Do I need Kubernetes to run AI inference in production?

No — you do not need Kubernetes to run AI inference in production.

Kubernetes is one option, but whether you need it depends on your scale, reliability needs, deployment style, and team expertise.

You may not need Kubernetes if:

• You’re serving a single model or a small number of models
• Traffic is moderate or predictable
• You want simpler operations
• You’re deploying on:
  • a VM
  • managed PaaS
  • serverless/container platforms
  • a dedicated inference service

Common alternatives:

• Docker on a VM
• AWS ECS / Fargate
• Google Cloud Run
• Azure Container Apps
• Managed model serving like:
  • SageMaker
  • Vertex AI
  • Azure ML
  • Hugging Face Inference Endpoints
• Specialized inference servers like:
  • vLLM
  • Triton Inference Server
  • TorchServe
  • BentoML
  • Ray Serve

Kubernetes can make sense if:

• You need high availability and auto-scaling
• You run many models/services
• You want GPU scheduling across a cluster
• You have multiple teams and complex deployment workflows
• You already use Kubernetes for other production systems
• You need advanced routing, rollout, and isolation patterns

Tradeoff:

Kubernetes gives flexibility, but adds:

• operational complexity
• debugging overhead
• cluster management burden
• more moving parts for GPU workloads

Practical rule of thumb

• Start without Kubernetes if you can.
• Move to Kubernetes when simpler deployment options can’t meet your needs.

If you want, I can also give you a decision checklist for choosing between Kubernetes, managed serving, and plain Docker/VMs.