If your machine learning workload runs cleanly from a container image and you don’t need to manage the operating system, use a container instance. If you need full OS control, want Docker to behave normally, or you keep hitting system-level limits, use a virtual machine (VM).
Virtual machines emulate entire servers, including their own operating system and own kernel, allowing them to run different operating systems as guest operating systems on the same physical hardware. VMs are isolated computing environments that emulate physical computers and contain their own operating system, making it possible to emulate entire servers or desktops within a single host. This means you can run different operating systems (such as Windows, Linux, or macOS) for development, testing, or compatibility purposes, all on the same physical hardware. In contrast, containers virtualize the operating system, sharing the host OS kernel, and do not emulate entire servers. This limits containers to the host OS kernel, so they cannot run different operating systems as VMs can.
Hivenet’s Compute supports both, and you pick the runtime when you create an instance. If you want the quick, non-ML-specific chooser, start here: VM or container: how to choose in 60 seconds.
Introduction to virtualization
Virtualization lets you run multiple virtual machines (VMs) on one physical machine, so you can host different operating systems and applications on a single server. This approach helps you make better use of your system resources—you can run various workloads without buying separate hardware for each one. Virtualization simplifies how you manage resources, supports automated processes, and cuts infrastructure costs by putting more workloads on fewer physical servers. It's become essential in software development and evaluating distributed compute providers because it makes testing, deploying, and scaling applications much easier. Whether you're running several virtual machines for different projects or supporting a complex development environment, virtualization gives you the flexibility and efficiency you need to keep pace with today's demanding software development and cloud computing requirements.](https://compute.hivenet.com/post/why-should-developers-compute-with-hivenet)..
What are containers?
Containers package and deploy your applications in a way that's lightweight, portable, and consistent across different environments. Unlike virtual machines that need a complete operating system for each instance, containers share your host operating system's kernel to run the application layer. You can run multiple applications with fewer resources—they start up quickly and use less storage space. Containers work well for cloud native applications (applications built specifically for cloud environments) where you need portability and scalability, especially when small and medium-sized businesses want to leverage AI trends with cloud GPU computing. When you bundle an application and all its dependencies into a single container, your software runs reliably whether it's on your laptop, a test server, or in production. This approach makes deployment and management simpler, which is why teams choose containers when they want to move fast and keep things consistent across different environments..
What you’re choosing on Compute: virtual machines
A container instance is an efficient runtime for running an app or service. It starts fast, stays lightweight, and is usually the easiest way to run common ML tooling. Containers are lightweight, portable, and self-contained executable images that contain software applications and their dependencies, making them ideal for rapid application deployment. Containers can be deployed quickly and easily, which is beneficial for applications that need rapid scaling. Containerized applications are commonly used for deploying and managing modern ML workloads, and you can deploy containers to efficiently deploy applications across various environments.
A VM is a full Linux environment. You choose an OS, connect over SSH, install system packages, run services, and generally treat it like “your own server.” VMs are often used for more complex application deployment scenarios or when you need to deploy applications that require full OS control. It’s a better fit when your ML setup looks like a machine, not a single process.
You can deploy containers for lightweight, scalable workloads, or use VMs for more complex or legacy application deployment needs.
If you want the broader overview of what changed, read: Compute now supports virtual machines (VMs).
The ML-specific factors that actually matter: operating system
Environment control
ML work is rarely “just run one binary.” You end up needing Python tooling, system libs, specific versions of packages, background processes, and sometimes weird dependencies. If you’re constantly blocked by environment limitations, a VM stops the friction. Containers, being typically smaller in size compared to virtual machines, are faster to deploy and scale, and they optimize resource usage, allowing for more cost effective scaling and deployment.
Reproducibility
Containers are strong for repeatability because you bake the environment into an image and run it the same way each time. Containers use static definitions for their environments, which helps ensure consistency and reduces configuration drift, since containers are typically destroyed and redeployed frequently. VMs can be reproducible too, but you have to be more disciplined about how you configure them.
Workflow shape
Some ML workflows look like one service (an inference server). Others look like a small system (API + queue + worker + UI + storage). Multi-service systems usually feel more natural on a VM, especially if you rely on Docker Compose.
Security and isolation comfort
A VM gives you a stronger “separate machine” boundary. If you’re operating in a stricter environment, or you simply want more isolation for peace of mind, that can be a deciding factor.
Cost control
Cost usually has less to do with runtime type and more to do with hardware and how long you leave it running. That said, containers often get you to “done” faster, and that can reduce wasted runtime. Containers are generally significantly less expensive than virtual machines due to their lighter weight and more efficient resource usage, making them a cost effective choice for many ML workloads. This article focuses on that side of the question: Cloud GPU VM pricing: what you’re really paying for.
Common ML scenarios and the runtime I’d pick
You’re prototyping or experimenting Start with a container instance. You’ll get to a working run faster, and if the experiment dies (as many do), you’re not left maintaining a server-shaped environment. If you later discover you need system control, switching is normal: When it’s worth switching from a container instance to a VM.
You’re training or fine-tuning with a known stack If your training job already runs from a container image, stick with containers. Containers are often used for microservices, web applications, and CI/CD pipelines, and are ideal for rapid scaling of ML training jobs or inference APIs using orchestration tools like Kubernetes or Docker Swarm. This is especially true when you’re iterating on code and want repeatable runs. A VM becomes attractive when you need OS-level tooling, special system dependencies, or you’re building a long-lived training environment you keep returning to.
You’re serving inference A container instance is often the cleanest path for inference because it maps well to “run a service, expose it, replace it when you update.” If your inference setup needs multiple cooperating services, custom system pieces, or a “host-like” layout, move up to a VM. VMs are typically used for legacy applications and diverse OS requirements, and can run any guest OS regardless of the host's OS.
You’re building a multi-service ML stack Lean VM. The moment you have a web UI, an API, a worker, and a queue, you’re in “small system” territory. Containers support the decomposition of monolithic applications into multiple containers (microservices), enhancing scalability and manageability. Managing containerized applications is often done with orchestration tools like Kubernetes, and a container runtime such as Docker is essential for running and managing containers. If Docker is part of your workflow, a VM keeps it familiar. Start here: Run Docker the normal way on a Compute VM. For the step-by-step Docker setup, use: Install Docker on Compute. Containerized workloads can be managed alongside VMs for flexibility in complex stacks.
You’re doing benchmarks or performance comparisons Lean VM. Benchmarking is mostly about controlling variables. A VM gives you a consistent base OS and a stable place for tooling. If you want a plain-English “who needs a GPU VM” explainer, use: GPU virtual machine: what it is and who actually needs one.
You’re blocked by system-level needs This is the clearest VM signal. If you keep needing sudo, system services, or OS-level installs, you’re done debating. Move to a VM and keep going. VMs are well-suited for legacy applications that require strong isolation, diverse operating systems, or hardware-level resource allocation. The switch guide is here: When it’s worth switching from a container instance to a VM.
Organizations can use containers for modern application development while relying on VMs for legacy applications. Containers can also be run inside VMs to combine the benefits of both technologies, such as portability and security.
A practical default that keeps you out of trouble
If you don’t know yet, start with a container instance. Containers can run multiple applications on the same hardware more efficiently than virtual machines, sharing resources with other containers on the host machine. This makes containers ideal for maximizing resource utilization and cost-effectiveness.
Containers are more lightweight and portable than virtual machines, making them suitable for rapid prototyping and scaling. They can start and stop in a few seconds, while virtual machines typically take a few minutes to boot up.
Switch to a VM the moment your notes start filling up with environment workarounds. If you’ve spent an hour trying to make a container behave like a normal server, that’s your answer.
Best practices for containers and virtual machines
You'll want to use specific tools and methods to run containers and virtual machines effectively. Use a container engine like Docker to manage your workloads, and create static build files (like Dockerfiles) so every deployment stays consistent and repeatable. For virtual machines, use a hypervisor to allocate system resources, and run multiple VMs on one physical host to get more from your hardware. In cloud environments, you can combine containers and VMs in a hybrid setup—this gives you flexibility, isolation, and the ability to scale. When you follow these methods, you'll improve your infrastructure, cut costs, and make sure both containers and virtual machines support your workloads effectively.
Reaching your ML app from the outside in cloud environments
This is where people lose time, so it’s worth saying plainly: “the app runs” and “I can reach it” are separate problems.
If you’re testing a UI privately, SSH port forwarding is often the least painful route. If you need a public web link, use HTTPS. If you need direct client connections, use TCP or UDP.
Future of machine learning with containers and virtual machines
Machine learning infrastructure needs containers and virtual machines to work well. Containers package ML models with their dependencies, so you can deploy them quickly and get the same results across different environments. Virtual machines give you the isolation and security that sensitive or resource-heavy ML workloads need, especially when you're dealing with compliance requirements or custom system setups. When you use containers and VMs together, your organization can scale cloud resources when needed and run complex applications. As ML workloads get bigger and more complex, you'll need both containers and virtual machines to deploy, manage, and innovate effectively. Organizations that use these technologies will handle modern machine learning demands better and create new opportunities in cloud computing.
Try Compute for containerized applications
If you want to make the decision with minimal risk, launch the smallest setup that can run your workflow, run one real test, then decide whether you want the speed of containers or the control of a VM.
