cloud ai
**Cloud platforms for AI/ML** provide **on-demand GPU compute and managed services for training and deploying machine learning models** — offering instances with A100s, H100s, and other accelerators alongside managed ML platforms like SageMaker, Vertex AI, and Azure ML, enabling teams to scale AI workloads without owning hardware.
**Why Cloud for AI/ML?**
- **No Capital Investment**: Pay for GPUs as needed, no $40K H100 purchases.
- **Elastic Scale**: Scale from 0 to 1000 GPUs for training, back to 0.
- **Managed Services**: Training, serving, monitoring handled by platform.
- **Latest Hardware**: Access H100s, H200s as they release.
- **Global Availability**: Deploy close to users worldwide.
**GPU Instance Comparison**
**High-End Training Instances**:
```
Instance | GPUs | GPU Memory| $/hr (On-Demand)
------------------|-----------|-----------|------------------
AWS p5.48xlarge | 8× H100 | 640 GB | ~$98
GCP a3-megagpu-8g | 8× H100 | 640 GB | ~$100
Azure ND H100 v5 | 8× H100 | 640 GB | ~$98
Lambda Cloud 8xH100| 8× H100 | 640 GB | ~$85
```
**Inference Instances**:
```
Instance | GPUs | GPU Memory| $/hr (On-Demand)
------------------|-----------|-----------|------------------
AWS g5.xlarge | 1× A10G | 24 GB | ~$1.00
GCP g2-standard-4 | 1× L4 | 24 GB | ~$0.70
Azure NC A100 v4 | 1× A100 | 80 GB | ~$3.67
AWS inf2.xlarge | 1× Inferentia2| 32 GB | ~$0.75
```
**Cost Optimization**
**Spot/Preemptible Instances**:
```
Type | Discount | Risk | Use For
--------------|----------|-----------------|------------------
Spot (AWS) | 60-90% | Interruption | Training w/checkpoints
Preemptible | 60-80% | 24hr max | Batch jobs
Spot Block | 30-50% | 1-6hr guaranteed| Short jobs
```
**Reserved/Committed**:
```
Commitment | Discount | Best For
--------------|----------|------------------
1-year | 30-40% | Steady inference workloads
3-year | 50-60% | Long-term production
PAYG fallback | 0% | Burst capacity
```
**Managed ML Services**
**AWS SageMaker**:
```
Component | Purpose
--------------|----------------------------------
Studio | IDE for ML development
Training | Managed training jobs
Endpoints | Model serving
Pipelines | ML workflow orchestration
Ground Truth | Data labeling
```
**GCP Vertex AI**:
```
Component | Purpose
---------------|----------------------------------
Workbench | Managed notebooks
Training | Distributed training
Prediction | Serving endpoints
Pipelines | Kubeflow-based workflows
Feature Store | ML feature management
```
**Azure Machine Learning**:
```
Component | Purpose
---------------|----------------------------------
Designer | Drag-and-drop ML
AutoML | Automated model selection
Compute | Managed clusters
Endpoints | Deployment targets
MLflow | Experiment tracking
```
**Decision Framework**
```
Use Case | Provider Strength
--------------------------|------------------
Existing AWS shop | SageMaker
Google ecosystem | Vertex AI
Microsoft shop | Azure ML
Cost-sensitive | Lambda, RunPod, Vast.ai
Simplest experience | Replicate, Modal
Maximum control | Raw GPU instances
```
**Storage Options**
```
Service | Provider | Use Case | Cost
---------------|----------|--------------------|---------
S3 | AWS | Datasets, artifacts| $0.023/GB
GCS | GCP | Same | $0.020/GB
Azure Blob | Azure | Same | $0.018/GB
EFS/Filestore | Various | Shared model access| Higher
FSx for Lustre | AWS | High-perf training | $0.14/GB/mo
```
**Cloud Architecture for LLM Training**
```svg
```
**Quick Starts**
**AWS** (Launch GPU instance):
```bash
aws ec2 run-instances \
--image-id ami-xxx \
--instance-type p4d.24xlarge \
--key-name my-key
```
**GCP** (Create GPU instance):
```bash
gcloud compute instances create gpu-instance \
--zone=us-central1-a \
--machine-type=a2-highgpu-1g \
--accelerator=type=nvidia-tesla-a100,count=1
```
Cloud platforms are **the infrastructure foundation for AI at scale** — providing the elastic GPU compute and managed services that enable teams to train frontier models and deploy production AI systems without massive capital investment.