Do I need Kubernetes to serve my own model?

No — you do not need Kubernetes to serve your own model.

Common ways to serve a model

You can deploy a model with much simpler setups, for example:

• Single server / VM

  • Run a FastAPI, Flask, or gRPC app
  • Load the model in memory and serve predictions
  • Good for prototypes and modest traffic

• Docker

  • Package the app and model together
  • Run it anywhere Docker is supported
  • Often enough for production on a single machine

• Managed model hosting

  • Services like AWS SageMaker, Google Vertex AI, Azure ML, Hugging Face Inference Endpoints, etc.
  • Easier operations, less infrastructure work

• Serverless / container platforms

  • Cloud Run, ECS/Fargate, Fly.io, Render, Railway, etc.
  • Good if you want autoscaling without managing Kubernetes

When Kubernetes makes sense

Kubernetes is useful if you need:

• Multiple replicas with load balancing
• Autoscaling across many machines
• High availability / failover
• Rolling deployments and complex rollout control
• Many services and shared infrastructure
• Running large workloads across a cluster

When you probably don’t need it

You likely don’t need Kubernetes if:

• You’re serving one model or a small number of models
• Traffic is low to moderate
• You want the simplest possible deployment
• You’re still experimenting
• You’re fine with one server or a managed service

Practical recommendation

If you’re starting out, a good path is:

1. Build a simple API around your model
2. Put it in Docker
3. Deploy to a single VM or managed container service
4. Only move to Kubernetes if you hit scaling/operational needs

If you want, I can suggest the simplest deployment option for your setup based on:

• model type
• expected traffic
• cloud/provider
• latency requirements