Recently, Google released Gemini — a powerful model now available on Vertex AI. Surprisingly, the company also released the pricing, and when compared with Microsoft Azure OpenAI Service advanced models, say GPT-4 (especially for training), it’s 2x more expensive than Vertex AI.
For instance, Vertex AI training costs range from $0.03 to $10 per node-hour, predictions vary from $0.00005 to $0.10 per request, and storage is $0.02 per GB/month. Microsoft Azure Open AI Service, which has two versions of GPT-4, namely GPT-4 8K and GPT-4 32K, is priced at $0.03 per 1,000 tokens for usage and $0.06 per 1,000 tokens for completion.
In Vision, particularly for Vertex AI, image storage cost is $0.02/GB/month, search queries cost $3 per 1,000 requests, and image/video indexing ranges from $0.043 to $0.292, depending on node hours.
For Vertex AI Conversation, standard functionality starts at $1,000/month, covering 83,333 queries at $0.012 each. In contrast, generative AI chat functionality requires a minimum monthly cost of $1,000 under the Dialogflow CX Enterprise Chat SKU.
On the other hand, the Microsoft Azure OpenAI Service’s GPT-4 32K, which is a larger and more powerful variant designed to handle complex tasks, the pricing is about $0.06 per 1,000 tokens for usage and $0.12 per 1,000 tokens for completion.
| It has to be noted that Vertex AI pricing is based on characters and Open AI’s pricing is based on tokens. Simply put, one token is equivalent to four characters. |
But, there is a way to reduce the cost, users can explore the free tier, which includes 20,000 tokens, to experiment with GPT-3.5 before committing to higher usage levels, whose performance level is similar to the present version of Google Gemini.
Google Vertex AI is 5x More Expensive than Microsoft Azure OpenAI Service
When compared, Gemini Pro adopts a pay-per-character model with equal charges for input and output, priced at $0.00025 per 1,000 characters. At the same time, GPT-3.5 operates on a pay-per-token basis at $0.002 per token. The minimum charge per request for Gemini Pro is $0.05, compared to GPT-3.5’s $0.01. In other words, Google Vertex AI is 5x times more expensive than Microsoft Azure OpenAI Service.
Gemini Pro API details, more expensive than gpt-3.5 (hint they use character pricing vs token) pic.twitter.com/6tFDSftdmv
— anton (@abacaj) December 13, 2023
Meanwhile, OpenAI has recently released GPT-4-Turbo, priced at $0.01 per 1,000 input tokens and $0.03 per 1,000 output tokens. It boasts significantly reduced costs, being 3x cheaper for input tokens and 2x cheaper for output tokens compared to its predecessor, GPT-4.
While Microsoft Azure Open AI Service is limited to OpenAI’s foundational models, Microsoft Azure AI as a platform also offers users a wide array of options and flexibility within the Microsoft Azure ecosystem.
All in all, Microsoft Azure OpenAI Service stands out for its diverse pricing models, including Reserved Instances and Savings Plans, which allow users to align costs with their usage patterns and commitments. On the other hand, Google’s Vertex AI, not only offers advanced capabilities and flexibility in building generative AI applications, but also gives choice for developers to choose from, including language models like Falcon and Llama2.
Notably, these models are gaining traction for their competitive performance, signalling a noteworthy shift in the dynamics of the AI field. Llama 2, in particular, stands out with its commendable performance and an attractive pricing strategy, offering a compelling option at $14,000.
Meanwhile, the Falcon 180-B is the most budget-friendly choice among these alternatives, priced at a notably economical $5119.
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