Cloud AI Offerings Comparison 2026
| Provider | Key AI/ML Platforms & Services | Foundation Models & APIs | Pricing Highlights | Notable Features |
|---|---|---|---|---|
| AWS | - SageMaker (ML development, AutoML, deployment) - Amazon Bedrock (foundation models API) |
- Supports various foundation models via Bedrock - Open models like Llama 3 |
- Pay-as-you-go, with custom pricing for models and infrastructure | - Extensive model marketplace - Custom training and tuning - MLOps tools |
| Azure | - Azure Machine Learning (ML studio, AutoML, deployment) - Azure OpenAI Service |
- Supports OpenAI models, custom models, and open-source models | - Pay based on compute, storage, and API calls | - Integrated with Azure ecosystem - MLOps and model management - Enterprise-grade security |
| Google Cloud | - Vertex AI (unified ML platform, generative AI) - Vertex AI Studio, Agent Builder |
- Gemini models (latest multimodal models) - Supports open-source models like Llama 3 |
- Starting at $0.0001 per token/character - Custom training costs vary by resources used |
- Advanced multimodal models (Gemini 3) - Extensive model discovery and testing - MLOps, evaluation, and deployment tools |
Additional notes:
- Google Cloud emphasizes Gemini models, which are highly capable multimodal models for understanding and generating text, images, video, and code.
- AWS offers Bedrock for foundation models, supporting multiple providers and open models.
- Azure integrates OpenAI models and provides a comprehensive ML development environment.
This comparison reflects the state of AI offerings in 2026, highlighting the focus on multimodal capabilities, enterprise readiness, and flexible deployment options across all three cloud providers.