Kubernetes vs Docker vs OpenStack: Stop Comparing Tools at Different Layers
A practical boundary guide: Docker packages and runs containers, Kubernetes orchestrates and keeps services stable at scale, and OpenStack turns datacenter hardware into an IaaS resource pool (VM/network/storage).
People often put Kubernetes, Docker, and OpenStack into one comparison table and ask “which one should we use in production?”.
That question is usually wrong, because these tools don’t live at the same layer.
- Docker is about packaging an app into an image and running it as a container (especially on a single machine).
- Kubernetes is about running many containers reliably across many machines: scheduling, rollout, self-healing, scaling.
- OpenStack is about turning a rack of hardware into a self-serve IaaS pool: VM, network, block storage, multi-tenant projects and quotas.
If you picture a typical stack, it’s often:
OpenStack (or public cloud IaaS) → VMs → Kubernetes cluster → containers (containerd/Docker build chain)
Below is how I explain the boundaries using the scenarios teams actually run into.
One-line boundaries (the fast mental model)
- If your main problem is shipping your app consistently, start with Docker.
- If your main problem is operating many services at scale, Kubernetes is the real answer.
- If your main problem is building an internal IaaS and handing out VMs with isolation/quota, that’s OpenStack territory.
More bluntly:
- Docker answers “how do I run it?”.
- Kubernetes answers “how do I keep it running, safely update it, and scale it?”.
- OpenStack answers “how do I provide compute/network/storage as a cloud-like pool?”.
Why people keep comparing them
Because they all “deliver compute”, but the unit of delivery is different:
- Docker delivers images/containers
- Kubernetes delivers Pods/Services/Deployments (and controllers)
- OpenStack delivers VMs/networks/volumes (and tenant projects/quotas)
Comparing them directly is like comparing a wrench, a fleet scheduler, and a highway system.
A practical comparison table
| What you care about | Docker | Kubernetes | OpenStack |
|---|---|---|---|
| Primary job | Container packaging + runtime (single host) | Orchestration across nodes: rollout, self-heal, scale | IaaS resource pool: VM/network/storage + multi-tenancy |
| What you manage | Containers/processes | Service replicas + traffic entry + policies | VMs, networks, volumes, quotas |
| Who restarts failures | You (scripts/systemd) | Controllers keep reconciling desired state | VM HA is possible; app-level still on you |
| Multi-tenancy | Weak by default | Namespaces + policy (needs governance) | Stronger IaaS-grade isolation |
| Complexity | Low | Medium to high | High |
Scenario 1: One box (or a couple), just get it running
If you’re on one VM or a few small servers, don’t rush into Kubernetes.
A sane approach
- Containerize your app with Docker
- Use docker-compose (or systemd + docker run) to run the stack
What bites teams later
- “It runs” isn’t “it’s operable”: logs, config, backups, upgrades.
- Stateful services need a real backup/restore story, not random bind mounts.
In short: small scale → Docker/Compose first, standardize delivery, keep it simple.
Scenario 2: More services, frequent releases, outages start to hurt
When you start needing:
- safe rollouts and rollbacks
- auto scaling
- self-healing after node/container failures
- repeatable deployments via pipelines
That’s Kubernetes’ sweet spot.
What you get in practice
- Rollouts + rollback without babysitting servers
- Self-healing and rescheduling
- Stable service entry via Service/Ingress
But Kubernetes isn’t “install and forget”
You still need basics around it:
- image registry + tagging rules
- CI/CD for release + rollback
- monitoring/logging/alerting
- RBAC + network policy (at least the minimal guardrails)
In short: Kubernetes reduces operational pain at scale, but only if your platform basics are there.
Scenario 3: You want an internal cloud that hands out VMs to many teams
If your goal is:
- multiple departments/projects requesting VMs
- quotas and isolation
- self-serve provisioning
That’s IaaS. This is where OpenStack makes sense.
What OpenStack is good at
- compute/network/storage abstraction
- project-based multi-tenancy and quotas
- a platform workflow around VM provisioning
The tradeoff
- lots of components
- upgrades and troubleshooting are real work
In short: OpenStack gives you IaaS building blocks, not application delivery.
The most common real-world setup: they stack
A very normal setup is:
- OpenStack provides VMs (or you’re on public cloud VMs)
- Kubernetes runs on those VMs
- applications ship as container images
Treating them as “either/or” is how teams paint themselves into a corner.
Three classic mismatches (easy to spot in reviews)
1) Using Kubernetes as a packaging tool
If image/build discipline doesn’t exist yet, a cluster won’t fix it.
You’ll just get more YAML and slower debugging.
2) Using Docker as production orchestration
Hand-rolled scripts for scaling/rollouts/traffic switching usually fail when you’re under pressure.
3) Using OpenStack to solve releases
You’ll get VMs faster, but you still need rollout strategies, service discovery, and operational controls.
A decision checklist you can paste into an RFC
- What are you delivering: containers/services, or VMs?
- Do you need self-healing/rollouts/scaling/service discovery? (Kubernetes)
- Do you need IaaS-grade multi-tenancy/quota/network resource pooling? (OpenStack)
- Can your team operate and upgrade a control plane long-term?
- Are registry/CI/CD/observability/access controls ready?
My default advice when teams are unsure:
Start by containerizing with Docker (standardize delivery). Move to Kubernetes when the operational pain is obvious. Consider OpenStack only when you truly need an internal IaaS resource pool.