AI & ML
Cloud AI/ML Cost Comparison: AWS SageMaker vs Azure ML vs Google Vertex AI vs OCI Data Science
The Hidden Cost of AI/ML in Cloud
AI and ML workloads are the fastest-growing category of cloud spend — and the most opaque in terms of pricing. Understanding the full cost model of each platform is critical before committing to a provider.
Platform Overview
| Platform | Managed ML Service | Key Differentiator |
|---|---|---|
| AWS | SageMaker | Most mature, broadest feature set |
| Azure | Azure Machine Learning | Enterprise MLOps, Microsoft ecosystem |
| GCP | Vertex AI | Best AutoML, BigQuery integration |
| OCI | Data Science | Cost advantage, Oracle DB integration |
Training Infrastructure Costs
For model training, you pay for the GPU or CPU instances used. Cost comparison for a 24-hour training job on 1× NVIDIA A100:
| Provider | Instance | $/hr | 24hr Training Job |
|---|---|---|---|
| AWS SageMaker | ml.p4d.24xlarge | $32.77 | $786 |
| Azure ML | Standard_ND96asr_v4 | $27.20 | $653 |
| GCP Vertex AI | a2-highgpu-1g | $3.67/hr + Vertex overhead | $110-150 |
| OCI Data Science | GPU.A100 | $5.60 | $134 |
Inference / Endpoint Costs
Running a real-time inference endpoint (1× A10G GPU, 24/7):
| Provider | Instance | Monthly Cost |
|---|---|---|
| AWS SageMaker (ml.g5.xlarge) | ml.g5.xlarge | $735/month |
| Azure ML (NV6ads A10) | NV6ads A10 v5 | $663/month |
| GCP Vertex AI (g2-standard-8) | g2-standard-8 | $768/month |
| OCI Data Science | GPU.A10 | $1,642/month |
LLM API Costs — Foundation Model Access
| Model | Provider | Input $/M tokens | Output $/M tokens |
|---|---|---|---|
| Claude 3.5 Sonnet | AWS Bedrock | $3.00 | $15.00 |
| GPT-4o | Azure OpenAI | $2.50 | $10.00 |
| Gemini 2.0 Pro | GCP Vertex AI | $1.25 | $5.00 |
| Llama 3.1 70B | AWS Bedrock | $0.99 | $0.99 |
Cost Optimisation for AI/ML
- Spot/Preemptible instances for training: 60-90% discount — just ensure checkpoint-based training
- Use smaller models where possible: Claude 3.5 Haiku at $0.25/M tokens vs Claude 3.5 Sonnet at $3/M — 12x cheaper
- Batch inference vs real-time: AWS SageMaker Batch Transform is ~70% cheaper than real-time endpoints for non-time-sensitive inference
- GCP's cheap compute: Vertex AI with standard instances is often cheapest for CPU-based ML training
For training, GCP Vertex AI and OCI Data Science consistently offer lower costs. For inference APIs, compare carefully by token volume — GCP Gemini is cheapest for high-volume inference applications.
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