Docker packages an AI service and its runtime dependencies. Kubernetes schedules and operates containers across a cluster. They are not competing replacements: most Kubernetes workloads are containerized, and many AI applications never need Kubernetes.

Short answer

Use Docker or Docker Compose for local development, a single model server, a small API, or a bounded worker stack on one host. Adopt Kubernetes only when multi-node scheduling, independent scaling, GPU allocation, controlled rollouts, service discovery, or platform-wide reliability justifies its operational cost.

Workload	Likely starting point
Local Ollama or evaluation environment	Docker
API calling hosted model providers	Docker on PaaS/VPS, or serverless
Small queue plus workers	Compose or managed container platform
Several inference services across GPU nodes	Kubernetes may fit
Multi-team platform with rollout and policy needs	Kubernetes may fit

When Docker is enough

A container provides a repeatable filesystem, runtime, native libraries, and startup command. Compose can connect an API, worker, cache, database, and local model runtime on one machine. That is often sufficient until scaling or availability requirements are measured.

Docker alone does not provide multi-node scheduling, automatic replacement across hosts, or a complete production control plane. A managed container platform can fill the gap without requiring your team to operate Kubernetes.

When Kubernetes earns its cost

Kubernetes becomes useful when you need to schedule many services across node pools, isolate workloads, perform controlled rollouts, scale components separately, or use a standardized platform across teams. It adds APIs and controllers; it does not automatically make inference reliable or cheap.

Before adopting it, confirm ownership for cluster upgrades, networking, storage, observability, capacity, security policy, and incident response.

GPU scheduling

GPU workloads require compatible host drivers, container runtime integration, device plugins, schedulable resources, and nodes with enough VRAM. A pod requesting a GPU may remain Pending even when the cluster has unused CPU.

Separate training, batch, and latency-sensitive inference pools where contention matters. Monitor utilization and model-loading time; scheduling a device does not prove it is used efficiently. See Pending GPU pods.

RAM, VRAM, and model storage

Container memory limits govern system RAM, while model execution may also depend on accelerator memory. Kubernetes cannot fix a model that does not fit. Quantization, batch size, context length, KV cache, concurrency, and runtime overhead all affect capacity.

Model weights are large and slow to pull repeatedly. Plan image size, persistent caches, node-local storage, warm-up, readiness, and disk pressure. Avoid baking every model into a giant application image.

Scaling patterns

Scale stateless AI gateways independently from model servers and job workers. Queue depth, concurrency, first-token latency, GPU utilization, and token throughput can be more useful signals than CPU alone.

For provider-backed APIs, rate limits and spending budgets may constrain scaling before compute does. Link autoscaling to backpressure and AI gateway cost controls.

Observability and rollouts

Track request IDs across gateway, queue, model, and tools. Combine infrastructure metrics with resolved model, token usage, latency, fallback, quality evaluation, and cost. Release model or prompt changes gradually and keep a rollback path.

Decision rule

Start with the smallest platform the team can operate reliably. Use Docker for packaging and bounded deployments; add Kubernetes when demonstrated scheduling and platform requirements outweigh complexity. Continue with AI Operations, AI Deployment & Hosting, local AI, and AI Kubernetes troubleshooting.