Confused about pricing differences between Vertex AI and Google AI Studio - especially deployment

[PaulNewson]

Hey fellow AI enthusiasts,

I've been diving into the world of Google's AI offerings, and I'm a bit puzzled about the pricing differences between Vertex AI and Google AI Studio, particularly when it comes to deployment costs. Here's what I've gathered so far:

1. Google AI Studio seems cheaper for usage:

   • Input: $0.075 per million tokens

   • Output: $0.30 per million tokens

2. Vertex AI is more expensive for usage:

   • Input: $0.15 per million tokens

   • Output: $0.60 per million tokens

But here's where I'm confused:

• Vertex AI has additional deployment costs, starting at $0.75 per node hour for endpoints.

• Google AI Studio doesn't seem to have these deployment costs.

Questions:

1. Am I missing something about Google AI Studio's deployment process?

2. For those who've used both, how do the total costs compare in real-world usage?

3. Are there hidden benefits to Vertex AI that might justify the higher costs?

4. Any tips for minimizing deployment costs on Vertex AI?

I'd really appreciate insights from those who have experience with both platforms. Thanks in advance!

[ibaui]

Hi @PaulNewson,

Welcome to Google Cloud Community!

Here are some potential solutions that might address your questions:

• Am I missing something about Google AI Studio's deployment process?

  For more information on Google AI Studio's deployment process, you can read through this documentation.

• For those who've used both, how do the total costs compare in real-world usage?

  When you're using Vertex AI for real-world applications, the overall cost is affected by how complex your AI model is, how much it's used (traffic volume), and the type of computing resources you choose to run it on. Although the cost per token might seem higher than other options, Vertex AI provides powerful features and a lot of control over your setup. This lets you fine-tune things to potentially save money in the long run, in ways that might not be possible with simpler platforms.

• Are there hidden benefits to Vertex AI that might justify the higher costs?

  Here are some key benefits that are often worth the extra investment:

  Scalability & Reliability: Vertex AI is built for production workloads. It automatically scales to handle increased traffic, ensuring your application remains responsive.

  Monitoring & Logging: Vertex AI provides robust monitoring tools to track performance, identify issues, and optimize your models.

  Security & Compliance: Vertex AI offers enterprise-grade security features and complies with various industry regulations.

  Customization & Fine-tuning: Vertex AI allows you to fine-tune pre-trained models with your own data, achieving better accuracy and performance for your specific use case.

• Any tips for minimizing deployment costs on Vertex AI?

  Here are some strategies for minimizing deployment costs on Vertex AI:

  Right-size your nodes: Start with smaller node types and scale up only when necessary. Monitoring will help determine the appropriate size.

  Auto-scaling: Enable auto-scaling to automatically adjust the number of nodes based on traffic.

  Consider Batch Prediction: If real-time predictions are not required, use batch prediction to process large datasets offline, which is often more cost-effective.

  Use caching: Cache frequent responses to reduce the number of API calls.

  Monitor usage regularly: Track your spending and identify areas for optimization.

  Consider Committed Use Discounts (CUDs): If you plan to use Vertex AI for the long term, CUDs can provide significant cost savings.

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