Chapter 9: AWS EC2 Instance Types
AWS Cloud EC2 Instance Types.
If EC2 is your “cloud computer rental service” (as we covered last time), then instance types are the different models/specs of those virtual computers. AWS doesn’t give you one generic laptop — they give you hundreds of options, like choosing between a budget phone, gaming rig, server-grade machine, or AI supercomputer.
This is super important because picking the wrong type wastes money or slows your app. Beginners often start with free-tier t3.micro, but real projects need smart choices. Let’s break it down like a real teacher — analogies, naming secrets, families/categories, processor options (Graviton vs Intel vs AMD), examples from Hyderabad/India, and how to pick in 2026.
1. What Are EC2 Instance Types? (Simple Definition)
EC2 instance type = a specific configuration of CPU, RAM (memory), storage options, network speed, and special hardware (like GPUs) that defines your virtual server’s power.
- Each type belongs to a family (e.g., M = general purpose, C = compute optimized).
- Families have generations (higher number = newer/better, e.g., M8 > M7 > M6).
- Within a family/generation, there are sizes (nano, micro, small, medium, large, xlarge, 2xlarge, up to metal-96xl for huge bare-metal servers).
Naming pattern (very useful to decode):
m8g.4xlarge
- m = family (general purpose)
- 8 = generation (8th gen, latest in 2026)
- g = processor modifier (Graviton ARM)
- .4xlarge = size (4× large = 16 vCPU, 64 GiB RAM typically)
Other modifiers:
- g = AWS Graviton (ARM, best price/performance ~20–40% cheaper)
- i = Intel Xeon
- a = AMD EPYC (often 10% cheaper than Intel)
- d = local NVMe SSD instance storage
- n = enhanced networking (higher bandwidth)
Analogy: Think of buying a car in India:
- Family = sedan (M), SUV (R for memory), sports car (C for compute), truck (I for storage)
- Generation = 2026 model year (M8 = latest)
- Processor = engine type (Graviton = efficient electric, Intel = reliable petrol, AMD = value diesel)
- Size = engine power/seats (micro = scooter, metal = 18-wheeler truck)
2. Main Categories/Families of EC2 Instance Types (2026 Overview)
AWS groups them into 5–6 big categories. Here’s the current landscape (from AWS docs & site as of Feb 2026):
| Category | Letter/Family | Best For (Workloads) | Key Families (Latest in 2026) | Processor Options | Price/Perf Tip (India) |
|---|---|---|---|---|---|
| General Purpose | M, T, Mac | Balanced: web/apps, dev/test, small DBs, microservices | M8g/M8i/M8a, M7g/M7i, T4g/T3, Mac (Apple silicon) | Graviton4/3, Intel Sapphire Rapids, AMD EPYC, Apple M-series | Start here! Graviton g versions 20–40% cheaper |
| Compute Optimized | C | High CPU: batch jobs, video encoding, gaming servers, scientific modeling | C8g/C8i/C8a, C7g/C7i, C6gn (high network) | Graviton4, Intel, AMD | Great for CPU-heavy apps like data processing |
| Memory Optimized | R, X, U, Z | High RAM: big databases (Redis, SAP), in-memory analytics, caches | R8g/R8i/R8a, X8g, U7i (ultra-high mem up to 32 TB), z1d | Graviton4, Intel, AMD | For apps that load huge datasets into RAM |
| Storage Optimized | I, Im, Is, D | High I/O: NoSQL (Cassandra), data warehousing, logs | I8g/I8i, Im4gn/Is4gen (high ratio storage) | Graviton, Intel | Local NVMe SSDs for fast reads/writes |
| Accelerated Computing | G, P, Trn, Inf | GPU/ML/HPC: training/inference, rendering, genomics | G6/G5, P5/P6 (NVIDIA Blackwell/Hopper), Trn2 (Trainium), Inf2 | NVIDIA GPUs, AWS Trainium/Inferentia | For AI/ML — P5 huge for large models |
| HPC Optimized | Hpc | Supercomputing: simulations, weather modeling | Hpc8a, Hpc7g | AMD/Graviton | Research & engineering sims |
2026 highlights:
- Graviton4 (g suffix in M8g, C8g, R8g, etc.) — best price/performance, up to 30% better than previous.
- Newer: M8azn (AMD-based general purpose), C8id (Intel + local SSD), etc.
- Apple Mac instances (Mac2-m2, Mac-m4pro) for iOS/macOS dev/testing.
- Free tier: t3.micro / t4g.micro (burstable, great for learning).
3. Processor Choices – Graviton vs Intel vs AMD (Big Decision in 2026)
Most families offer 2–3 processor options:
- Graviton (g) — AWS custom ARM — 20–40% better price/performance, energy efficient. Use for new/greenfield apps (Linux only, ARM-compatible software). Huge in India for cost savings.
- Intel (i or no suffix) — Broadest compatibility (Windows + legacy x86 software). Use if your app requires Intel-specific features.
- AMD (a) — x86 compatible, usually 10% cheaper than Intel, good performance.
Example choice: Web app backend → M8g (Graviton) for cheapest + fast. Legacy Windows ERP → M8i (Intel).
4. Real Hyderabad/India Examples (Pick Based on Workload)
- Student/Startup website (low traffic) → t4g.micro or t3.micro (free tier, burstable CPU) — costs ~₹0 first year.
- E-commerce backend (Zomato-like microservices) → m8g.large or m7g.large (balanced, Graviton cheap) — ₹1,000–3,000/month.
- Video processing app (Tollywood trailer encoding) → c8g.2xlarge (high CPU, Graviton) — fast & cost-effective.
- Redis cache for mobile app → r8g.large (high memory) — keeps data in RAM for speed.
- AI model training (small startup in Gachibowli) → g6.xlarge (NVIDIA GPU) or trn2 (Trainium for cheaper inference).
- Big data logs (Swiggy-like) → i8g.4xlarge (high storage I/O).
5. How to Choose in Console (Practical Tip)
- Launch instance → “Instance type” section.
- Use filters: vCPU, memory, storage, network, processor (Graviton!).
- Compare: Click “Compare instance types” — see specs side-by-side.
- Use AWS Compute Optimizer (free) — it recommends better/cheaper types based on your usage.
Pro tip for cost: Prefer Graviton (g) unless compatibility blocks it. Use Savings Plans for 1–3 year commit → 40–70% off.
Quick Cheat Sheet Table – Popular Picks 2026
| Workload Type | Recommended Type (2026) | vCPU / RAM Example | Why? (Key Benefit) | Approx Monthly Cost (ap-south-1, On-Demand) |
|---|---|---|---|---|
| Learning / Small project | t4g.micro | 2 / 1 GiB | Free tier, burstable | ₹0 (first year) |
| Web server / API | m8g.medium | 1 / 4 GiB | Balanced, Graviton cheap | ₹800–1,200 |
| High CPU batch jobs | c8g.2xlarge | 8 / 16 GiB | Fast compute, good price/perf | ₹5,000–8,000 |
| Database / Cache | r8g.large | 2 / 16 GiB | High memory | ₹3,000–5,000 |
| ML Training (small) | g6.xlarge | 4 / 16 GiB + GPU | NVIDIA GPU acceleration | ₹10,000+ |
Got the big picture? Instance types = your way to match power to need without overpaying.
Next class?
- Deep dive on one family (e.g., Graviton M8g details)?
- How to compare prices in Mumbai/Hyderabad regions?
- Or launch example with specific type?
Just tell me — ready when you are! 🚀🖥️
