Droplets vs Custom Images Explained | Generated by AI

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Question: Why do people use Droplets instead of Custom Images, or vice versa, in DigitalOcean or AMD Developer Cloud?

Answer:

In platforms like DigitalOcean or AMD Developer Cloud, Droplets and Custom Images solve different problems. They work together rather than replacing each other.

What is a Droplet?

A Droplet is basically a running virtual machine (VM).

Think of it as:

It is the actual compute instance you SSH into and run workloads on.

Examples:

In DigitalOcean terminology:

What is a Custom Image?

A Custom Image is like a saved template or frozen state of a machine.

It usually contains:

You use it to create new Droplets quickly.

Instead of:

apt install ...
pip install ...
git clone ...
docker pull ...

you boot from a prepared image and everything is already there.

Why people use Droplets directly

Most people only need:

So they just:

  1. Create a Droplet
  2. Configure it manually
  3. Use it
  4. Destroy it later

This is simpler and cheaper initially.

Especially for:

On AMD Developer Cloud, many users:

No need for reusable images.

Why people use Custom Images

Custom Images become useful when setup is expensive or repeated.

Common reasons

1. Fast scaling

Suppose you spent:

You don’t want to redo this every time.

So you create a custom image and instantly clone new machines.

This is common in:

2. Consistency

Without images:

Custom images ensure:

Very important in enterprises and banks like HSBC.

3. Disaster recovery

If a server dies:

Instead of rebuilding manually.

This matters for:

4. Cost optimization for time

Images cost storage money:

But engineers are expensive.

Example:

So companies gladly pay storage cost.

Why people avoid Custom Images

1. Storage costs

Large AI images become huge.

Typical ML image:

Can easily become:

That becomes expensive across many snapshots.

2. Images become stale

You save an image today:

Then maintaining images becomes operational work.

Modern DevOps often prefers:

Instead of huge VM snapshots.

Reason:

So many advanced teams now use:

instead of giant custom images.

In AI/GPU workloads

Custom images are especially valuable because setup is painful.

Example on AMD MI300X:

can take hours.

So AI engineers often:

  1. Configure one perfect GPU VM
  2. Snapshot it
  3. Reuse everywhere

This is extremely common.

Simple analogy

Concept Real-world analogy
Droplet Running computer
Custom Image Backup disk / machine template
Snapshot Saved restore point
Docker Image App-level template
Kubernetes Large-scale orchestration

Your observation about AMD Developer Cloud quota is related

You noticed AMD limits users to small GPU quotas.

That changes behavior:

So users:

rather than building large image pipelines.

But startups or advanced users often still create reusable images because GPU environment setup is expensive and fragile.

References:

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