Model Fallback and Retry - Interactive Visualization

Production LLM systems must handle provider outages, rate limits, and timeouts gracefully. A model fallback chain routes requests through a prioritized list of models - GPT-4o first, then Claude Sonnet, then a self-hosted Llama - with exponential backoff between retries. This pattern ensures high availability without sacrificing quality when the primary provider is unavailable.

• Fallback chain: configure primary, secondary, and tertiary model providers in order
• Failure injection: simulate rate limits, timeouts, and API errors at each provider
• Exponential backoff: watch retry delays grow - 1s, 2s, 4s - before cascading to the next model
• Latency impact: see how fallback adds end-to-end latency and when it is worth the tradeoff

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.