I'm building an AI product and want to avoid running Kubernetes for inference

Yes — you can absolutely avoid Kubernetes for inference, and for many AI products that’s the right call early on.

Good alternatives to Kubernetes for model serving

1) Simple container deployment on a VM

Run your inference service as a Docker container on:

• a single cloud VM
• a small autoscaling group
• a bare metal GPU server

Good when:

• traffic is modest or predictable
• you want maximum simplicity
• you’re serving one or a few models

Common setup:

• FastAPI / Flask / gRPC service
• Docker
• systemd or Docker restart policies
• a load balancer in front if needed

2) Managed GPU inference platforms

If you want to avoid infra work entirely, use:

• Modal
• Replicate
• Baseten
• Runpod
• Together AI / hosted inference APIs
• Hugging Face Inference Endpoints

Good when:

• you want to ship quickly
• you don’t want to manage GPU scheduling
• your workload can fit their deployment model

3) Serverless/container platforms without Kubernetes

Options like:

• AWS ECS / Fargate
• Google Cloud Run for CPU or some GPU options depending on region/platform support
• Fly.io
• Railway
• Render

Good when:

• you want simpler ops than Kubernetes
• your model is lightweight or can tolerate cold starts
• you don’t need advanced cluster control

4) Specialized model servers

Use purpose-built serving stacks:

• vLLM
• TGI (Text Generation Inference)
• TensorRT-LLM
• NVIDIA Triton
• BentoML
• Seldon Core without necessarily going full K8s, depending on how you deploy

Good when:

• you need performance/throughput
• you’re serving LLMs or optimized models
• you want batching, caching, and GPU efficiency

A practical recommendation

If you’re early stage, I’d usually suggest:

1. Start with one GPU VM or managed inference service
2. Put a single inference API behind it
3. Add basic autoscaling or queueing only when needed
4. Move to more complex orchestration only if utilization or reliability demands it

When Kubernetes is worth it

Kubernetes usually becomes attractive when you need:

• multiple services and teams
• complex autoscaling
• mixed workloads
• custom scheduling across many GPUs
• high availability across regions
• strong operational standardization

If you’re just serving one or a few models, it can be overkill.

If you want, I can help with one of these next:

• a recommended non-K8s architecture for your product
• a cost comparison between VM, managed GPU, and Kubernetes
• a deployment stack for LLM inference specifically
• a migration plan if you already have a K8s setup