Managed model hosting or self-hosted Kubernetes for inference?

It depends on what you optimize for:

Choose managed model hosting if you want:

• Fastest time to production
• Minimal ops overhead
• Auto-scaling and built-in reliability
• Simple deployment for a few models/endpoints
• Predictable API-based usage

Best when:

• You’re early-stage
• You don’t have an MLOps/platform team
• Your traffic is moderate or bursty
• You can accept platform constraints and cost per request

Choose self-hosted Kubernetes for inference if you want:

• Maximum control over runtime, scaling, networking, and security
• Lower unit cost at sustained high volume
• Custom model serving stack (vLLM, Triton, TGI, Ray Serve, etc.)
• GPU scheduling and bin-packing optimization
• Data residency / compliance requirements
• Multi-model or multi-tenant platform needs

Best when:

• You have steady or high throughput
• You need specific hardware/network topology
• You have platform engineering expertise
• You need to tune latency/cost aggressively

Practical tradeoff

• Managed hosting = cheaper operationally, often more expensive per token/request.
• Kubernetes self-hosting = more engineering effort, often cheaper at scale.

A simple rule

• < 1–3 production models or low/moderate traffic: managed hosting
• High traffic, strict compliance, or custom serving needs: Kubernetes
• Unsure: start managed, migrate once cost/constraints justify it

Hybrid is common

Many teams use:

• Managed hosting for experimentation and low-traffic endpoints
• Kubernetes for core/high-volume workloads
• Sometimes managed GPUs + self-managed serving as a middle ground

If you want, I can give you a decision matrix for your specific case (traffic, model size, latency target, budget, compliance).