LLM Routing Strategy Planner

Different LLMs have different cost/performance trade-offs. GPT-4o costs $2.50/1M input tokens while Llama 3.1 8B on Groq costs $0.05/1M. For simple classification (yes/no, category labeling), a cheap model is often as good as an expensive one. Saving expensive models for complex reasoning or code generation can reduce your LLM bill by 80-95% without sacrificing quality on the tasks that matter.

Real-time means responses must appear within ~300ms (e.g. autocomplete, live filters). Interactive means users are waiting but can tolerate 1-3 seconds (e.g. chat messages, search results). Batch means results can arrive in seconds or minutes (e.g. nightly report generation, email drafting). Lower latency requirements allow more powerful, slower models and reduce infrastructure costs.

Estimates use current provider pricing and typical token counts per task type. Actual costs vary based on your real prompt/response lengths. Classification tasks typically use 200-500 input tokens; code generation tasks can use 1000-3000 tokens. Monitor your actual token usage for the first 2 weeks with real traffic, then adjust routing based on observed averages.

The most common pattern is a task-type-based router function: `function getLLMClient(taskType: string)` that returns different configured SDK clients. Use LiteLLM, Portkey, or a simple if/switch statement to route. For production, store the model mapping in config/environment variables so you can update it without code deployments.

Not always. If classification errors have high business impact (e.g. content moderation, medical triage, financial risk flagging), use a stronger model even if it costs more. For low-stakes classification (e.g. tagging blog posts, sorting support tickets), cheap models work well. Always measure accuracy on your specific data before committing to a cheaper model.