Metrics

The SDK offers two complementary metrics paths:

1. Prometheus RED/USE for HTTP (PrometheusMiddleware + make_prometheus_router) — listens to every request, increments http_requests_total + a latency histogram + http_requests_in_flight, and exposes it all on GET /metrics in Prometheus text format ready to be scraped by your Prometheus / Grafana / Datadog.
2. On-demand system snapshots (MetricsUtils) — collects CPU / memory / disk / NVIDIA GPU stats for a custom endpoint (internal debug page, /oncall, etc.). No built-in Prometheus exporter — the goal is the instant snapshot.

Use #1 in production always. Add #2 when you need to inspect the host where the app runs.

#1 Prometheus HTTP — [prometheus] extra

Install with [prometheus] (pulls prometheus-client). The middleware measures every request; the router serves the scrape endpoint.

uv add "tempest-fastapi-sdk[prometheus]>=0.32.0"

# src/api/app.py
from fastapi import FastAPI

from tempest_fastapi_sdk import (
    PrometheusMiddleware,
    make_prometheus_registry,
    make_prometheus_router,
)


def create_app() -> FastAPI:
    app = FastAPI(title="my-service")

    # Per-app registry — avoids collisions with other global prometheus-client users.
    registry = make_prometheus_registry()

    app.add_middleware(PrometheusMiddleware, registry=registry)
    app.include_router(make_prometheus_router(registry=registry))
    return app

Done. GET /metrics now returns something like:

# HELP http_requests_total Total HTTP requests
# TYPE http_requests_total counter
http_requests_total{method="GET",path="/api/users",status="200"} 142.0
http_requests_total{method="POST",path="/auth/signup",status="201"} 7.0
# HELP http_request_duration_seconds HTTP request latency
# TYPE http_request_duration_seconds histogram
http_request_duration_seconds_bucket{le="0.005",method="GET",path="/api/users"} 89.0
...
http_requests_in_flight{method="GET",path="/api/users"} 2.0

Default buckets (DEFAULT_LATENCY_BUCKETS) cover 5ms → 30s — fits typical APIs. Override with PrometheusMiddleware(registry=..., buckets=(0.001, 0.005, 0.025, 0.1, 0.5, 2, 10)) when your workload is more granular.

Path normalization

The path label uses the route template (/api/users/{user_id}), not the concrete path, so cardinality doesn't explode with unique UUIDs. That comes from FastAPI/Starlette — no config needed on your end.

Scrape config

prometheus.yml:

scrape_configs:
  - job_name: my-service
    metrics_path: /metrics
    static_configs:
      - targets: ["my-service:8000"]

Or via compose:

services:
  my-service:
    image: ...
    ports: ["8000:8000"]
  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports: ["9090:9090"]

#2 System snapshots — [metrics] extra

MetricsUtils collects CPU, memory, disk and NVIDIA GPU usage via psutil + pynvml. Every method has a sync and an async variant (the async wrapper runs the same code via asyncio.to_thread). GPU sampling gracefully degrades to [] when pynvml or NVIDIA drivers are missing.

Install with [metrics].

# src/api/routers/system.py
from typing import Any

from fastapi import APIRouter

from tempest_fastapi_sdk import MetricsUtils

router = APIRouter()


@router.get("/system-metrics")
async def system_metrics() -> dict[str, Any]:
    """JSON snapshot. NOT the Prometheus endpoint — that one is /metrics."""
    snapshot = await MetricsUtils.snapshot_async(disk_paths=["/", "/data"])
    return snapshot.to_dict()

Don't mount this at /metrics

This endpoint is not the Prometheus one — mounting it on the same path collides with make_prometheus_router when both are active. Use /system-metrics, /admin/sysinfo, or some restricted oncall prefix.

MetricsUtils.cpu(interval=...) blocks the event loop

The sync call spends interval seconds sampling — the cpu_async wrapper avoids the block by running in a thread. Always prefer MetricsUtils.snapshot_async() from handlers.

Individual collectors

snapshot = await MetricsUtils.snapshot_async(disk_paths=["/"])

print(snapshot.cpu.percent, snapshot.memory.percent)
for disk in snapshot.disks:
    print(disk.path, disk.percent)
for gpu in snapshot.gpus:
    print(gpu.name, gpu.utilization_percent, gpu.memory_used_bytes)

Individual collectors are also available: MetricsUtils.cpu(interval=...), MetricsUtils.memory(), MetricsUtils.disk(path), MetricsUtils.disks(paths), MetricsUtils.gpus() — plus their *_async variants. Each one returns a typed dataclass (CPUMetrics, MemoryMetrics, DiskMetrics, GPUMetrics, SystemMetrics) with a to_dict() helper for JSON serialization.