Choosing the Right LLM Strategy for Your Product

The large language model landscape is evolving at breakneck speed. Every week brings a new model, a new benchmark, and a new set of opinions about the "right" way to build with LLMs. For engineering teams trying to ship products, this noise makes decision-making harder, not easier.

The Three Paths

When integrating LLMs into a product, you generally have three options:

1. API-based (OpenAI, Anthropic, etc.): You send prompts to a hosted model and get responses back. Lowest barrier to entry, highest ongoing cost at scale, and you're dependent on a third party for uptime, latency, and pricing.

2. Fine-tuned open-source: You take a base model (Llama, Mistral, etc.) and fine-tune it on your domain-specific data. Higher upfront investment, lower per-query cost at scale, and full control over the model.

3. RAG (Retrieval-Augmented Generation): You use a base model (API or open-source) combined with a vector database of your domain knowledge. The model retrieves relevant context before generating responses. Good balance of accuracy and flexibility.

Decision Framework

The right choice depends on four factors: volume (how many queries per day?), specificity (how domain-specific are your needs?), latency requirements (real-time vs. batch?), and data sensitivity (can data leave your infrastructure?).

For most startups building their first AI feature, we recommend starting with an API-based approach using RAG. It gives you the fastest time-to-market and lets you validate the product hypothesis before investing in infrastructure.

When to Fine-Tune

Fine-tuning makes sense when: you have a high volume of similar queries (>10K/day), you need consistent output formatting, your domain vocabulary is highly specialized, or you need to reduce per-query costs at scale.

The key mistake we see is teams fine-tuning too early, before they understand their users' actual query patterns.

Production Considerations

Regardless of which path you choose, you need: prompt versioning (treat prompts like code), output validation (LLMs will surprise you), fallback mechanisms (what happens when the API is down?), cost monitoring (LLM costs can spike unexpectedly), and user feedback loops (the best training data comes from your users).

Our Recommendation

Start simple, measure everything, and increase complexity only when the data justifies it. The best LLM strategy is the one that ships.