A deployment image is a packaged artifact that captures the software, configuration, and assumptions needed to recreate an application or server environment.
For developers, the important decision is not only whether to use containers or virtual machines. Raff already has a separate guide for that runtime comparison. This guide focuses on the artifact itself: should your team package the application as a container image, package the full server as a VM image, or use both together? Raff Technologies gives teams full-root Linux VMs, Docker-ready infrastructure, custom ISO support, and fast deployment, which makes it practical to choose the artifact model that fits the workload instead of forcing every application into one pattern. Raff’s Linux VM page lists deployment in under 60 seconds, full root access, 9 Linux distributions, NVMe SSD storage, and plans from $3.99/month. Raff Linux VM
This guide belongs in Raff’s deployment and application architecture cluster. Raff’s Docker vs Virtual Machines guide explains the runtime difference between containers and VMs. Raff’s VM Provisioning Models guide explains cloud-init, custom images, and one-click applications. This guide sits between them: it explains when developers should standardize on a container image, when a VM image is the better artifact, and when both should work together. Docker vs Virtual Machines
Container Images and VM Images Solve Different Packaging Problems
A container image packages an application and its user-space dependencies. A VM image packages a whole machine state.
That difference sounds small, but it changes deployment, security, updates, rollback, and team ownership.
The Open Container Initiative defines an OCI image as an artifact made of an image manifest, an optional image index, filesystem layers, and configuration. The goal is interoperability: tools should be able to build, transport, and prepare the image to run. Open Container Initiative Image Specification
A VM image is broader. Cloud providers commonly use VM images to create new virtual machines from a saved disk or machine state. Google Cloud describes custom images as useful when a persistent boot disk has been modified to a specific state and should be reused to create VM instances. Google Cloud Custom Images
In practical terms:
| Image type | Packages | Recreates | Best mental model |
|---|---|---|---|
| Container image | App, libraries, runtime dependencies, metadata | A containerized process or service | “This is my application artifact.” |
| VM image | Operating system, packages, system config, base services, disk state | A virtual machine | “This is my server artifact.” |
The mistake is treating them as interchangeable. They are not.
A container image is usually the better artifact when the application needs to move across environments. A VM image is usually the better artifact when the machine itself needs to be consistent.
The Image Decision Framework
Use this framework to decide whether a container image, VM image, or hybrid model fits the workload.
| Deployment need | Better image model | Why it fits | Watch out for |
|---|---|---|---|
| Ship a web app across dev, staging, and production | Container image | App runtime becomes repeatable across environments | Host networking, storage, and secrets still need design |
| Recreate a hardened server baseline | VM image | OS, packages, users, agents, and security config stay consistent | Image drift if updates are not maintained |
| Deploy many similar app services | Container image | Each service can version and release independently | Registry, scanning, and orchestration discipline matter |
| Provision identical servers for a team | VM image | Server setup is captured once and reused | Large images can become stale or difficult to audit |
| Run Docker Compose on one VM | Container images on a VM | VM provides host; images package services | Host backup, firewall, and updates still matter |
| Run a Windows-only workload | VM image | OS and desktop/server environment matter | Application updates may still need separate process |
| Build disposable test environments | Container image or lightweight VM image | Fast recreation matters more than preservation | Data persistence must be separate |
| Preserve legacy application state | VM image | Full server state may be easier to capture | Can preserve old risk and undocumented configuration |
The central distinction is simple: container images standardize the application layer; VM images standardize the machine layer.
Many teams need both. A VM can provide the stable infrastructure host, while container images provide repeatable application releases on top of it.
Container Images Are Strongest for Application Portability
Container images are usually the better choice when the application is the thing you want to version, test, ship, and roll back.
A container image lets developers package the runtime assumptions close to the application: language version, libraries, system packages, startup behavior, and exposed ports. That makes it easier to move the same application through development, staging, and production without rebuilding the server each time.
Docker’s documentation describes Docker as a platform for containerizing applications, and the broader Docker ecosystem is built around the idea of building images and running containers from those images. Docker Documentation
Container images are strong when:
| Situation | Why container images help |
|---|---|
| The app changes more often than the server | Releases can happen without rebuilding the whole VM |
| Multiple services use different runtimes | Each service carries its own dependencies |
| CI/CD matters | Images become build artifacts |
| Rollbacks need to be fast | Previous image versions can be redeployed |
| Developers need local parity | The same image can run locally and on a server |
| The team may adopt Compose or Kubernetes later | Container images remain useful across orchestration models |
Container images are not magic. They do not automatically solve secrets, databases, storage, networking, backups, monitoring, or host security. They package the app; they do not replace infrastructure design.
That is why a container image works best when the team treats it as one part of a deployment system, not the entire system.
VM Images Are Strongest for Machine Consistency
A VM image is useful when the operating system and server-level setup matter as much as the application.
This is common for hardened baselines, Windows workloads, legacy applications, specialized packages, custom agents, desktop environments, compliance-sensitive configurations, and servers that need to boot into a known state with minimal setup.
A VM image can capture:
- operating system version,
- installed packages,
- system users,
- security baseline,
- monitoring agents,
- language runtimes,
- service managers,
- firewall defaults,
- disk layout,
- custom drivers,
- and preconfigured application dependencies.
Raff’s VM Provisioning Models guide explains that custom images are one provisioning model alongside cloud-init and one-click applications. That matters because VM images are not only about backup; they are also about repeatable provisioning. VM Provisioning Models
| VM image is a good fit when | VM image becomes risky when |
|---|---|
| Server baseline must be identical | The image is not patched or reviewed |
| OS-level configuration matters | App releases are trapped inside full server images |
| Boot speed and consistency matter | Hidden manual state is captured accidentally |
| Legacy apps depend on machine state | The image preserves old security debt |
| Windows or GUI workloads matter | Image size and update cadence grow heavy |
A VM image should not become a dumping ground for every change. If every application update requires a new full machine image, the team may lose the speed and clarity that container images provide.
The Difference Matters Most During Updates
The easiest way to understand the difference is to look at updates.
With a container image, the application artifact changes. The host can stay mostly the same. The team builds a new image, tests it, deploys it, and can roll back to a previous image if needed.
With a VM image, the machine artifact changes. The team updates the operating system, packages, agents, runtime, and sometimes the application together. That can be powerful for baseline consistency, but it can also make releases heavier.
| Update type | Better image model |
|---|---|
| Application code update | Container image |
| Language runtime update for one service | Container image |
| Base OS hardening update | VM image |
| Security agent baseline update | VM image |
| One package shared by all servers | VM image or configuration automation |
| Multi-service release | Container images |
| Full server template refresh | VM image |
| Emergency app rollback | Container image |
The operational lesson is clear: the faster-changing layer should usually have the smaller artifact.
If the application changes daily, package it as a container image. If the server baseline changes monthly or quarterly, standardize it as a VM image or provisioning template. Mixing those lifecycles carelessly creates slow releases and confusing rollbacks.
Rollback Looks Different for Each Image Type
Rollback is one of the biggest practical differences.
A container image rollback usually means returning a service to a previous application image. That can be fast if the host, database schema, secrets, and storage are compatible.
A VM image rollback usually means recreating or restoring a whole machine state. That can be safer for OS-level changes, but it may be heavier and more disruptive.
| Rollback need | Better model | Reason |
|---|---|---|
| Bad app release | Container image | Previous image version can restore app behavior |
| Bad OS baseline | VM image or snapshot | Machine-level state must be restored |
| Broken dependency inside one service | Container image | Service dependency travels with the app |
| Broken system package shared by server | VM image or snapshot | Host-level state changed |
| Corrupted data | Backup, not image rollback | Images should not be the only data recovery path |
| Compromised server | Clean VM image plus restored data | Repairing the existing machine may preserve risk |
Images are not a replacement for backups. A container image does not protect your database. A VM image may preserve a server baseline, but it should not be the only way to recover important data.
For production workloads, images, snapshots, and backups should have separate jobs: images recreate software, snapshots support fast rollback, and backups protect data.
Security Ownership Changes by Image Type
Security also looks different.
Container image security focuses on base images, dependencies, secrets, image scanning, registry access, and runtime permissions. VM image security focuses on OS patching, user accounts, SSH or RDP access, baseline hardening, agents, firewall defaults, and disk contents.
Docker Official Images are curated images that aim to follow best practices such as rebuilding for updates and security fixes. That does not remove responsibility from the application team, but it shows why base image selection matters. Docker Official Images
| Security area | Container image concern | VM image concern |
|---|---|---|
| Base layer | Base image vulnerabilities | Operating system vulnerabilities |
| Secrets | Secrets accidentally baked into image | Secrets stored on disk or in config files |
| Access | Registry and runtime permissions | SSH, RDP, users, sudo, administrator access |
| Updates | Rebuild images when dependencies change | Patch and refresh the VM image baseline |
| Scanning | Image vulnerability scanning | OS and configuration auditing |
| Runtime boundary | Container isolation and host exposure | Hypervisor and VM-level isolation |
| Drift | Running container differs from image | VM diverges from baseline after manual changes |
The worst practice in both models is the same: baking secrets and undocumented assumptions into the image.
An image should make deployment more repeatable, not hide sensitive information or operational shortcuts.
Portability Is Different From Reproducibility
Container images are often described as portable, and that is mostly true at the application layer. A well-built container image can run on a developer laptop, a VM, a CI runner, Docker Compose, or Kubernetes if the surrounding assumptions are satisfied.
But portability is not the same as full reproducibility.
A container image may still depend on external databases, mounted volumes, environment variables, secrets, DNS, storage, queues, and network policies. If those are missing, the image alone does not recreate the system.
A VM image is less portable across providers, but it can be more complete at the machine layer. It may include OS configuration, system services, agents, runtime packages, and local files. That makes it useful for recreating a server, but less flexible as a general application artifact.
| Question | Container image answers well | VM image answers well |
|---|---|---|
| What app version is running? | Yes | Sometimes |
| What runtime does the app need? | Yes | Yes |
| What OS baseline should the machine use? | No | Yes |
| Can this service move between hosts? | Usually | Less easily |
| Can this full server be recreated? | Not alone | Yes |
| Can releases happen independently from host updates? | Yes | Not always |
| Can multiple apps share one server baseline? | Yes | Yes, if host is managed well |
The best systems separate concerns: container images make apps reproducible; VM images make servers reproducible.
Team Size Should Influence the Choice
A large platform team can maintain image pipelines, registries, scanners, hardened VM baselines, Kubernetes clusters, and golden image refresh cycles. A small team usually cannot do all of that at once.
For small teams, the best image strategy is the one they can maintain consistently.
| Team situation | Practical default |
|---|---|
| One app, one small team, one VM | Use a standard VM plus simple app deployment or Docker Compose |
| Several services on one server | Use container images and Docker Compose |
| Multiple similar servers | Use a VM image or provisioning automation |
| Frequent app releases | Use container images |
| Strict server baseline requirements | Use VM images plus patch discipline |
| Windows-only application | Use a Windows VM image or template |
| Team lacks container experience | Start with standard VM deployment and introduce containers when useful |
| Team lacks ops experience | Avoid overbuilding image pipelines too early |
Raff’s Kubernetes vs Docker Compose guide makes a related point: for small teams, Docker Compose is often the first clean production answer, while Kubernetes becomes useful when the coordination problem is genuinely larger than one host. Kubernetes vs Docker Compose
The same logic applies here. Start with the smallest image strategy that gives your team repeatability without creating maintenance debt.
A Hybrid Model Is Usually the Practical Answer
In real deployments, container images and VM images are often complementary.
A team may use a VM image to create a hardened base server with Docker, monitoring, firewall defaults, and security agents. Then it may deploy application services as container images on top of that VM.
That hybrid model gives each image type a clear job.
| Layer | Better artifact | Responsibility |
|---|---|---|
| Server baseline | VM image | OS, packages, agents, firewall defaults, Docker/runtime setup |
| Application release | Container image | App code, dependencies, runtime configuration |
| Runtime orchestration | Compose, systemd, Kubernetes, or scripts | Start, stop, network, restart, and coordinate services |
| Data | Backup or storage system | Persistent state and recovery |
| Configuration | Environment variables, secrets, config management | Runtime-specific settings |
| Recovery | Snapshots and backups | Fast rollback and data protection |
This is often the most realistic model for small teams running on cloud VMs. The VM provides stable infrastructure. The container image provides repeatable app deployment. The data layer remains protected separately.
The important rule is to avoid mixing responsibilities. Do not use a VM image as your only app release mechanism if the app changes often. Do not expect a container image to recreate your entire server security baseline.
Image Sprawl Is the Hidden Cost
Both models can create sprawl.
Container image sprawl happens when teams generate many images, tags, variants, base images, and unused versions without clear retention or ownership. VM image sprawl happens when teams create many server templates, each with slightly different packages and unknown patch status.
| Sprawl type | Symptoms | Risk |
|---|---|---|
| Container image sprawl | Too many tags, unclear latest version, old vulnerable images | Confusing deploys and security risk |
| VM image sprawl | Many similar templates, unknown owner, stale OS packages | Drift and unpatched baselines |
| Base image sprawl | Different teams use different language or OS bases | Inconsistent vulnerability management |
| Environment sprawl | Dev, staging, and production use different artifacts | “Works in staging” failures |
| Manual image changes | Images are updated outside a repeatable process | No reliable rebuild path |
The fix is not necessarily more tooling. The fix is ownership.
Every important image should have an owner, purpose, refresh rhythm, and retirement rule. If nobody knows why an image exists, it probably should not be a production dependency.
How Container and VM Image Planning Applies on Raff
Raff gives developers enough control to use either model.
For container-based deployments, Raff Linux VMs provide full root access, SSH key authentication, Docker-ready infrastructure, KVM virtualization, NVMe SSD storage, unmetered bandwidth, and fast deployment. That makes a Raff VM a practical host for Docker, Docker Compose, containerized web apps, workers, automation tools, and development environments. Raff Linux VM
For VM-image-driven deployments, Raff’s support for standard Linux distributions, custom ISO workflows, Windows VMs, and full administrative control helps teams standardize machine-level environments when the OS matters. Raff’s Windows VM page lists Windows Server 2019, 2022, and 2025, full administrator access, RDP access, and pricing from $9.99/month. Raff Windows VM
A practical Raff model looks like this:
| Workload | Recommended image approach |
|---|---|
| Small web app | Container image on a Linux VM or standard VM deployment |
| Multi-service app | Container images with Docker Compose on a VM |
| Hardened server baseline | VM image or provisioning model |
| Windows application | Windows VM image or template |
| Legacy application | VM image if machine state is hard to separate |
| Frequent app releases | Container image release pipeline |
| Repeatable infrastructure | VM image, cloud-init, or Infrastructure-as-Code |
The design rationale is simple: Raff should not force one deployment artifact. Developers should be able to run a simple VM, containerize the app, standardize a server baseline, or combine those patterns as the workload matures.
Aybars’ practical angle for this guide is that image choice should follow ownership. If the application team owns rapid releases, container images usually fit. If the infrastructure team owns a trusted server baseline, VM images matter. If both layers are important, use both deliberately.
Common Image Strategy Mistakes
Treating container images and VM images as the same thing.
A container image packages the app layer. A VM image packages the machine layer.
Baking secrets into images.
Secrets should not live permanently inside container images or VM images.
Using VM images for every app release.
This can make frequent releases slower and heavier than necessary.
Using container images without managing the host.
The container still depends on the VM, kernel, storage, firewall, monitoring, and backup strategy.
Letting image tags become unclear.
A tag like latest is convenient, but production needs clear versioning and rollback confidence.
Keeping stale golden images.
A VM image that is never patched becomes a fast way to deploy old risk.
Forgetting persistent data.
Images recreate software. They are not a complete data protection strategy.
A Practical Image Policy for Small Teams
A small-team image policy should be simple enough to follow.
| Policy area | Recommended baseline |
|---|---|
| Application releases | Use container images when the app changes frequently |
| Server baselines | Use VM images or provisioning automation when OS consistency matters |
| Versioning | Keep clear image versions for production rollback |
| Secrets | Store secrets outside images |
| Updates | Rebuild container images and refresh VM images on a regular cadence |
| Ownership | Assign an owner for each production image |
| Retention | Remove stale images after a defined review period |
| Recovery | Use backups and snapshots separately from image strategy |
| Documentation | Record what each image contains and when it should be used |
The goal is not to create enterprise platform overhead. The goal is to prevent confusion when the team needs to deploy, patch, roll back, or rebuild.
The Right Image Strategy Keeps Deployment Understandable
Container images and VM images are not enemies. They package different layers.
Use container images when the application needs to move, release, and roll back cleanly. Use VM images when the whole server environment needs to be consistent. Use both when the VM provides the trusted base and containers provide repeatable app releases.
For related reading, this guide should link to Raff’s Docker vs Virtual Machines guide, VM Provisioning Models guide, Docker vs Podman guide, Kubernetes vs Docker Compose guide, Single Server vs Multi-Server Architecture guide, and Infrastructure-as-Code guide.
On Raff, the practical path is to choose the image model that matches the layer you are standardizing. If you are standardizing app releases, use container images. If you are standardizing server baselines, use VM images. If you need both, keep the responsibilities separate so deployment remains clear as the system grows.