Docker packages an AI application and everything it depends on into a container that runs the same way on any machine. Kubernetes manages many containers across many machines: it decides where each one runs, restarts it when it fails and scales it up or down. For AI, the practical rule is simple. If you have one AI system, or a few that each do a fixed job, Docker with Docker Compose is enough. You need Kubernetes when several teams share several GPU servers, when the service must survive a machine failure, or when your IT department already runs everything else on it. Adopting Kubernetes for a single AI box adds complexity without adding capability.
Docker vs Kubernetes: what does each do?
| Docker (with Compose) | Kubernetes | |
|---|---|---|
| Scope | Containers on one machine | Containers across a cluster of machines |
| Typical AI use | Model server, vector database and chat interface on one system | Shared GPU pool for many teams and services |
| GPU access | NVIDIA Container Toolkit | NVIDIA GPU Operator and device plugin |
| Failure handling | Restart policy on the same host | Reschedules work to another node |
| Scaling | Manual | Automatic, by policy |
| Skills needed | Basic Linux and containers | A platform team, or a managed distribution |
| Time to first result | Hours | Days to weeks |
Why are containers the default for AI?
AI software has fragile dependencies: a specific driver, CUDA version, framework build and Python libraries all have to match. A container freezes that combination. Model servers such as vLLM, packaged services such as NVIDIA NIM, vector databases and chat front-ends are all distributed as container images, and NVIDIA's DGX systems ship ready to run them. Whichever side of this comparison you land on, you will be using containers.
What changes when GPUs are involved?
- GPUs don't share like CPUs. By default a container gets a whole GPU. Sharing one GPU between workloads needs time-slicing or hardware partitioning; see NVIDIA MIG vs vGPU.
- Models are large and slow to start. A container that must load 60 GB of weights can't be spun up in a second, so the autoscaling that works for web services is much less elastic for AI. Where the weights are stored matters; see data locality.
- Scheduling is about scarcity. The main job of orchestration in AI is deciding which team gets which GPU and when, with queues and quotas.
- Drivers must match across nodes. The GPU Operator automates driver and runtime installation on every node, which is one of the real benefits of Kubernetes for GPU fleets.
When is Docker enough?
- One AI system, such as a GB10 desktop or a single GPU workstation or server.
- A fixed set of services: one or two models, a database, a web interface.
- A small team that can tolerate a short outage while the machine restarts.
- No dedicated platform engineers.
A single Compose file describing the model server, the vector database and the chat front-end is easy to back up, easy to read and easy to rebuild on new hardware. This covers the great majority of SME deployments.
When do you need Kubernetes?
- Several GPU servers shared by several teams. You need quotas, queues and fair scheduling.
- Availability requirements. The service must keep running if one node fails. See the single point of failure problem.
- Many models and services. Dozens of deployments with different versions and owners.
- An existing Kubernetes estate. If IT already runs it, AI workloads should join it for consistent security, monitoring and access control.
- Software that expects it. Multi-node serving frameworks and techniques such as disaggregated inference are built for Kubernetes.
Our guide to integrating a DGX Station with Kubernetes covers that case.
Kubernetes solves the problems of having many machines and many teams. If you have one machine and one team, it mostly gives you its own problems instead.
Is there something in between?
- Lightweight Kubernetes. Distributions such as K3s or MicroK8s run on one or a few nodes with far less overhead, a reasonable step for two to four AI systems.
- A job scheduler. Research groups often use Slurm to queue training jobs on shared GPUs instead of Kubernetes.
- GPU partitioning on one host. Splitting one large GPU between teams can postpone the need for a cluster.
- Managed platforms. Commercial Kubernetes distributions and AI platforms reduce the operational load, at a licence cost.
What does this mean for hardware?
| Setup | Orchestration | Also needed |
|---|---|---|
| One desktop AI system | Docker Compose | Backup of the Compose file, models and data |
| Two to four AI systems | Compose per machine, or K3s | Shared NAS, 10GbE or faster |
| A rack of GPU servers | Kubernetes with the GPU Operator | Shared storage, high-speed networking, monitoring |
A cluster needs shared storage for models and data and a network fast enough to move them. Monitoring becomes essential; see monitoring with DCGM, Prometheus and Grafana.
Buying in Singapore
For a single-system Docker deployment, the NVIDIA DGX Spark 4TB (S$9,450) or HP ZGX Nano G1n 4TB (S$11,499). For multi-GPU servers, the NVIDIA RTX PRO 6000 Blackwell Server Edition (S$32,999). For shared model and data storage, the Synology RS2423+ (S$4,002) with a dual-port 10GbE card (S$387.60) (listed 11 Nov 2026; prices change; ask for a current quotation). GPU servers such as the Lenovo ThinkSystem SR675 V3 are price on request. To start, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
What is the difference between Docker and Kubernetes? Docker builds and runs containers on a single machine. Kubernetes orchestrates containers across a cluster, handling placement, restarts, scaling and access between many machines and teams.
Do I need Kubernetes to run AI models? No. A single AI system running a few services works well with Docker and Docker Compose. Kubernetes is worthwhile when several GPU servers are shared or high availability is required.
How do containers access the GPU? On a single host, the NVIDIA Container Toolkit exposes GPUs to containers. In Kubernetes, the NVIDIA GPU Operator installs drivers and makes GPUs schedulable resources.
Is there a lighter alternative to Kubernetes? Yes. K3s and MicroK8s are lightweight distributions for small clusters, and schedulers such as Slurm are common for shared training workloads.
