LLM Orchestration Patterns: Choosing the Right Model for the Job

The Hybrid Model Strategy

One common mistake in AI product development is using a “one-size-fits-all” model. Sending every simple text classification task to GPT-4 is like using a sledgehammer to drive a nail—it’s expensive and slow.

1. Strategic Model Tiering

At MysticStack, we leverage a tiered approach:

• Reasoning Tier (Pro): Use GPT-4o or Claude 3.5 Sonnet for complex logic, strategic Planning, and nuanced creative writing.
• Utility Tier (Local/Fast): Use Ollama-hosted Llama 3 or Mistral for data extraction, simple summarization, and formatting.

2. The Router Pattern

We implement “LLM Routers” that analyze an incoming request’s complexity and route it to the most efficient model. This optimizes for both Cost and Latency without sacrificing quality where it counts.

3. Local-First Development

By using tools like Ollama, our engineers can iterate on agent prompts locally without incurring API costs. This “Local-First” development cycle allows for much faster experimentation and ensures that the core logic is model-agnostic.

4. Governance & fallback

Production AI demands reliability. We implement fallback patterns where, if a local model fails to produce a valid JSON output, the system automatically escalates the task to a Pro-tier model to ensure a seamless user experience.

“Orchestration is the art of making the right tool show up at the right time.”