Custom Grafana Dashboards for Infrastructure Monitoring

Imagine your online store goes down, but the default Grafana dashboard shows only green graphs, hiding that Redis is full or N+1 queries in the database are killing P95. We've encountered this dozens of times. A custom dashboard solves this by designing panels for real incident scenarios. We develop dashboards that answer three key questions in seconds: is the service alive, where is the bottleneck, what to do? Experience shows a custom dashboard is 5x faster than default in incident search—MTTR drops from 40 to 15 minutes. Our track record: over 50 dashboards for projects of varying complexity, from startups to enterprise.

Why Default Dashboards Don't Solve Your Problems?

Community dashboards suffer from two problems: information noise and lack of context. CPU and memory panels per host are not service monitoring but hardware metrics. Your SRE doesn't look at CPU unless there's an incident. They need: 5xx errors, response latency, and status of external dependencies. Default dashboards don't provide this. For example, one of our clients—a food delivery service—after implementing a custom dashboard reduced average recovery time (MTTR) from 40 to 15 minutes.

How to Build a Dashboard That Answers Real Questions?

We use a metric pyramid: at the top—availability and errors, below—performance, then resources. Each panel is actionable. For instance, a metric "CPU 67%" is useless. We add a trend, target threshold, and scaling alert. This way the engineer doesn't guess but makes a decision. As a result, the team saves up to 8 hours per week on problem finding.

We embed dashboard variables: $__timeRange, $__interval, plus environment and instance variables. This allows viewing staging and production without cloning.

Which Metrics to Include in a Dashboard for Rapid Problem Finding?

It's important to select metrics that reflect user experience and service health. We recommend an SLI/SLO approach: define service level indicators (error rate, latency, throughput) and target values. The dashboard must include:

• Error Rate (5xx)—error percentage. Threshold: <1% for critical services.
• P95 Latency—delay for 95% of requests. Threshold depends on SLA.
• RPS—requests per second, needed for understanding peaks.
• Uptime—availability, checked via synthetic monitoring.
• Database metrics: active connections, query latency, slow queries.
• Cache metrics: hit rate, memory usage, evictions.

Example Dashboard Structure for a Web Application

Row 1: Service Health (big stat panels)
  [Error Rate %] [P95 Latency ms] [Uptime %] [Active Users]

Row 2: Traffic & Performance
  [RPS - time] [Response time P50/P95/P99 - time] [HTTP status breakdown]

Row 3: Infrastructure
  [CPU % per host] [Memory % per host] [Disk I/O] [Network I/O]

Row 4: Database
  [DB Connections active/max] [Query latency P95] [Slow queries count]

Row 5: Cache
  [Redis hit rate %] [Redis memory usage] [Evictions per sec]

Dashboard as Code: Dashboards in Git

Storing dashboards in UI chaos leads to losses. We use Dashboard as Code via Grafonnet or Terraform Grafana provider. Example in Jsonnet:

local grafana = import 'grafonnet/grafana.libsonnet';
local dashboard = grafana.dashboard;
local graphPanel = grafana.graphPanel;

dashboard.new(
  'Application Overview',
  time_from='now-1h',
  refresh='30s',
)
.addPanel(
  graphPanel.new(
    'Error Rate',
    datasource='Prometheus',
  )
  .addTarget(
    grafana.prometheus.target(
      'sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) * 100',
      legendFormat='Error Rate %'
    )
  ),
  gridPos={ x: 0, y: 0, w: 12, h: 8 }
)

This approach enables versioning, auditing, and reproducibility. Deployment annotations are added via CI/CD—each release is marked on graphs. A custom dashboard reduces downtime root cause search time by 5x, saving the team up to 8 hours per week.

Process

1. Audit current infrastructure and metrics
2. Design panels using a top-down approach
3. Build dashboards in Grafana with variables and annotations
4. Implement Dashboard as Code (Jsonnet/Terraform)
5. Documentation and team training
6. Guarantee: free adjustments within 30 days

Estimated Timelines

Experience and Guarantees

We have developed over 50 dashboards for projects of varying complexity—from startups to enterprise. Our engineers are certified in Grafana and experienced with Prometheus, VictoriaMetrics, InfluxDB. We guarantee that every dashboard answers three questions: "Is the service alive? Where is the problem? What to do?"

Order custom dashboard development—from design to team training. Get a consultation for your project. Contact us for a preliminary assessment.

Wikipedia: Grafana

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.