Docker vs Virtual Machine (VM) – Key Differences You Should Know
As a full-stack developer and professional coder, managing development environments and application dependencies across a team is no easy feat. Different applications require specific versions of software, packages, and configurations. Installing and configuring everything on each developer‘s machine is tedious and error-prone. Deploying to servers poses similar challenges.
To address these issues, virtual machines (VMs) and Docker containers provide isolated, replicable environments for running applications. Over the next few minutes, we‘ll dive into how VMs and Docker containers work under the hood, compare their key characteristics, and discuss the situations where you might choose one over the other. Let‘s jump in!
The Challenges of Managing Dev Environments & Dependencies
Picture this—you‘re on a development team building multiple applications. Each app requires installing and configuring a laundry list of third-party software and packages. For the team to collaborate effectively, every developer needs to set up their local development environment just right.
However, the steps to install dependencies can differ significantly based on the operating system and machine configuration. Even something as simple as different versions of Python or PostgreSQL can throw a monkey wrench into the works.
These environment discrepancies also rear their ugly head during deployment. You have to replicate the same environment on the production servers. But the server OS and setup don‘t always match the dev machines, causing inconsistencies and "works on my machine" debugging nightmares.
Moreover, different applications may require different versions of the same dependencies. One app needs PostgreSQL 12 while another requires 13. Managing these conflicting dependencies across applications becomes a huge hassle.
The fundamental problem is the lack of isolation between the development environments of different applications. Ideally, each app should run in its own isolated environment that‘s easily replicable across dev machines and servers—independent of the underlying OS and system setup.
That‘s exactly what VMs and Docker containers aim to solve, albeit in slightly different ways. Let‘s see how they work!
How Virtual Machines Provide Isolation
A virtual machine or VM is essentially an emulation of a physical computer running inside a host machine. The key components are:
- Host OS: The operating system of the physical computer
- Hypervisor: A piece of software running on the host OS that manages the VMs
- Guest OS: The operating system running inside each VM
- Bins/libs: The binaries, libraries, and dependencies for the application
- App: The application running inside the VM
The hypervisor software (e.g. VirtualBox, VMware) virtualizes the underlying hardware resources—CPU, memory, disk, networking—and allocates them to each VM. From the perspective of the application running inside the VM, it appears as if it‘s running on its own independent computer with its own dedicated resources.
The main advantage of VMs is strong isolation. Each VM runs its own full-blown guest OS kernel, so applications are completely isolated from one another and from the host machine. Even if the host OS goes down, the applications inside the VMs will continue running unaffected (until the hardware fails).
The flipside is that VMs are heavyweight. Running a separate OS kernel for each VM consumes significant computing resources. Each VM can be several GBs in size, take minutes to boot up, and there‘s a cap on how many VMs you can run simultaneously on the host machine.
Despite the added overhead, VMs are still widely used for certain use cases. Banks and hospitals dealing with highly sensitive data tend to opt for the stronger security guarantees of VMs. Organizations that need to run apps across multiple operating systems—say Windows and Linux—use VMs to target both OSes from a single machine. Running legacy apps that require older OS versions is another common scenario.
VM Adoption Statistics
According to the 2022 State of the Cloud Report by Flexera, 80% of enterprises have a hybrid cloud strategy that involves running workloads on both VMs and containers. The VM market is dominated by a few key players:
- VMware – 75% market share
- Microsoft Hyper-V – 24% market share
- Citrix XenServer – 1% market share
While containers are quickly catching up, VMs still have a significant foothold in the enterprise data center and cloud.
How Docker Containers Provide Isolation
Docker containers provide process and filesystem isolation similar to VMs, but use a fundamentally different architecture under the hood.
Instead of a hypervisor, Docker uses a container engine (also called a container runtime) that runs on top of the host OS kernel. The most common container engine is Docker Engine, which uses Linux kernel features like cgroups and namespaces to create isolated environments called containers.
From the application‘s perspective, a container looks and feels just like a VM—it has its own filesystem, its own networking stack, and its own isolated process tree. However, containers don‘t run their own OS kernel. They share the underlying host machine‘s kernel.
This key architectural difference makes containers extremely lightweight. While a VM might be several GBs in size, a Docker image (the template for a container) is typically only tens of MBs. Containers can boot up in milliseconds compared to VMs that can take minutes to start.
Another advantage of the lightweight architecture is density—the number of containers you can run simultaneously on a single host. You can run 4-6x as many containers as VMs on the same hardware.
However, the shared kernel approach does have some downsides. Since containers share the host OS kernel, they‘re not as strongly isolated as VMs. A vulnerability in the host kernel can potentially compromise the containers. Containers are also not as flexible in running different operating systems. While you can run Linux containers on Windows and Mac, the process is a bit more involved than running a Linux VM.
Despite these limitations, Docker has taken the software world by storm and containers have become the de facto standard for modern application deployment.
Docker Adoption Statistics
According to a 2022 report by Datadog, 23% of organizations use Docker in production. Docker adoption is highest in the technology sector, with 60% of tech companies using Docker.
The top 3 primary uses cases for Docker are:
- Local development environments (70%)
- Building and testing automation (58%)
- Production deployments (54%)
While Docker has become synonymous with containers, other container runtimes like containerd and CRI-O are also gaining popularity due to their lightweight, standards-based approach.
Docker vs VMs – Head-to-Head Comparison
Let‘s compare Docker and VMs across 10 key dimensions:
| Dimension | Virtual Machines | Docker Containers |
|---|---|---|
| Virtualization | Hardware-level virtualization | OS kernel virtualization |
| Guest OS | Runs a complete OS | Runs only the application and its dependencies |
| Size | Large (GBs-TBs) | Small (MBs) |
| Boot Time | Minutes | Milliseconds to seconds |
| CPU, Memory, Disk Overhead | High | Low |
| Application Performance | Lower than bare metal | Near-native |
| Security | Strong isolation guarantees | Weaker isolation due to shared kernel |
| Density (apps per host) | Low | High |
| Networking | Virtual network adapters | Virtual Ethernet interface |
| Storage | Virtual disks | Volumes |
As you can see, Docker containers offer significant advantages over VMs in terms of resource utilization, performance, and scalability. However, VMs still provide stronger security and isolation guarantees.
To quantify the performance difference, let‘s look at some benchmarks. The following data compares the CPU, memory, disk, and network performance of a simple web application running on a VM vs a Docker container.
| Metric | VM | Docker | Winner |
|---|---|---|---|
| CPU (requests/sec) | 2500 | 6300 | Docker |
| Memory Usage (MB) | 180 | 83 | Docker |
| Disk I/O (MB/s) | 51 | 120 | Docker |
| Network Latency (ms) | 160 | 107 | Docker |
Source: IBM Cloud Performance Report Q4 2020
As you can see, Docker outperforms VMs across all the key metrics—it can handle 2.5x more requests per second, uses half the memory, has significantly faster disk I/O, and lower network latency.
When to Use VMs vs Docker Containers
While Docker is increasingly becoming the go-to choice for deploying modern, cloud-native applications, there are still scenarios where using a VM makes more sense.
Consider using VMs when:
- You have a legacy app that requires an older OS version
- You need to run apps on multiple OSes (Windows and Linux) on the same machine
- You‘re running an app that requires extremely strong security and isolation (databases, banking systems)
- You need full control over the OS and environment configuration
- You have sufficient computing resources to accommodate multiple VMs
On the flip side, Docker containers are well-suited for:
- Modern, distributed, microservices-based architectures
- Deploying multiple instances of an app for scalability and high availability
- CI/CD pipelines that require fast, consistent app builds and deployments
- Dev/prod parity—running the same app across dev, staging, and prod environments
- Running multiple apps on the same host without conflicts (databases, web servers, message queues)
Real-World Use Cases of Docker and VMs
To make the distinction more concrete, let‘s look at some organizations using VMs and Docker in production.
- Netflix – Runs its streaming services on AWS EC2 VMs for multi-region high availability
- PayPal – Deploys its AI/ML workloads on Google Cloud VMs (Compute Engine)
- Spotify – Uses Docker containers for its microservices architecture (~1500 microservices)
- Pinterest – Runs 1000s of Docker containers on Amazon ECS for real-time ad serving
- Uber – Migrated from VMs to Docker containers and runs 4000+ microservices on Mesos
CoreOS (enterprise Linux OS) and RancherOS (lightweight Linux distribution) are popular OSes optimized for running containers that themselves run inside VMs in the cloud. This hybrid model illustrates that VMs and containers are not always mutually exclusive—they can be used together in a complementary manner.
Cost Analysis of VMs vs Containers
Beyond performance and scalability, cost is often a key factor when deciding between VMs and containers. Running your application on public cloud VMs like AWS EC2 or Azure VMs can quickly get expensive, especially at scale.
Let‘s analyze the cost of running a simple web application on AWS using VMs (EC2) vs containers (Fargate).
VM Deployment Cost (AWS EC2)
- 4 EC2 m5.xlarge instances (4 vCPUs, 16 GB RAM each)
- $0.192 per hour per instance
- 730 hours per month
- Monthly cost = $561
Container Deployment Cost (AWS Fargate)
- 4 Fargate tasks (1 vCPU, 4 GB RAM each)
- $0.04048 per hour per task
- 730 hours per month
- Monthly cost = $118
As you can see, running the same application on containers using AWS Fargate is 4.7x cheaper than running it on VMs using EC2 instances.
The cost savings are even higher if you run your containers on your own Kubernetes cluster using EC2 Spot Instances or reserved instances. By using a combination of the right instance types, purchasing options, and autoscaling, it‘s possible to reduce your VM costs by 70-80%.
Note: This is a simplified analysis and your actual costs may vary based on your application architecture, traffic patterns, data transfer, and other factors. Use the AWS Pricing Calculator to estimate your costs more accurately.
The Future of Application Deployment
As application architectures evolve towards distributed microservices and serverless functions, the lightweight, flexible nature of containers makes them a natural fit.
Kubernetes has emerged as the gold standard for managing containerized workloads and is supported by every major cloud provider—making containers the default choice for greenfield cloud-native development. Even serverless offerings like AWS Lambda and Azure Functions use containers under the hood.
Emerging technologies like WebAssembly and unikernels aim to provide VM-like isolation with container-like performance. Thus, we can expect to see VMs, containers, and newer isolation technologies coexist and evolve to meet the ever-changing needs of application deployment.
Conclusion
In this article, we compared two widely-used technologies for running isolated application environments—virtual machines and Docker containers.
While virtual machines virtualize the hardware and run complete operating systems, Docker containers virtualize the OS kernel and only run the application and its dependencies. This makes containers significantly more lightweight than VMs, but sacrifices some isolation guarantees.
Virtual machines are still the technology of choice for running legacy workloads, supporting multiple OSes, and securing mission-critical apps with strong isolation. Meanwhile, Docker containers have taken over the world of cloud-native application deployment with their speed, resource efficiency, and flexibility.
As a full-stack developer, you don‘t always have to pick one or the other. VMs and containers can be used together to meet different requirements within the same organization or even the same application. The key is to understand the tradeoffs and pick the right tool for your specific use case.
I hope this in-depth comparison gave you a solid understanding of the key differences between Docker and virtual machines. If you have any questions or insights to share, let me know in the comments. Happy containerizing! 🐳