Otimize seus Custos com a Calculadora de Preços do Google Vertex AI

Provides detailed cost estimations for Amazon SageMaker's comprehensive suite of machine learning services, including training, inference, and various components.

Similar to Google Vertex AI Pricing Calculator, it helps users estimate costs for a broad ML platform, but specifically for AWS SageMaker services like instance types, training duration, and model deployment, often highlighting free-tier allowances.

Offers cost estimations for Azure Machine Learning, integrating with the broader Azure ecosystem and its various compute and storage options.

Directly comparable to Vertex AI's calculator, it allows users to estimate costs for building, training, and deploying ML models on Azure, including considerations for different compute capacities, savings plans, and other consumed Azure services.

Focuses on transparent, token-based pricing for its leading generative AI models (LLMs, image generation, etc.), with options for different model sizes and usage tiers.

Unlike Vertex AI's calculator which covers a broad ML platform, OpenAI's pricing is primarily for direct API consumption of specific AI models, detailing costs per token for input and output, and sometimes offering free credits for new users.

Provides pricing for dedicated inference endpoints, training clusters, and data storage, catering to users leveraging open-source AI models and the Hugging Face ecosystem.

While Vertex AI offers a managed platform, Hugging Face's pricing focuses on providing compute resources for training and deploying models from its vast open-source library, with detailed hourly rates for different hardware configurations and a free tier for the Hub.

Offers cost estimation for various IBM watsonx AI models, allowing users to compare pricing, context windows, and capabilities for different use cases.

Similar to OpenAI, this calculator focuses on the cost of using specific AI models (LLMs) from IBM's watsonx platform, providing a direct comparison for model-specific AI consumption rather than the broader ML platform cost estimation of Vertex AI.