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How to calculate your AI costs on Google Cloud
- Understanding the costs of enterprise AI on Google Cloud is crucial for sustainable AI adoption and maximizing value from investments.
- Google Cloud offers various AI services with different pricing structures, making it essential to manage costs to avoid budget overruns and stalled projects.
- The major cost components of running AI workloads on Google Cloud include model serving, training and tuning, cloud hosting, training data storage, application layer costs, and operational support.
- An example scenario of a retail customer deploying a chatbot illustrates how costs can be estimated for model serving, training, cloud hosting, storage, application layer, and operational support.
- Calculations for model serving, training, storage, application layer, and operational support costs show the breakdown of expenses for a hypothetical AI use case.
- Understanding AI costs helps in developing optimization strategies for infrastructure choices, resource utilization, and monitoring practices to maintain performance while controlling expenses.
- By leveraging Google Cloud's tools and resources, organizations can confidently pursue their AI journey in a financially responsible manner.
- To start understanding AI costs, one can explore the Google Cloud Pricing Calculator and the Vertex AI Pricing Page, attend sessions on AI investment at Google Cloud Next, or seek expert guidance from Google Cloud Consulting or PSO.
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