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Phase 19 · System Design for AI Applications

Topics

Load Balancing AI Workloads

Part of the AI Engineer Roadmap.

Summary

Distributing inference requests across multiple model instances or providers to avoid bottlenecks and single points of failure in an AI service.

How to Learn This

  • 1Design a load-balancing strategy across multiple model instances or API keys.
  • 2Learn how to handle provider-level rate limits with multi-provider fallback.
  • 3Understand health checks and graceful degradation when a model endpoint is down.

Previous

Caching LLM Responses

Next

Designing a RAG System at Scale

More topics in System Design for AI Applications

Designing Scalable AI SystemsLatency & Cost OptimizationCaching LLM ResponsesDesigning a RAG System at ScaleDesigning a Recommendation SystemDesigning a Content Moderation SystemHandling High-Throughput InferenceChoosing Between Fine-Tuning and RAG
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