do I need Kubernetes for AI apps?

Not necessarily.

You do not need Kubernetes for most AI apps, especially if you’re:

• building a prototype or MVP
• running a single model/API
• serving low traffic
• using managed AI services like OpenAI, Anthropic, AWS Bedrock, Vertex AI, etc.

When Kubernetes is overkill

If your AI app is basically:

• a frontend
• a backend API
• calls to an LLM API
• maybe a vector database

…then simpler deployment options are usually better:

• Serverless: Vercel, Cloud Run, AWS Lambda
• Containers without K8s: Docker on a VM, ECS, App Runner, Cloud Run
• Managed platforms: Render, Fly.io, Railway, Azure Container Apps

When Kubernetes starts making sense

Kubernetes can be useful if you need:

• many services with complex networking
• high availability and autoscaling
• GPU workloads you want to manage yourself
• custom model hosting at scale
• multiple teams/environments
• portable infrastructure across clouds/on-prem
• fine-grained control over deployments, rollouts, and resource limits

For AI specifically

Kubernetes is more common when you’re doing:

• self-hosted model inference
• training pipelines
• batch jobs / distributed workloads
• GPU scheduling
• MLOps platforms with many moving parts

But if you’re just calling a hosted model API, Kubernetes usually adds complexity without much benefit.

Simple rule of thumb

• Start without Kubernetes
• Use it only when operational complexity demands it

If you want, I can also give you a “Kubernetes vs serverless vs Docker VM” decision tree for AI apps.