AWS provides a wide variety of EC2 instance types

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Pintu Patra
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Table of contents

1. General-Purpose Instances

Balanced compute, memory, and networking resources, suitable for a variety of workloads.

  • **T-Series (Burstable Performance)
    **Examples: t4g, t3, t2
    Use Cases: Web servers, small databases, and development environments.

  • **M-Series (Balanced Performance)
    **Examples: m7g, m6i, m5, m4
    Use Cases: Application servers, gaming servers, and backend servers.

2. Compute-Optimized Instances

Optimized for compute-intensive applications.

  • **C-Series
    **Examples: c7g, c6i, c5, c4
    Use Cases: High-performance web servers, batch processing, and scientific modeling.

3. Memory-Optimized Instances

Designed for memory-intensive applications.

  • R-Series (RAM-optimized)
    Examples: r7g, r6i, r5, r4
    Use Cases: In-memory databases, real-time big data analytics.

  • X-Series (Extra Large Memory)
    Examples: x2idn, x1e, x1
    Use Cases: SAP HANA, high-performance databases.

  • u-Series (Bare Metal Memory Instances)
    Examples: u-6tb1.metal, u-12tb1.metal
    Use Cases: Massive in-memory databases.

4. Storage-Optimized Instances

For workloads requiring high, sequential read and write access to large datasets.

  • I-Series (IO-optimized)
    Examples: i4i, i3en
    Use Cases: NoSQL databases, high-transaction workloads.

  • D-Series (Dense Storage)
    Examples: d2, d3
    Use Cases: Data warehouses, Hadoop/Spark clusters.

  • H-Series (High-Disk Throughput)
    Examples: h1
    Use Cases: Data warehousing, distributed file systems.

5. Accelerated Computing Instances

For GPU-based and FPGA workloads.

  • P-Series (GPU for Machine Learning and High-Performance Computing)
    Examples: p4d, p3, p2
    Use Cases: Deep learning, AI inference, HPC.

  • G-Series (Graphics-intensive Workloads)
    Examples: g5, g4ad
    Use Cases: Video rendering, gaming.

  • F-Series (FPGA Instances)
    Examples: f1
    Use Cases: Hardware acceleration, custom computing.

6. High Network Throughput Instances

Optimized for network-heavy applications.

  • Inf-Series (Inference for Machine Learning)
    Examples: inf1
    Use Cases: Machine learning inference.

  • **High Bandwidth Instances
    **Examples: c6in, m6in
    Use Cases: Big data analytics, media transcoding.

Factors to Consider When Choosing an Instance

  • Workload: Determine your compute, memory, storage, and network requirements.

  • Budget: Some instance types are cost-effective for specific workloads (e.g., Spot Instances, Reserved Instances).

  • Scalability: Choose based on expected scaling needs, like Auto Scaling groups.

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