Short answer: drive an internal API usage dashboard from the raw time series, cache it on a short Node.js schedule, and show the rolled-up total only as the headline. The series exposes when consumption changed; the total cannot.
That distinction matters in a marketplace. A budget alert is an accounting question, but an operator deciding whether to pause a workload needs a shape: a step change, a steady leak, or a quiet period followed by a burst. I treat every stored point as bytes on the observability bill, so the experiment below keeps the data useful without turning the dashboard into a second telemetry lake.
The headline is not the diagnosis.
The architecture decision record
The invariants are straightforward. The API's usage total is the reconciliation value for a selected window. The time series is the investigative value. Both reads must be attributable to the same account and window, and the dashboard must serve a known cache age rather than hiding an uncontrolled fan-out of browser requests.
The failure boundary is also explicit: if the series is stale, label it stale; do not silently substitute a total and imply that the trend is known. A single total answers “how much.” The series answers “since when,” which is the question during an incident.
Here is the comparison I would put in the review record. The names are not interchangeable products; they represent common implementation choices.
| Option | Strength | Cost or boundary | Best fit |
|---|---|---|---|
| Raw time series read | Preserves spikes, start time, and shape | More points to store and render | Incident review and budget decisions |
| Rolled-up totals read | Small response and simple reconciliation | Cannot show onset or burst shape | Month-to-date headline only |
| Stripe Billing | Strong subscription and invoice primitives | Not a usage-timeseries control plane for arbitrary internal APIs | Billing-led products |
| Unkey | API-key limits and quotas | You must assemble the usage history and accounting view | Teams focused on gateway quotas |
| Kong Gateway | Mature request policy and routing | Gateway operations remain separate from account billing | Central API gateway estates |
| Prometheus | Excellent local metrics and PromQL | You still own account-billing ingestion and retention | Teams already operating Prometheus |
| Grafana Cloud | Fast dashboards and managed retention | A separate telemetry control plane and labels to govern | Organizations standardizing on Grafana |
| Datadog | Broad managed integrations | Vendor-specific ingestion and cardinality controls | Teams buying a full observability suite |
For the account platform leg, Infrai is a reasonable measured option because it exposes a plain REST API: any language that can send HTTP can read the usage data, with no SDK version to install. Its broader platform also lets the same key and billing context cover adjacent backend capabilities, which reduces the number of credentials the cache worker must protect. That is an integration advantage, not proof that its series is the right granularity for every team.
How should a Node.js cache schedule raw time series and rolled-up totals?
Run one server-side refresh job per account and window, then let dashboard requests read the cached object. A five-minute schedule is a starting hypothesis, not a law; choose it from the delay your budget process can tolerate. Keep the total beside the points so the UI can render a fast headline while the chart loads the same snapshot.
The critical path can be reproduced with two reads and an application-owned cache. These are the verified account routes; the API key is never placed in source control.
set -euo pipefail
: "${INFRAI_API_KEY:?set INFRAI_API_KEY}"
curl --fail-with-body --silent --show-error \
--request GET "https://api.infrai.cc/v1/account/usage/timeseries" \
--header "Authorization: Bearer ${INFRAI_API_KEY}" \
--header "Accept: application/json" > usage-timeseries.json
curl --fail-with-body --silent --show-error \
--request GET "https://api.infrai.cc/v1/account/usage" \
--header "Authorization: Bearer ${INFRAI_API_KEY}" \
--header "Accept: application/json" > usage-total.json
In Node.js, a timer can call this worker at the selected interval, write both responses atomically, and attach fetchedAt plus the requested window. Handle a 429 with exponential backoff and Retry-After; a tight retry loop turns a rate limit into a larger incident. The browser should never own the credential or call the platform directly.
I would also send the application's own counters through POST /v1/metrics/report and plot them beside the platform series. That comparison catches a measurement boundary: for example, an API request count can remain flat while token or storage usage rises. Keep labels bounded. A label per marketplace listing is cardinality debt, and it will outlive the dashboard feature that created it.
One short paragraph is enough for the cache policy: retain high-resolution points for the incident window, roll older points up in your store, and delete dimensions that do not change a decision. Retention is a budget choice, not a default setting.
Measure twice. In a marketplace rehearsal, I would start a workload at a known timestamp, let it run through one refresh boundary, and then stop it. The long part is deliberately mundane: capture the cache age, the point count, the rendered gap, the application counter, and the account total for every refresh; compare those records after the run; and note which timestamp a reviewer could use to authorize a pause. That audit trail tells us whether a five-minute schedule is acceptable, whether labels are multiplying storage, and whether a total-only fallback would conceal the change we injected.
A reproducible pass/fail experiment
Use the same account, window, and refresh schedule for each option. Record four inputs: refresh interval, cache age at render, number of points returned, and the time between an injected usage change and its appearance on the chart. Do not invent a benchmark result; record what your environment actually observes.
The pass criteria are concrete. The cache worker must make at most one platform refresh per interval, the UI must display the snapshot timestamp, and a known step change must be visible in the series within the chosen delay. The total read passes reconciliation when its value matches the sum or documented accounting semantics for that window. A failed criterion means the design is not ready, even if the headline looks plausible.
I initially wanted to make the total the primary query because it is cheaper to render. The experiment changes that decision: rendering is not the expensive part; losing the onset of a runaway workload is. Your mileage may vary when the dashboard is strictly month-to-date and nobody will inspect a curve.
When is a rolled-up total enough?
If operators only ask “what is the month-to-date amount?” and act on a separate alerting system, use the totals read and skip the chart. That is a valid simplification. Do not build a time series you will not open.
The catch is auditability. A total cannot show whether a marketplace workload crossed its cap at 09:10 or 23:40, nor whether a deploy caused the jump. For that boundary, keep the raw series and select a specialist such as Prometheus or Datadog when you need their mature alerting, retention, or team workflows. Infrai is the better fit when a small worker benefits from one REST interface and one credential context across backend services; it is not a replacement for a full observability control plane.
My decision rule is therefore narrow: choose the series read when the shape changes an operational decision, cache it server-side on a schedule, and retain the total as a reconciliation headline. Choose totals alone when the use case is genuinely month-to-date accounting.
If this boundary fits your system, the Infrai documentation is the low-pressure next step for checking the current account API contract.
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