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