This decision gets framed too simply. Managed ML platforms are described as "easy," and self-hosted Kubernetes as "advanced." In reality, the choice depends on your workload shape, cost sensitivity, and operational maturity.
Whether you're looking at sagemaker vs kubernetes ml or vertex ai vs self-hosted, the goal is to pick the operating model that matches your current scale without creating a massive technical debt.
SageMaker and Vertex AI: Speed Over Customization
Managed platforms are strongest when speed to first production deployment is the bottleneck. They provide opinionated workflows and tight integration with cloud-native IAM and storage.
Technical Depth: Deploying a SageMaker Endpoint with Terraform
For teams staying managed, Infrastructure as Code (IaC) is still essential to avoid "click-ops" sprawl.
resource "aws_sagemaker_model" "example" {
name = "nlp-classifier-v1"
execution_role_arn = aws_iam_role.sagemaker_role.arn
primary_container {
image = "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:1.13-transformers4.26-gpu-py39-cu117-ubuntu20.04"
model_data_url = "s3://${var.model_bucket}/model.tar.gz"
}
}
resource "aws_sagemaker_endpoint_configuration" "example" {
name = "nlp-classifier-config"
production_variants {
variant_name = "all-traffic"
model_name = aws_sagemaker_model.example.name
initial_instance_count = 1
instance_type = "ml.g4dn.xlarge"
}
}
Self-Hosted Kubernetes: Control Over Economics
Kubernetes becomes attractive when you have multiple teams sharing GPU pools, or when you need custom serving runtimes that managed services don't support well.
Technical Depth: vLLM on Kubernetes (Self-Hosted)
If you are serving open-source LLMs, running vLLM on Kubernetes gives you direct control over continuous batching and quantization—features that are often restricted in managed "one-click" endpoints.
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-llama-3
spec:
replicas: 2
template:
spec:
containers:
- name: vllm-server
image: vllm/vllm-openai:latest
args: ["--model", "meta-llama/Meta-Llama-3-8B-Instruct"]
ports:
- containerPort: 8000
resources:
limits:
nvidia.com/gpu: 1
For a deeper look at what it takes to run this at scale, see Building an Internal ML Platform on Kubernetes.
The Crossover Point: When to Migrate?
If you have fewer than 10 production models, managed services are usually the rational choice. However, as your estate grows to 20+ models or multiple teams, the unit economics of Kubernetes become undeniable.
If you find yourself hitting the limits of SageMaker's abstractions, it might be time for a step-by-step migration to Kubernetes.
Final Takeaway: Choosing the Right Path with Resilio Tech
Managed vs self-hosted ml platform is not a maturity badge; it is a fit question. SageMaker and Vertex AI are excellent for speed and early-stage development. Kubernetes, combined with tools like KubeRay, Argo Workflows, and vLLM, is the better long-term operating model for complex, cost-sensitive, and multi-model environments.
At Resilio Tech, we help companies make this decision with data, not dogma. We specialize in both managed cloud ML services and self-hosted Kubernetes platforms. Whether you need to optimize your SageMaker spend or architect a custom ML platform from scratch, our team provides the engineering expertise to ensure your infrastructure scales with your business.
Not sure if you've outgrown your managed ML platform? Contact Resilio Tech for an architecture review and platform strategy session.
