The LLM landscape changes monthly. New models drop, prices fall, capabilities expand. This playbook gives you a framework for making decisions that won't be obsolete by next quarter.
The decision framework
Every LLM decision comes down to four variables: capability (can it do what you need?), cost (per token, at your expected volume), latency (how fast does it respond?), and reliability (uptime, consistency, support).
When to use frontier models
GPT-4, Claude Opus, Gemini Ultra. These are the most capable models available. Use them when you need complex reasoning, nuanced language understanding, or when quality matters more than cost. Most prototypes should start here to establish a quality ceiling.
- Complex multi-step reasoning
- Nuanced content generation
- Tasks where errors are costly
- Prototyping and establishing baselines
When to use smaller models
For many production use cases, smaller models (GPT-4o-mini, Claude Haiku, open-source alternatives) are not just cheaper, they're better. Faster response times, lower costs at scale, and often 'good enough' quality for straightforward tasks.
The best model is the cheapest one that reliably does what you need. Nothing more.
The hybrid approach
Most production systems we build use multiple models. Route simple queries to fast, cheap models. Escalate complex ones to frontier models. Use specialized models for specific tasks like embeddings or classification. This isn't premature optimization; it's how you build sustainable AI products.
Vendor lock-in considerations
Build abstractions. Today's best model might be tomorrow's legacy choice. We architect systems so you can swap models without rewriting your application. The extra work upfront pays dividends when you need to migrate.