In the era of artificial intelligence, Generative AI has emerged as a powerful technology, revolutionizing content creation and app development. But what does it take to develop a Generative AI application, and how much does it cost? How can you leverage existing models to keep the costs down? In this article, we’ll dive into the world of Generative AI, exploring its capabilities, training methods, and the Generative AI cost involved in building your own app.
What is Generative AI?
Generative AI, short for Generative Artificial Intelligence, is a class of AI systems designed to autonomously create new content. From text to images to music, Generative AI has the remarkable ability to generate content that wasn’t explicitly programmed.
Leveraging deep learning techniques, these models learn from vast datasets to produce content that resembles the input data, all while operating autonomously and adapting to different inputs.
Training Gen AI Models
Training a Generative AI model involves exposing it to large datasets to learn patterns and styles. This training phase is essential for the model to generate content accurately and effectively. However, training models can be time-consuming and resource-intensive, requiring significant computational power and expertise.
Building a Gen-AI Model – How much does it cost?
The cost of building a Generative AI model depends on various factors, including the scope and complexity of the application, the type of content generated, the choice of algorithms and models, and the expertise of the development team. Basic applications with limited features may cost less, while more advanced applications with sophisticated functionalities can incur higher expenses.
A basic Generative AI app may cost between $40,000 to $100,000, while a more feature-rich application can range from $100,000 to $400,000 or more.
Factors influencing the cost
Scope and complexity
The more intricate the application, the higher the development effort and cost.
Type of content generated
Multimedia applications may require advanced algorithms, contributing to higher costs.
Algorithm and model selection
Advanced models may involve higher development expenses.
User interface and user experience
Investing in a well-designed UI/UX may increase development costs.
Integration with external systems
Seamless integration with external platforms can enhance capabilities but requires additional development effort.
Testing and Quality Assurance
Rigorous testing processes contribute to development costs.
Development team expertise
Highly skilled teams may command higher rates but deliver high-quality results.
Geographic location of the development team
Rates vary based on the location of the development team.
Using Pre-built Gen AI Models
Instead of building a Generative AI model from scratch, businesses can leverage pre-built models, such as those offered on Google Cloud. These models provide a cost-effective solution, allowing developers to uptrain existing models to suit their specific needs.
Benefits of Using Pre-built Models
- Cost-effectiveness: Uptraining existing models is often cheaper than building from scratch.
- Time-saving: Leveraging pre-built models can accelerate development timelines.
- Quality Assurance: Established models have undergone rigorous testing and validation.
Leveraging Google’s Generative AI Models
When it comes to developing Generative AI applications, Google offers a suite of pre-built models through Vertex AI, providing cost-effective solutions for businesses looking to harness the power of AI without starting from scratch. Let’s explore two key examples and their associated price points:
Multimodal models
Google’s multimodal models on Vertex AI offer versatility in content generation, allowing input of either text or media like images and videos. Pricing is structured based on input and output parameters:
Gemini Pro
Image Input: $0.0025 per image
Video Input: $0.002 per second
Text Input: $0.000125 per 1k characters
Output: $0.000375 per 1k characters
Text generation models
For text-based applications, Google’s Generative AI on Vertex AI charges based on the number of characters in input prompts and output responses. Here’s a breakdown of pricing:
PaLM 2 for Text (Text Bison)
Input: $0.00025 per 1k characters
Output: $0.0005 per 1k characters
Example Cost Calculation
Suppose a user sends five requests to the PaLM Text Bison model, each with a 200-character input and 400-character output:
Input cost
200 input characters x 5 prompts = 1,000 total input characters
$0.00025 input cost per 1k characters x (1,000 total input characters / 1,000) = $0.00025
Output cost
400 output characters x 5 prompts = 2,000 total output characters
$0.0005 output cost per 1k characters x (2,000 total output characters / 1,000) = $0.001
Total cost
$0.00025 input cost + $0.001 output cost = $0.00125
For more about the pricing of generative AI models available in Google Cloud’s Vertex AI Model Garden, see the detailed price list.
By utilizing these pre-built models, businesses can significantly reduce development costs and accelerate the deployment of Generative AI applications. Whether generating images, videos, or text, Google’s Vertex AI offers accessible and scalable solutions for a wide range of AI-powered applications.
Get more for less out of generative AI
The cost of Generative AI app development varies depending on numerous factors. You can navigate the complexities of Generative AI app development and create a cutting-edge application that meets your objectives. Whether building from scratch or leveraging pre-built models, investing in quality development is key to unlocking the full potential of Generative AI in your application. If you want to ask FOTC cloud experts about the capabilities of Gen AI models as well as cost optimization, contact us.
