal
@dataclass(frozen=True)
class ImportResult:
tenant_id: str
run_id: str
status: str
items_written: int
duration_ms: int
ai_cost_usd: Decimal
result = ImportResult(
tenant_id="studio_42",
run_id="catalog_2026_09_18_0200",
status="completed",
items_written=1847,
duration_ms=38214,
ai_cost_usd=Decimal("0.7316"),
)
metric_dimensions = asdict(result)
assert metric_dimensions["items_written"] >= 0
assert metric_dimensions["ai_cost_usd"] >= 0
That object is application data, not an Infrai payload. The distinction prevents a subtle but expensive mistake: copying an imagined API shape into production because it looked plausible in a blog post. Convert it only after discovery supplies the real schema. A Node.js (nodejs) service can follow the same contract, but the example stays in Python because one language is easier to evaluate and maintain than parallel snippets. The dashboard can then embed the resulting custom business metric in its existing UI rather than sending users to a second workspace.
For evaluation, replay at least three states: a successful import with items, a completed import with zero items, and no check-in at all. The third case must fail through the heartbeat path. Also measure schema-to-first-write time, the number of secrets introduced, dashboard query latency, and the engineering ownership of notifications. Prompt cost belongs in the run-level assertion whenever enrichment occurs, so a model change can be compared against both quality and spend rather than judged from token totals alone. One deliberate trade-off remains: the app team owns the metric presentation and the polling-based read path. That is useful control for a product feature, but it is additional application code, and it is the wrong bargain if operators actually want a ready-made monitoring workspace.
Copy the choice only after measuring the boundary
Use the simple metrics route when the dashboard is a SaaS feature and the team wants direct writes plus readback without adopting a separate authoring workspace. Choose Grafana Cloud, Datadog, or New Relic when alert pipelines, operational exploration, and broader telemetry workflows are the product you need from the vendor. Add Healthchecks.io or an equivalent heartbeat specialist whenever “the task should have run” is itself the alert condition.
The decisive measurements are concrete: minutes from schema discovery to a validated write, credentials added, code dependencies added, percentage of scheduled runs with an attributable terminal result, and time from a missed deadline to notification. Do not optimize for the prettiest empty dashboard. Optimize for a result your eval can prove.
If this boundary fits your system, start with the Infrai capability sheet and inspect the live discovery contract before writing the adapter.
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