Trends in tech come and go, but certain underlying primitives stick around forever.
In software, two such primitives are virtual machines and containers.
Virtualization paved the way for the cloud to become massive. Data centers would likely never have been commercially viable without it.
While still relatively new, containerization has already made a serious mark on the software engineering world. Container adoption has brought the rise of DevOps, platform engineering, and a cultural shift geared towards achieving higher velocity .
Cycle users now enjoy environments where containerized workloads and virtual machine workloads can run side by side on the same compute nodes, on the same networks in Cycle environments.
Now more than ever, the choice of whether to run a workload in a container or a VM comes down to user preference. But in a time where many software decisions are driven by hype more than data, we decided to demystify how to choose.
Decision Drivers: Containers vs Virtual Machines
By default, most new workloads should be geared towards containers. Containers have been proven to be more efficient in tight release cycles, they're more portable, and it's easier to achieve higher usage density on compute nodes using them. So, the angle we will take in deciding when to use containers vs virtual machines is easiest if thought of as “what are the most concrete examples of when I should use a virtual machine and why?”
Before diving into the deep end, let's take a high-level look at some of the scenarios:
| Factor | Why It Matters | VM‐Leaning Signals | Container‐Leaning Signals |
|---|---|---|---|
| Isolation & Kernel / Driver Control | Custom kernels or proprietary drivers can destabilize a shared host; separate kernels shrink blast radius. | Windows Server, vendor GPU modules, strict audit scope | Vanilla Linux, no special drivers, single‐tenant stack |
| Workload Type | Some workloads (legacy apps, heavy hardware) are easier to keep in VMs. | Legacy ERP, telco NFV, vertical‐scale DB | REST API, cron job, microservice |
| Data Persistence | Snapshot and recovery strategy changes when data is large or mission‐critical. | Large monolithic DB, need crash‐consistent snapshots | Replicated stores, small or ephemeral data |
| Deployment Cadence | Faster deploy cycles benefit from lighter images and quicker restarts. | Monthly/quarterly releases | Daily/continuous releases |
| Operational Considerations | Day‐to‐day runtime behaviors—load profile, tenant separation, and team capability. | Predictable load; external audit; small SRE team | Spiky load; lightweight scaling |
Think of each factor as a lens that can be a solid reason to move from containers to VMs for a workload. If you're looking at two or more factors leaning towards VMs, it's almost a guarantee that VMs will be the better tech for that workload.
Isolation, Security, Kernel Control and Workload Type
Containers run directly on the host's kernel. Virtual machines run next to the host kernel with a kernel of their own. That one design choice can settle many container vs VM debates. It's incredibly relevant if you need to run a Windows or legacy application—almost certainly in a VM. But if you're running a modern Linux workload, consider:
Custom Kernel Modules or Drivers: If the workload demands custom drivers or a kernel change, forcing things through the container would impact every workload on the host. That's enough to merit a VM.
Blast Radius Concerns: If a kernel issue could take all tenants offline, ask if the impact is acceptable. A VM confines that blast radius.
Compliance Pressure: Some compliance standards (PCI‑DSS, HIPAA, FedRAMP High) demand “own kernel.” In those cases, a VM is the safe choice.
Data Persistence and Operational Cadence
Stateful workloads run well in containers, but sharing volumes can add complexity. VMs simplify snapshot semantics:
- VM snapshots are crash‐consistent and cheap, even for multi‑TB volumes.
- Container volumes live outside the image and require separate backup strategies.
Deployment mechanics also differ:
| Stage | Container | VM |
|---|---|---|
| Build | docker build: small layered image | packer build: full OS image with packages, services, app, and init scripts |
| Publish | Push ~100 MB image to registry, layers dedupe | Upload multi‑GB image to VM library or snapshot store |
| Deploy | Orchestrator pulls layers and starts container—cold start in seconds | Hypervisor streams full disk and boots OS—cold start in tens of seconds to minutes |
| Rollback/Canary | Point at older image hash or run side‑by‑side pods | Restore snapshot or redirect traffic to new VM |
| Patch Cycle | Ship new container image; host kernel stays put | Patch OS inside each guest and maintain a golden image |
This highlights container efficiency. If you don't need VMs, containers can be a huge productivity gain.
Operational Considerations
Peak to Average Load: Containers scale faster. For spiky workloads that need rapid scaling, containers win.
Tenant Isolation per Compliance: Strict compliance might mandate full kernel isolation—VMs are then necessary.
Team Shape: If your team lacks container expertise, start with VMs or use a platform like Cycle to migrate gradually.
Wrapping Up
Containers or VMs—choose based on real workload needs.
Software engineering now offers a golden age of choice; previously “sacrilegious” options are now moot. If a workload needs its own kernel, heavy snapshots, or strict audit boundaries, spin up a VM. Otherwise, reach for containers.
If you're not already deploying to Cycle, give it a try. Run containers and VMs side by side across multi‑cloud, hybrid‑cloud, on‑prem, and colo—making developers' lives easier.