Originally published on CodingCush.
A Docker image is a read only template that contains your application code, runtime, libraries and settings, all packed together and ready to use. A Docker container is a live, running instance of that image. In short, the image is the blueprint and the container is the thing you actually run. You build an image once, store it, share it and then start as many containers from it as you need. Each container runs in isolation and can be stopped or deleted in seconds, and deleting it never harms the image it came from.
What Is a Docker Image?
Think of an image as a neatly packed box holding everything your app needs to run: your code, the runtime such as Node.js or Python, the libraries it depends on, and the settings that tell Docker how to start it. Nothing depends on whatever happens to be installed on your laptop that day.
An image is built from a Dockerfile, a short text file with step by step instructions, a bit like a recipe card. Docker reads it and builds the image layer by layer. Each instruction adds a layer, and Docker caches layers, so rebuilds are fast when only part of your app changes.
Images do not run. They sit quietly, like an installer you have downloaded but not opened yet. An image is read only, so its contents never change while containers run. Images are stored in registries such as Docker Hub, which hosts official images for MySQL, Nginx and Ubuntu, and they are versioned with tags such as my-app:1.0 or node:20. A tag is a friendly label for one exact version. New to Docker itself? Start with our guide on what Docker is and how it works, then come back here.
What Is a Docker Container?
A container is what you get when you run an image. It is the image brought to life, doing real work, serving real requests and using real memory and CPU. Docker creates it from the image, gives it an isolated space and starts the main process defined in the image.
Isolation is the quiet superpower here. Each container gets its own file system view, network space and process list, so your app thinks it has the machine to itself, even if ten other containers are running happily next door. Containers share the host operating system kernel, which is why they start in seconds and feel so light compared to virtual machines.
On top of the read only image layers, Docker adds a thin writable layer for each container. Files your app creates or changes go into this layer, while the image underneath stays clean. Delete the container and that layer goes away too, so important data should live in volumes, not inside a container. The Docker overview on docs.docker.com explains this model clearly and is worth a slow read afterwards.
A Simple Analogy That Makes It Stick
Use the class and object analogy from programming. An image is like a class, a definition that describes something. A container is like an object, a real instance created from that definition. You write the class once, create many objects from it, and changing one object does not rewrite the class.
Prefer cooking? An image is the recipe and a container is the cake baked from it. One recipe can produce many cakes, and eating a cake never eats the recipe. Either way, templates and running instances are different things, and Docker keeps them separate on purpose.
The Docker documentation page explaining what Docker and containers are, on docs.docker.com.
The Key Differences at a Glance
Here is the whole comparison in one table. Remember this table and you will already be ahead of most beginners.
| Point | Docker Image | Docker Container |
|---|---|---|
| What it is | A read only template or blueprint with app code, runtime and libraries | A live running instance created from an image |
| State | Static and not running | Running, paused or stopped |
| Changes | Never changes once built, you build a new version instead | Has a thin writable layer where runtime changes are stored |
| Storage | Stored locally and in registries such as Docker Hub, shared with tags | Lives on the host where it runs, usually short lived |
| Analogy | A class, a recipe or a blueprint | An object, a cake or a finished house |
Notice the pattern. Images are about definition and sharing: you version, push and pull them. Containers are about running and doing: you start, inspect and delete them.
From Dockerfile to a Running Container, Step by Step
Let us walk through the full journey with a tiny web app, so you can see where the image ends and the container begins. Here is a very small Dockerfile that uses Nginx to serve a static page.
FROM nginx:alpine COPY index.html /usr/share/nginx/html/index.html EXPOSE 80 Three short lines, no drama. The first line picks the base image, the second copies your page into the image, and the third documents that the app listens on port 80. The guide on what an image is in the Docker documentation explains layers and base images in more depth.
Now build the image and give it a name and a tag. The dot tells Docker to use the current folder as the build context.
docker build -t my-app:1.0 . After the build, list your images to confirm it exists.
docker images You should see my-app with the tag 1.0. That is your template, sitting still and doing nothing. Now run it to create a container.
docker run -d -p 8080:80 my-app:1.0 The -d flag runs the container in the background. The -p flag maps port 8080 on your computer to port 80 inside the container, so localhost:8080 in your browser shows the page. This command is the exact moment an image becomes a container.
To see running containers, use this command. It lists each container with its ID, image, status and ports.
docker ps When you are done, stop the container and remove it, using the ID shown on your screen.
docker stop docker rm Removing the container does not delete the image, so the same docker run command creates a fresh container tomorrow. When you truly want the image gone, note that docker rmi my-app:1.0 removes the image itself. Docker refuses to remove an image while containers from it still exist, which is a sensible safety net.
One Image, Many Containers
One image can create many containers, and each runs independently. You could start three containers from my-app:1.0 on different ports to test behaviour under a load balancer, or run one for development and one for a demo from the exact same image, with no works on my machine surprise.
This is why teams share images instead of setup instructions. A new teammate pulls the image, runs it, and has the same environment as everyone else within a minute. When your app grows into several services, such as a web app plus a database, our guide on what Docker Compose is shows how those containers start together with one simple file.
Common Mistakes Beginners Make
First, do not treat a container like a permanent server with important files stored only inside it. Containers are disposable by design. Second, clean up old images now and then, because they quietly eat disk space. Third, avoid the latest tag in serious projects, because it can change without warning. Pinning an exact tag like 1.0 keeps your setup predictable and your future self calm.
Frequently Asked Questions
Can a container run without an image?
No. Every container is created from an image. The image provides the file system, runtime and start command, so there is nothing to run until an image exists.
Does deleting a container delete the image?
No. It only removes that instance and its writable layer. The image stays ready to create fresh containers until you remove it with docker rmi.
How many containers can one image create?
As many as your machine can handle. There is no fixed Docker limit, so one image can power a single test container or hundreds of copies in production.
Where are Docker images stored?
Locally on your machine after a build or download, and in registries such as Docker Hub for sharing. You push an image to a registry and other machines pull it.
Conclusion
An image is the template and a container is the running instance, and that single sentence unlocks most of Docker. Build images carefully, version them with clear tags and share them through a registry. Then treat containers as light, disposable workers you can start and remove without fear. Try the example above today: run one image twice on two ports and watch two containers live side by side. That tiny experiment teaches more than another hour of reading.
