Decoding the Cost of Generative AI Implementation: Navigating Business Case, Development, and Operations

Funding generative AI projects requires substantial investment by organizations, The costs can vary widely depending on your objectives, implementation technology, and project scope. Each generative AI application has its own scope, risks, and potential impacts. It's crucial to understand the cost allocations, ROI and and make better choices.

Come 2025, the agentic era of AI, the AI agents will disrupt the App world with their unique strengths to simplify the AI adoption and achieve specific enterprise objectives to drive efficiency and customer experience. The agentic AI will disrupt customer service; employee empowerment; code creation; data analysis; cybersecurity; and innovation.

Given these dynamics, I want to throw some light into the key phases in a GenAI project and the cost imperatives for each,

1. Build your Business Case

• The purpose of your generative AI project will significantly impact its costs. Simple implementations where generative AI models can be readily adapted, like chatbots, content or code generation, summarization, and virtual assistants, require less resources, so the cost may not be high. CSPs are providing Cloud services for these use cases for building, integrating and deploying rapidly
• However, costs can rise with complex use cases that involve extensive data acquisition and processing. For Example: For Medical Diagnostics even a simple chatbot can become expensive if it handles heavy data flow, such as images and videos. Projects like creating advanced personalization engines for e-commerce or drug discovery for pharma or product design for a manufacturer etc., will increase the budget due to the need for significant computational power and cloud computing resources.

2. Model Choice: Build Vs Buy

• Build:

If your project requires a highly specialized solution and you have the resources, building your own model might be the way to go. Building a model from scratch is expensive, considering the need for data acquisition, skilled talent, and investing in infrastructure.

• Buy:

§? If you're looking for a quicker, more cost-effective solution that leverages cutting-edge AI, fine-tuning a general-purpose model could be the better option. Buying a model and finetuning is cost effective, helps in faster deployment, leverage state of the art market models. However, this route has its own drawbacks like option for less customizations, dependency on the Model maker, Generalization and fine-tuning challenges.

§? The FM you choose is the heart of your generative AI solution and determines your project's costs, time, and effort. Be it GANs, VAEs, Transformer model etc., the cost factors vary. Hence, organization needs to evaluate which model is suitable for their business case and data. ?For example, Transformer Models are used mainly in Text Generation, Language translation and code generation and they require resource intensive training and computational power. Diffusion models are computationally expensive requiring high GPU power for image and video generation.

3.? Build Phase

The cost involved in Build Phase include Data Collection and Preparation cost, Cloud Resources cost, Training cost, Fine Tuning cost, People cost like Data Scientists, ML Engineers, Cloud and DevOps professionals and testing team.

Training large generative AI models demands specialized hardware, like GPUs or TPUs. These resources can be rented from cloud providers (CSP) or purchased directly.

• ?Data Collection Preparation

§? Generative AI solutions are only as effective as the data they are trained on, and that’s where costs come into play.

§? Data quality and quantity are critical for the success of AI models, often driving up overall expenses. Effective data acquisition, cleaning, and processing are fundamental yet costly steps that ensure your generative AI delivers desired results. Understanding these costs can help in budgeting and optimizing resource allocation.

• Training, Fine-Tuning based on your case

§? Training: Ideal for highly specialized tasks with sufficient resources and expertise.

§? Fine-tuning involves copying the Foundation Model and incorporating your own data. This process modifies the weights of the base foundation model. You need to pump in your training data and store it externally for prompting and fine-tuning. Note that fine-tuning requires Provisioned Throughput, which comes with a different pricing model.

§? Prompt Engineering: Here there is no further training required to the model. So, there is no additional cost implication

§? Retrieval Augmented Generation

·??????? Here you use an external knowledge base like AWS S3 to train the model

·??????? Here you will have to setup a vector database to store the tokens, so additional cost involves

§? Instruction based Fine Tuning:

·??????? Here the FM is fine-tuned based with specific instructions and requirements, so it needs additional computational cost, but less intensive than Domain Adaptation Fine Tuning

§? Domain Adaptation Fine Tuning:

·??????? This is critical for business applications, and it demands high computational resources, so it is highly expensive as the model needs to adapt to domain requirements.

§? Continuous pre-training is about enhancing the model's broad knowledge base, while fine-tuning hones the model for a particular task. Continued pretraining is costly, requires a significant amount of data, and demands experienced ML engineers.

§? Data preparation costs are crucial, with data quality playing a pivotal role in fine-tuning and model evaluation.

• Transfer Learning:

§? Transfer learning is broader than fine-tuning. It adapts a pre-trained model to a new, related task, commonly used for image classification and NLP (e.g., BERT and GPT). Transfer learning requires fewer computational resources since only the new layers are trained.

o?? Model inference – The process of a foundation model generating an output (response) from a given input (prompt). Online Vs Batch and Edge has cost implications.

4.??????? Deployment and Integration

• Deploying and integrating Generative AI (GenAI) models involves multiple steps to ensure they function seamlessly within your existing systems.
• Convert the model into a deployable format (e.g., ONNX, TensorFlow Serving).
• Set up the necessary hardware (e.g., GPUs or TPUs) and software environments on-premises or in the cloud.
• Conduct comprehensive tests to ensure the model's functionality and performance.
• Implement measures to secure data and adhere to privacy regulations.
• Allocate sufficient computational resources to support the model's requirements.
• Utilize orchestration tools like Kubernetes to manage and coordinate AI services.
• Setup your model monitoring and CloudOps

5.??????? Maintenance and ongoing CloudAIOps

• Maintaining GenAI models and ensuring their ongoing operations involves several continuous and proactive activities to keep the models performing effectively and accurately
• Model Updates: Frequently update the model with new data to keep it relevant and accurate.
• Software Patches: Apply necessary updates to the software and libraries used by the model.
• Real-Time Monitoring: Use monitoring tools to observe the model's performance in real-time.
• Conduct A/B tests to compare different models or configurations for optimal performance.

Let us assume that you have selected AWS Bedrock a fully managed service from AWS for GenAI implementation. Below are the cost considerations:

1. With Amazon Bedrock, you will be charged for model inference and customization. You have a choice of two pricing plans for inference:

• On-Demand and Batch: This mode allows you to use FMs on a pay-as-you-go basis without having to make any time-based term commitments.
• Provisioned Throughput: This mode allows you to provision sufficient throughput to meet your application's performance requirements in exchange for a time-based term commitment.
• For Custom Model imports you will be charged for model inference, not for imports
• For proprietary models, you are charged the software price set by the model provider (per hour, billable in per second increments, or per request) and an infrastructure price based on the instance you select and the tokens processed
• For customization of a text-generation model, you are charged for the model training based on the total number of tokens processed by the mode (number of tokens in the training data corpus x the number of epochs)
• Inferences using customized models are charged under the Provisioned Throughput plan and requires you purchase Provisioned Throughput (you must purchase a 1-month or 6-month commitment term.)
• For Model Distillation you pay for what you use
• Additional cost drivers include node transaction execution, SQL data retrieval for using knowledge base, Guardrails configuration, Model inference and evaluation (Claude Instant Inference) , Data automation reference APIs

In conclusion, Generative AI offers immense potential, but understanding the full cost spectrum is key. Beyond initial costs, consider updates, retraining, and scaling to avoid unexpected expenses. AI costs vary, making it crucial to factor in scope and consult experts to maximize ROI.

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Thank you and Regards,

Srinivas Y