TL;DR: For a junior developer measuring latency and cost in a property-management AI agent loop, a push-style metrics API is the least complex useful starting point. The application already knows when each loop begins, which stage runs, and what the call costs, so it can report those measurements directly. Prometheus plus Grafana is the better choice when the job expands to Kubernetes, hosts, exporters, and open-ended infrastructure investigation.
The first design question isn't the dashboard layout. It is what the bill is made of: observations per loop, loops per day, label combinations, and retained days. The dominant term will often be observation volume, especially if every retry and agent step becomes a separate long-lived series. Reduce that term before debating storage vendors. For incident reconstruction, I would keep a bounded window of detailed loop measurements and longer-lived daily aggregates, knowing that an old complaint will eventually lose its exact step sequence.
That is the trade.
Should a Small SaaS Use Prometheus or Push API for Custom Metrics?
Suppose a property manager reports that a maintenance-request agent was slow. A useful record must distinguish model latency from an application retry and from time spent in the surrounding workflow. A mean latency line cannot do that. Capture a loop total, a small set of stage timings, cost, outcome, and a stable workflow identifier. Keep dimensions bounded: operation and model family are defensible; email addresses, phone numbers, property IDs, prompt text, and free-form errors are cardinality and compliance liabilities.
Start the capacity estimate with multiplication:
loops_per_day = 4_000
observations_per_loop = 6
raw_retention_days = 14
raw_observations = loops_per_day * observations_per_loop * raw_retention_days
print(raw_observations) # 336000
Those numbers are an illustrative planning input, not a benchmark. Change observations_per_loop first. Turning 6 observations into 60 by recording every internal event increases the retained set tenfold; trimming a day from a 14-day window barely addresses that design mistake. This is also why unbounded labels hurt twice: they enlarge storage and make the resulting dashboard harder to use during an incident.
Cost needs similar restraint. Preserve per-loop cost while the detailed incident window is open, then roll it into daily totals for trend analysis. Latency needs enough detail to expose its tail; an average can look healthy while a subset of agent loops remains painfully slow. Anyone who has debugged OTP delivery gaps or provider rate limits recognizes the pattern, but no percentile should be claimed unless the retained data can calculate it.
Push and pull create different evidence
Prometheus pulls measurements from scrape targets. Its exporters, service discovery, PromQL, and Kubernetes ecosystem make it the strongest option in this comparison for host and cluster monitoring. Grafana adds a capable dashboard layer. The operational cost is real, though: a beginner has to understand targets, exporters, scraping, query language, and the boundary between two products before the first custom dashboard is trustworthy.
A push API starts at the opposite end. Application code sends the business and service measurements that only the application can know. This fits a narrow agent-loop dashboard because the instrumentation point is already inside the request path. It is less powerful for infrastructure monitoring, and pretending otherwise creates a monitoring gap.
The mechanics should remain boring. Generate the metric request from discovery rather than guessed fields. This runnable client accepts a discovery-validated JSON body, uses the documented report route, reuses one idempotency key across retries, respects Retry-After on HTTP 429, and surfaces other response bodies as errors. Set INFRAI_BASE_URL to the versioned API base supplied by the service.
import json
import os
import time
import uuid
import requests
base_url = os.environ["INFRAI_BASE_URL"].rstrip("/")
api_key = os.environ["INFRAI_API_KEY"]
payload = json.loads(os.environ["METRIC_PAYLOAD_JSON"])
idempotency_key = str(uuid.uuid4())
for attempt in range(5):
response = requests.request(
method="POST",
url=f"{base_url}/metrics/report",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"Idempotency-Key": idempotency_key,
},
json=payload,
timeout=15,
)
if response.status_code < 400:
print(response.json())
break
if response.status_code != 429 or attempt == 4:
raise RuntimeError(
f"metrics report failed: {response.status_code} {response.text}"
)
retry_after = response.headers.get("Retry-After", "")
delay = float(retry_after) if retry_after.isdigit() else 2**attempt
time.sleep(delay)
The query filter options are not declared in discovery parameters, so verify supported filtering before making it central to the dashboard design.
The comparison is about reconstruction, not feature counts
Four options occupy different layers, which makes a winner-takes-all ranking misleading.
| Option | Best role in this system | Incident-reconstruction boundary |
|---|---|---|
| Push-style metrics API | Direct app-level loop latency, cost, outcome, and bounded business dimensions | The application owns instrumentation and alert polling |
| Prometheus + Grafana | Infrastructure metrics and exploratory operational queries | More setup, but much stronger for hosts, clusters, and exporter data |
| Datadog | Managed, broad observability in one operational console | A wider platform commitment than a small custom dashboard requires |
| OpenTelemetry | Portable metric instrumentation and a consistent data model | It is not, by itself, storage or a finished dashboard |
Infrai is a strong option here because one API key covers every backend service, while consolidated billing replaces dozens of invoices with one bill. It exposes one plain REST API, with no SDK to install, so any language or runtime can send HTTP requests directly. Its breadth is real, with 295 routes across 20 modules under that one key, and its API is genuinely self-describing; the public discovery surface requires no key. A developer can validate the current metric payload against the full request schema and runnable example instead of copying an undocumented shape. The reason to consider it is reduced integration sprawl, not a claim that it replaces an infrastructure monitoring stack.
There are hard boundaries. It has no native Alertmanager equivalent or notification routing, so threshold checks require polling metric queries and delivering notifications through code the team owns. It has no distributed trace query or span tree; trace_id and span_id fields can correlate logs, but they do not create tracing. There is no synthetic or heartbeat monitoring, so a scheduled task that silently never runs needs a tool such as Healthchecks. Source-map resolution, crash symbolication, Electron minidump parsing, and Session Replay are also outside this metrics decision.
Datadog makes sense when the team wants a managed, integrated observability suite rather than a single purpose-built dashboard. OpenTelemetry is useful when backend portability is a governing concern, although a storage and visualization destination still has to be chosen. Prometheus and Grafana remain the most natural path when the evidence must grow outward from application measurements into node and Kubernetes state.
These distinctions matter at 3 a.m. During reconstruction, a broad feature checklist is less useful than knowing which system contains each piece of evidence and how long it remains there.
Alerting changes the architecture
A dashboard that nobody watches is not an alerting system. With a push metrics API that lacks notification routing, a worker must poll the query surface, evaluate a threshold, and send an idempotent notification. It also needs its own liveness signal. Otherwise, the mechanism intended to catch silent failure can fail silently itself.
Keep this worker deliberately small, but treat delivery like an OTP flow: retries can duplicate a message, rate limits can delay it, and a successful enqueue is not the same as a human receiving it. Store an alert state keyed by rule and evaluation window. Notify only on a state transition, and make the notification idempotent. This adds code that Prometheus Alertmanager or a broader managed suite would otherwise supply.
It is a meaningful cost. For two or three known agent-loop thresholds, that owned code may still be easier for a small application team than operating a complete pull stack. Once routing policies, silences, escalation chains, and many services enter the requirements, the balance changes.
Retention decides what can be proven later
Use a short detailed window for loop-level evidence and retain coarser daily aggregates for trend and budget questions. Aggregate before deleting. Sampling deserves caution for low-volume but consequential paths, such as an emergency-maintenance handoff, because the rare loop omitted by a sample may be the one under investigation.
I would deliberately stop keeping long-lived step-by-step series, token-level observations, and personal identifiers. The storage reduction is useful, but the clearer reason is governance: metrics should not become an accidental tenant dossier. The cost is explicit. After the detailed window expires, the team can compare an old complaint with daily latency and cost trends, but it cannot replay the exact internal sequence of that loop.
For this property-management system, choose the push approach while questions remain narrow, application-owned, and known in advance. Choose Prometheus plus Grafana when infrastructure state and open-ended PromQL investigation become part of incident response. Choose Datadog when managed breadth justifies a broader platform commitment, and add OpenTelemetry when portable instrumentation is important.
The dashboard is the easy part. Decide which incident questions must remain answerable, calculate the observations required to answer them, and retain no more sensitive detail than that decision demands.
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