How to Choose the Right LLM

How to Choose the Right LLM

Choosing an LLM isn’t about finding the “best” model, but the “right” one for your specific task.

1. Step One: Drill into Business Requirements

Before looking at models, analyze the problem through these three lenses:

• Data Characteristics: Understand the quality, quantity, and structure (structured vs. unstructured) of your data.
• Business Outcome Metrics: Move beyond technical metrics like loss or perplexity. Focus on commercial outcomes (e.g., “Are the right candidates being shortlisted?”).
• Non-functional Constraints: Evaluate your budget for training and inference, as well as your Time to Market (delivery in one month vs. six months).

2. Step Two: Establish a Baseline

Never start with a complex system without a baseline.

• Traditional Approach: Simple code with if statements or logistic regression.
• Modern Approach: Use the highest-performing model available (e.g., GPT-4o) to set a “high bar” for performance before attempting to optimize or migrate.

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3. The Big Decision: Closed Source vs. Open Source

This choice dictates your infrastructure design and long-term costs.

Strategy A: Start Closed Source (API-based)

• Prototyping: Start with the most “beefy” models during the PoC phase. Initial cost is trivial compared to development time.
• Production: Don’t rush to switch. Unless you have massive traffic, the reliability of a premium API often outweighs the marginal savings of switching too early.

Strategy B: When to Move to Open Source

Consider open-source models (e.g., Llama, Mistral, Qwen) in these four scenarios:

Scenario	Reason to Switch
Proprietary Data	Fine-tuning an OS model on unique data can outperform frontier models in specific domains.
Privacy & Security	Necessary when data is highly sensitive and requires 100% data sovereignty.
Inference Cost	At massive scale, self-hosting OS models allows for better long-term cost control.
Edge Computing	For offline or hardware-specific applications, use SLMs (Small Language Models) like Llama 3.2.