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AI model hosting and cloud infrastructure expert reference guide
AI infrastructure expert
Hosting & managing models
on cloud infrastructure
A complete reference for deploying, scaling, and operating AI/ML models in production — from raw GPU instances to fully managed inference APIs.
3
Deployment patterns
5
Core infra layers
12+
Key considerations
Infrastructure layers
Compute
GPU / TPU instances, spot vs reserved, right-sizing for inference vs training loads.
Serving layer
Model server (Triton, vLLM, TGI), batching, concurrency, and latency tuning.
Orchestration
Kubernetes / EKS / GKE, Helm charts, pod autoscaling, node affinity for GPU nodes.
Networking
VPC, load balancing, API gateway, ingress controllers, and TLS termination.
Observability
Metrics (latency, throughput, GPU util), logging, tracing, and alerting pipelines.
Model deployment pipeline
Train / Fine-tune
S3 / GCS artifact
Package
Docker + weights
Register
Model registry
Stage & test
Shadow traffic
Promote
Blue-green / canary
Monitor
Drift + alerts
Deployment strategies
Fully managed
- Bedrock, Vertex AI, Azure ML
- No infra management
- Pay-per-token pricing
- Limited customization
Self-hosted on cloud
- EC2 / GCE GPU instances
- Full model control
- vLLM / TGI serving
- Ops overhead
Serverless inference
- Modal, RunPod, Replicate
- Scale-to-zero
- Cold-start tradeoff
- Good for bursty loads
Key performance metrics
Time to first token
~120ms
Target < 200ms
Tokens / second
~1.4k
per A100 GPU
GPU utilization
~78%
Well-batched
Error rate
0.02%
p99 SLA met
Cost / 1M tokens
$0.80
Optimizable
Recommended stack
vLLM
High-throughput LLM serving with continuous batching & paged attention
Serving
Kubernetes + KEDA
Container orchestration with event-driven autoscaling on GPU nodes
Orchestration
MLflow / W&B
Experiment tracking, model registry, and artifact versioning
Registry
Prometheus + Grafana
Metrics collection, dashboards, and alerting for inference endpoints
Observability
Terraform + Atlantis
Infrastructure as code with GitOps workflows for cloud provisioning
IaC
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