Custom Metrics and Alerts (Prometheus / CloudWatch)

Imagine your online store losing orders, but CPU and memory metrics are normal. Where to look? We faced a case where a client complained about slow cart page loading. After implementing custom metrics, we discovered the payment gateway response time exceeded 5 seconds under load. Standard metrics didn't show this. Custom metrics are the only way to see real application behavior. Without them, you're blind: an incident can cost up to 10% of revenue per hour of downtime. This article covers which metrics to customize, how to set them up in Prometheus and CloudWatch, and how long integration takes. With over 5 years of monitoring experience and 50+ projects, we confirm: custom metrics save millions on incidents.

Which metrics to customize first?

Business metrics: orders created per minute, checkout funnel conversion, active user sessions. Technical metrics: task queue size, cache hit rate, specific operation execution time, error count by type. External dependencies: latency to third-party APIs, payment gateway availability, integration status. For example, conversion dropped from 3% to 1% — we would track the conversion_rate metric and alert. For quick issue identification, we also monitor the 99th percentile of response time and database error count (N+1 query).

How to set up custom metrics in Prometheus?

For instrumenting Python (FastAPI), use the prometheus_client library:

from prometheus_client import Counter, Histogram, Gauge
from prometheus_fastapi_instrumentator import Instrumentator

# Счётчик
order_counter = Counter(
    'orders_created_total',
    'Total orders created',
    ['status', 'payment_method']
)

# Гистограмма (для percentile)
checkout_duration = Histogram(
    'checkout_duration_seconds',
    'Time spent in checkout process',
    buckets=[0.1, 0.5, 1.0, 2.0, 5.0, 10.0]
)

# Gauge (текущее значение)
queue_size = Gauge(
    'task_queue_size',
    'Current size of processing queue'
)

# Использование в коде
async def create_order(order_data: dict):
    with checkout_duration.time():
        result = await process_order(order_data)
    
    order_counter.labels(
        status=result.status,
        payment_method=order_data['payment_method']
    ).inc()
    
    return result

Node.js (prom-client):

const client = require('prom-client')

const httpDuration = new client.Histogram({
  name: 'http_request_duration_ms',
  help: 'Duration of HTTP requests in ms',
  labelNames: ['method', 'route', 'code'],
  buckets: [1, 5, 15, 50, 100, 200, 500, 1000, 2000]
})

app.use((req, res, next) => {
  const end = httpDuration.startTimer()
  res.on('finish', () => {
    end({ method: req.method, route: req.route?.path, code: res.statusCode })
  })
  next()
})

After adding metrics, remember to expose them via the /metrics endpoint and configure scraping in prometheus.yml.

Prometheus vs CloudWatch: what to choose?

Prometheus allows collecting metrics at up to 10 ms intervals, 20 times faster than CloudWatch. However, CloudWatch is more convenient for AWS environments. For detailed study, refer to Prometheus documentation and CloudWatch documentation.

Prometheus Rules: Recording and Alerting

For fast dashboards and timely notifications, we configure recording and alerting rules. Recording rules precompute complex expressions (e.g., job:request_errors:rate5m), speeding up Grafana queries. Alerting rules trigger notifications when thresholds are exceeded. Example configuration:

groups:
  - name: app_slo
    interval: 30s
    rules:
      # Recording rule: precomputed error metric
      - record: job:request_errors:rate5m
        expr: rate(http_requests_total{status=~"5.."}[5m])
      
      # Alert: high error rate
      - alert: HighErrorRate
        expr: job:request_errors:rate5m > 0.05
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Error rate {{ $value | humanizePercentage }}"

Alertmanager then sends notifications to Slack, Telegram, or PagerDuty. This allows response in minutes, not hours.

How to set up CloudWatch Custom Metrics?

import boto3

cw = boto3.client('cloudwatch')

def put_metric(name: str, value: float, unit: str = 'Count', dimensions: dict = None):
    metric_data = {
        'MetricName': name,
        'Value': value,
        'Unit': unit
    }
    
    if dimensions:
        metric_data['Dimensions'] = [
            {'Name': k, 'Value': v} for k, v in dimensions.items()
        ]
    
    cw.put_metric_data(
        Namespace='MyApp/Business',
        MetricData=[metric_data]
    )

# Usage
put_metric('OrdersCreated', 1, 'Count', {'Environment': 'production'})
put_metric('CheckoutDuration', 0.85, 'Seconds', {'PaymentMethod': 'card'})
put_metric('QueueDepth', queue.size(), 'Count')

CloudWatch Alarm on a custom metric:

resource "aws_cloudwatch_metric_alarm" "queue_depth" {
  alarm_name          = "high-queue-depth"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = 3
  metric_name         = "QueueDepth"
  namespace           = "MyApp/Business"
  period              = 60
  statistic           = "Maximum"
  threshold           = 1000
  alarm_description   = "Task queue is backed up"
  
  dimensions = {
    Environment = "production"
  }
  
  alarm_actions = [aws_sns_topic.alerts.arn]
  ok_actions    = [aws_sns_topic.alerts.arn]
}

What's included in the work?

How we set up monitoring in 3-7 days

Our process: audit current metrics, select tools, write metric code, configure alerts, test, document, and train the team. You receive Grafana dashboards (or CloudWatch Dashboard), notifications in Slack/Telegram/email, and full documentation. We guarantee 99.9% SLA on correct metric operation. We can integrate with existing systems (PagerDuty, Opsgenie) if needed.

Step-by-step: adding your first custom metric

1. Install the prometheus_client library (Python) or prom-client (Node.js).
2. Create a metric of the required type (Counter, Histogram, Gauge).
3. Instrument the code: add metric calls at key points.
4. Expose the metric (e.g., via /metrics endpoint).
5. Configure metric scraping in Prometheus (add a job in prometheus.yml).

Checklist for setup:

• Prometheus installed or AWS CloudWatch access
• Instrumentation libraries
• Access to application code
• Alertmanager or SNS topic configured

Why custom metrics save budget

Standard metrics (CPU, RAM) don't show business indicators. Without custom metrics, you spend hours searching for nonexistent issues. One incident detected an hour late can cost more than a year of monitoring service. Custom metrics reduce mean time to detect (MTTD) from hours to minutes, reducing financial losses.

Order custom metric setup — gain full control over your application's performance and business indicators. Contact us for a free consultation — we'll evaluate your project in one day. Over 50 monitoring projects and 5+ years of experience guarantee results.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.