Generative AI Basics

Discriminative Models

Google Gen AI

🧠 How to Choose the Right Foundation Model for Your Business Use Case

✅ Overview

Choosing the appropriate foundation model—a large pre-trained machine learning model like GPT, PaLM, or BERT—is a strategic decision for businesses looking to implement AI. These models can be used for tasks like summarization, translation, image generation, question answering, code generation, and more.

However, not every model fits every scenario. You need to consider several technical and business factors to ensure the solution aligns with your goals, budget, and operational requirements.

📌 Key Factors to Consider

Let’s explore the major dimensions to evaluate when selecting a foundation model:

1. Modality

Definition: Modality refers to the type of input and output the model handles, such as text, image, video, audio, or a combination (multimodal).

Consider:

Use CaseSuitable Modality
Customer service chatbotText-only
Product image captioningMultimodal (image + text)
Video surveillance analysisMultimodal (video + text)
Speech-to-text transcriptionAudio + text

Pro Tip: Choose a model trained specifically for the modality relevant to your business need (e.g., CLIP for image-text or Whisper for audio).

2. Context Window

Definition: The maximum amount of input data (tokens) a model can process in one prompt.

Why It Matters:

  • Long context windows are critical for tasks like summarizing long documents, legal contracts, or codebases.
  • Short context windows might miss important earlier context.

Examples:

ModelContext Window
GPT-3.5~4,000 tokens
GPT-4-turboup to 128,000 tokens
Claudeup to 200,000 tokens

Choose if: You need deep comprehension of long documents, conversational memory, or long-form reasoning.

3. Security and Compliance

Definition: Ensuring your foundation model adheres to your company’s privacy, governance, and regulatory requirements.

Consider:

  • Does the model run in a VPC/private environment?
  • Is your data encrypted at rest and in transit?
  • Is the provider SOC 2, ISO 27001, HIPAA, GDPR compliant?
  • Can you prevent training on your data?

Recommended Vendors for Regulated Use:

  • Google Cloud (Vertex AI + PaLM 2/3)
  • Azure OpenAI (HIPAA, FedRAMP compliant)
  • AWS Bedrock (custom VPCs)

4. Availability and Reliability

Definition: Uptime, service-level agreements (SLAs), failover capabilities, and regional availability.

Look for:

  • 99.9% uptime SLAs
  • Multi-region deployments
  • Load balancing and failover
  • Backup inference endpoints

If downtime is unacceptable (e.g., real-time fintech apps), choose a provider with global redundancy and SLAs.

5. Cost

Definition: Total cost of ownership including inference cost per 1K tokens, fine-tuning, storage, API calls, and compute.

Factors Influencing Cost:

  • Model size (larger models cost more)
  • Usage frequency (e.g., batch vs real-time)
  • Prompt length and output size
  • Fine-tuning and hosting costs
ProviderPrice Range (per 1K tokens)
GPT-3.5~$0.002–$0.01
GPT-4-turbo~$0.01–$0.03
Claude~$0.005–$0.02
Gemini (Google)Competitive, varies by tier

Tip: Use smaller models like FLAN-T5 or DistilBERT if you’re cost-sensitive.

6. Performance (Accuracy & Latency)

Definition: How well the model performs for your task, in terms of accuracy, relevance, and speed.

Key Considerations:

  • Benchmark scores (MMLU, HELM, SuperGLUE)
  • Task-specific evaluations (e.g., BLEU for translation, ROUGE for summarization)
  • Latency requirements (real-time vs batch processing)

How to Evaluate:

  • Run A/B tests
  • Evaluate hallucination rates
  • Measure precision/recall on your own data

7. Fine-Tuning and Customization

Definition: Adapting a foundation model to your specific domain or tasks.

Options:

  • Prompt engineering only (zero/few-shot learning)
  • Parameter-efficient fine-tuning (e.g., LoRA, PEFT)
  • Full fine-tuning (retraining the entire model)

When to Fine-tune:

  • You have proprietary domain data
  • You need higher accuracy than zero-shot can provide
  • You want brand voice or tone customization

Platforms Supporting Fine-Tuning:

  • Google Vertex AI (PaLM + Gemini models)
  • AWS Bedrock (with Anthropic, Cohere)
  • OpenAI (custom GPTs, fine-tuning for GPT-3.5)
  • Hugging Face (open-source model fine-tuning)

💼 Real-World Scenarios

📚 Legal Document Summarization

  • Need: Long context window, accurate summarization, compliance
  • Choose: Claude or GPT-4-turbo
  • Tools: Google Cloud’s Vertex AI or Anthropic on Bedrock

🛍️ E-commerce Product Search Enhancement

  • Need: Multimodal (image + text), real-time performance
  • Choose: Gemini 1.5 or CLIP (open source)
  • Tools: Google Cloud, Hugging Face

🤖 Internal Knowledge Assistant

  • Need: Custom tone, retrieval-augmented generation (RAG)
  • Choose: OpenAI GPT-4-turbo with custom instructions or fine-tuning
  • Tools: Azure OpenAI, LangChain, Vector DB

🧠 Tips for Evaluation

  • Start with prompt-based testing before committing to fine-tuning.
  • Use eval frameworks like PromptBench, OpenPromptEval, or LangChain Evaluation.
  • Monitor latency, cost per interaction, and accuracy continuously post-deployment.

🏁 Conclusion

Choosing the right foundation model is not about the biggest or most expensive—it’s about fit. Match the model’s capabilities to your use case by carefully evaluating:

  • Input/output modality
  • Context and accuracy needs
  • Security and compliance
  • Budget
  • Performance expectations
  • Customization flexibility

By aligning these factors with business goals and infrastructure, you can ensure successful AI integration with measurable ROI.