How do you choose the right LLM for a business use case? What model selection framework do you follow and what tradeoffs do you consider?

Set the Ground

While selecting an LLM, the key is ensuring that the model aligns with business goals.
I use a 4-pillar evaluation framework:
Accuracy → Scalability → Cost → Security/Compliance

Models Considered

• GPT-4 / GPT-4o
  
  

• Claude 2
  
  

• LLaMA 3
  
  

• Mistral
  
  

• Gemini 1.5
  
  

After benchmarking, we selected GPT-4o due to:

Reasons for Choosing the Model

• Best performance in complex reasoning and contextual understanding.
  
  

• Excellent few-shot generalization → reduced fine-tuning needs.
  
  

• Lower hallucination rate compared to open-source models.
  
  

• Scalable pricing and predictable performance.
  
  

• Strong enterprise compliance and security support.
  
  

Why Other Models Were Not Selected

• Claude 2 – strong summarization; weaker reasoning for multi-step tasks.
  
  

• LLaMA 3 – great control but required heavy customization; expensive to maintain infra.
  
  

• Mistral – lightweight but lacked deep reasoning capabilities.
  
  

• Gemini – latency spikes and weaker ecosystem/tooling support.
  
  

Additional Criteria

• Latency thresholds
  
  

• Tool-use capabilities
  
  

• API reliability
  
  

• Documentation + support channels
  
  

• Integration with LangChain / LlamaIndex / RAG providers
  
  

Final Outcome

Selected GPT-4o due to best tradeoff between accuracy, cost-efficiency, latency, and compliance for enterprise workloads