Resilient LLM API Integration Patterns

Building LLM-powered applications means accepting that API calls sometimes fail. Networks drop, rate limits trigger, providers go down, and services hang. The difference between a fragile prototype and a production system is not the absence of failures—it's handling them gracefully and recovering automatically. This series teaches you the battle-tested resilience patterns used by teams shipping at scale: exponential backoff to space out retries, circuit breakers to fail fast, idempotency to enable safe replay, provider failover to survive outages, and graceful degradation to keep serving users when conditions degrade.

Whether you're integrating OpenAI, Anthropic, or a custom LLM backend, these patterns are universal. You'll learn each technique in isolation, then combine them into a production-ready resilient client library that your team can rely on. By the end, you'll understand not just how to implement these patterns, but why each one matters and when to apply them.

Articles in this series

• What Is Resilient API Integration? Foundations Guide
• Exponential Backoff in API Calls: Complete Implementation
• Handling Rate Limits: Best Practices for LLM APIs
• Request Timeouts and Deadlines: How to Prevent Hangs
• Circuit Breaker Pattern: Fail Fast and Recover
• Provider Failover Strategies for Multimodel Resilience
• Idempotency and Replay: Building Fault-Tolerant Requests
• Graceful Degradation: Maintaining Service Under Load
• Building a Resilient LLM Client: Production-Ready Architecture
• Testing Resilient Integrations: Chaos and Faults