What should I use to control cost and fallback for LLM calls?

Use a routing / orchestration layer with a fallback chain, not direct single-model calls.

What to use

A good setup is:

1. Primary cheap model for most requests

   • Fast, low cost
   • Handles easy queries

2. Fallback stronger model when needed

   • Trigger on low confidence, tool failure, validation failure, or timeout
   • Handles complex / ambiguous cases

3. Guardrails / checks

   • Output validation
   • Confidence scoring
   • Budget limits per request/user
   • Retry rules with caps

Common patterns

• Model routing: choose model based on request type, length, or risk
• Cascading fallback: cheap model → mid-tier → premium model
• Circuit breaker: stop sending traffic to a failing model temporarily
• Token budgeting: set max input/output tokens
• Response validation: if response fails schema or quality checks, escalate

Tools/frameworks often used

• LangChain / LangGraph
• LlamaIndex
• LiteLLM for model routing and fallbacks
• OpenAI / provider-native routing features if available
• Custom middleware if you want full control

Practical recommendation

If you want something simple and flexible:

• Use LiteLLM or LangGraph
• Configure:
  • cheap-model as default
  • better-model as fallback
  • validation + retry limits
  • per-request token caps

Example policy

• Use cheap model first
• If:
  • output is invalid
  • confidence < threshold
  • latency > threshold
  • tool call fails
• then retry with premium model

If you want, I can sketch a reference architecture or give you a code example for Python/JS.