Reduce AWS Costs: Reserved Instances and Savings Plans in Practice

We often see clients paying 1.5–2 times more for AWS resources than necessary. The root cause: no usage analysis and buying Reserved Instances or Savings Plans "by eye." Over 5 years of AWS work, we have executed 50+ optimizations and know how to save 30%–60% without sacrificing performance. According to AWS documentation, Savings Plans can reduce costs up to 72% compared to on-demand.

How to Choose Between Reserved Instances and Savings Plans?

Reserved Instances (RI) commit you to a specific instance type in a region/AZ. Maximum discount with minimal flexibility. Savings Plans (SP) commit you to a certain hourly spend. They flexibly apply to EC2, Fargate, and Lambda. Lower discount, more freedom. SP offer better flexibility than RI, but if your workload is stable, RI is more cost-effective.

Why Baseline Analysis is Critical?

Buying without analysis is risky. Common mistake: purchase RI for 20 instances, then scale down to 5. Unused RI still incur charges. Correct approach:

• Collect metrics for at least 2–4 weeks using AWS Cost Explorer.
• Determine the baseline—the number of instances running 24/7.
• Cover only the baseline with RI/SP; leave the rest on-demand or Spot.

Example: for 10 constantly running m6i.xlarge instances, the baseline is 10. Savings Plans on that amount yield roughly $1,200 monthly savings.

def calculate_savings_plan_commitment(
    instance_hours_per_day: dict,
    on_demand_rates: dict,
    savings_plan_discount: float = 0.32
) -> float:
    total_on_demand_per_hour = sum(
        hours / 24 * on_demand_rates[instance_type]
        for instance_type, hours in instance_hours_per_day.items()
    )
    return total_on_demand_per_hour * 0.80 * (1 - savings_plan_discount)

Which Payment Type to Choose for Reserved Instances?

Payment type affects discount and cash flow:

Term: 1 or 3 years. Three-year terms offer significantly higher discounts but require confidence in long-term architecture.

Compute Savings Plans: Practical Example

Baseline: 10 m6i.xlarge instances in us-east-1 running constantly. On-demand price: $0.384/hour. Commitment via Compute Savings Plans: ~$0.261/hour, which is 32% less than on-demand. The actual discount ranges from 20% to 50% depending on commitment level.

RDS Reserved Instances

For databases, RI are especially beneficial—databases run 24/7, so paying on-demand makes no sense. RDS Multi-AZ with a 1-year RI yields a 30–40% discount; ElastiCache Reserved Nodes give 30–50%. Important: RDS RI do not transfer between instance families.

Marketplace for Unused RI

If your infrastructure changes and you have unused RI, sell them on the AWS Marketplace for Reserved Instances. The selling price is usually lower than on-demand but helps recover part of the cost.

Regular Review

Quarterly:

• Check utilization of purchased RI/SP (target > 90%).
• Assess architectural changes expected in the next 12 months.
• Decide on renewal or modification of commitments.

What's Included in the Work

1. Usage audit: collect metrics for 4 weeks, identify baseline.
2. Recommendations: choose RI/SP type, optimal combination, and term.
3. Implementation: purchase via console or API, configure options.
4. Monitoring: track utilization, set alerts for drops below 90%.
5. Savings report: transparent with calculations.

How We Do It

Our process includes: analysis of current usage (4 weeks of metrics), baseline determination, selection of optimal RI/SP type, purchase, and effectiveness monitoring. We guarantee savings start from the first month. To reduce your AWS costs, contact us—we will conduct a free preliminary analysis.

Timelines

• Usage analysis: 1–2 days.
• Recommendation preparation: 1 day.
• RI/SP purchase (after approval): 1–2 hours.
• Discount application verification: 1–3 days.

Order a cloud cost audit: we guarantee savings start from the first month. Over 5 years of AWS experience, 50+ cost optimization projects. Get a consultation: our team will analyze your account and propose concrete steps.

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.