deployment-comparison.md

Deployment Options: Cloud vs VM vs HPC

Decision matrix for deploying distributed ML workloads.

Deployment Models

1️⃣ Managed Kubernetes (Cloud-Native)

Providers: EKS, GKE, AKS

Pros:

• ✅ Auto-scaling (pods + nodes)
• ✅ Self-healing (restarts, rescheduling)
• ✅ Declarative deployment (YAML, operators)
• ✅ Production observability (Prometheus, Grafana)
• ✅ Managed control plane

Cons:

• ❌ Complexity (K8s learning curve)
• ❌ Cost overhead (control plane, load balancers)
• ❌ Slight network latency vs bare metal

Best for: Production ML pipelines, elastic workloads, multi-tenant

2️⃣ Self-Hosted Kubernetes

Tools: Rancher, Kubeadm, Kind (dev)

Pros:

• ✅ Full control over infrastructure
• ✅ Cost savings (no managed fees)
• ✅ Custom networking (RDMA, InfiniBand)

Cons:

• ❌ Ops burden (upgrades, security, monitoring)
• ❌ Requires K8s expertise
• ❌ No auto-provisioning

Best for: On-prem deployments, compliance requirements

3️⃣ Virtual Machines (Terraform/Ansible/Puppet/Chef)

Providers: EC2, Compute Engine, Azure VMs

Pros:

• ✅ Simple, flexible
• ✅ Full SSH access
• ✅ No orchestration overhead

Cons:

• ❌ Manual scaling, failure handling
• ❌ No native job scheduling
• ❌ Ops complexity for multi-node

Best for: Fixed-size workloads, short experiments, debugging

4️⃣ HPC Clusters (Slurm)

Systems: NERSC, TACC, Summit

Pros:

• ✅ Maximum throughput (InfiniBand, NVLink)
• ✅ Massive scale (1000s of nodes)
• ✅ Specialized for scientific computing

Cons:

• ❌ Fixed resources (no elasticity)
• ❌ Queue wait times
• ❌ Batch-oriented (not for real-time)

Best for: Large-scale pretraining, scientific simulations

Decision Matrix

Factor	Managed K8s	Self-Hosted K8s	VMs	HPC
Setup Time	Minutes	Days	Minutes	Weeks
Scaling	Auto	Manual	Manual	Fixed
Fault Tolerance	Built-in	DIY	None	Minimal
Cost	Medium-High	Low-Medium	Low	High
Complexity	Medium	High	Low	High
Throughput	Good	Good	Good	Max
Flexibility	High	High	High	Low
Best For	Production ML	On-prem	Experimentation	LLM pretraining

Ray + Managed Kubernetes = Optimal Default

Why?

1. Elasticity: Ray's dynamic workers + K8s autoscaling
2. Reliability: Self-healing, declarative state
3. Simplicity: Managed control plane (EKS/GKE/AKS)
4. Production-ready: Observability, security, networking

Alternatives:

• VMs: Flexible but manual ops
• HPC: Fixed-size, batch workloads only

Resources

• Ray on Kubernetes
• AWS EKS Best Practices
• Terraform AWS Modules

TL;DR: Ray + managed K8s is the default for scalable production; VMs and HPC for specific use cases.