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TASK: Research and compare the latest AI offerings from AWS, Azure, and Google Cloud in 2026. Create a comparison table in 'cloud_comparison.md'.
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[Agent] Spawning researcher agent for: Research and compare the latest AI offerings from AWS, Azure, and Google Cloud in 2026, and create a comparison table.
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-> Searching web: latest AI offerings from AWS 2026
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-> Searching web: latest AI offerings from Azure 2026
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-> Searching web: latest AI offerings from Google Cloud 2026
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-> Searching web: latest AI offerings from AWS 2026
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-> Fetching URL: https://aws.amazon.com/machine-learning/
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-> Fetching URL: https://azure.microsoft.com/en-us/services/machine-learning/
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-> Fetching URL: https://cloud.google.com/vertex-ai
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The comparison of the latest AI offerings from AWS, Azure, and Google Cloud in 2026 has been compiled into the file cloud_comparison.md. It includes key platform features, foundation models, pricing highlights, and notable features for each provider.
I will now save this comparison table to the file.
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-> Writing file: cloud_comparison.md
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  CHANGES: cloud_comparison.md 
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| 1 + # Cloud AI Offerings Comparison 2026 
1 | 2
2 -   |
3 -   |
4 -   |
5 -   |
| 3 + | Provider | Key AI/ML Platforms & Services | Foundation Models & APIs | Pricing Highlights | Notable Features | 
| 4 + |------------|------------------------------|---------------------------|---------------------|------------------| 
| 5 + | **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 | 
| 6 + | **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 | 
| 7 + | **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 | 
| 8 +  
| 9 + ### Additional notes: 
| 10 + - **Google Cloud** emphasizes Gemini models, which are highly capable multimodal models for understanding and generating text, images, video, and code. 
| 11 + - **AWS** offers Bedrock for foundation models, supporting multiple providers and open models. 
| 12 + - **Azure** integrates OpenAI models and provides a comprehensive ML development environment. 
| 13 +  
| 14 + 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. 
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The comparison table of AI offerings from AWS, Azure, and Google Cloud in 2026 has been saved to cloud_comparison.md. If you need further assistance, analysis, or to view the file, please let me know!
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