Docker vs Kubernetes for AI: Comparison for ML Deployment
Compare Docker and Kubernetes on ML model deployment, scale, and complexity. Discover which container strategy best fits your AI infrastructure.
Docker
The standard container platform for packaging and running applications. Docker packages AI models, dependencies, and inference servers in images. Simple to use on one machine or small clusters. The foundation for all container deployment.
Kubernetes
Container orchestration for managing distributed workloads. Kubernetes scales AI inference automatically, offers load balancing, and self-healing. KubeFlow, Seldon, and KServe add ML-specific features. Overkill for small deployments.
Comparison table
| Feature | Docker | Kubernetes |
|---|---|---|
| Scope | Containers — image and runtime | Orchestration — scheduling, scaling |
| Scale | One to dozens of containers | Hundreds to thousands of pods |
| ML tools | Docker Compose, custom scripts | KubeFlow, KServe, Seldon |
| Complexity | Low — docker run | High — clusters, operators |
| GPU | nvidia-docker, manual | Native GPU scheduling |
| Use case | Development, small production | Enterprise, multi-model, auto-scale |
Verdict
Docker is the foundation — you always need it. Kubernetes is the next step when scale and orchestration are critical. For most AI apps: Docker + managed service (Railway, Fly, ECS). For enterprise ML: Kubernetes.
Our recommendation
AVARC Solutions uses Docker for all AI deployments. We recommend Kubernetes only for clients with high inference load or multi-model serving. For typical RAG and LLM apps Docker and managed runtimes (Vercel, Railway) suffice.
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