LLM API Integration Patterns for Production Applications

Integrating LLM APIs like OpenAI or Anthropic into production applications requires more than just making API calls. You need patterns that handle failures gracefully, control costs, and maintain good user experience even when things go wrong.

The Facade Pattern

Wrap your LLM provider behind an abstraction layer. This allows you to:

• Switch providers without changing application code
• Add consistent logging and monitoring
• Implement fallback strategies

interface LLMProvider {
  complete(prompt: string, options: CompletionOptions): Promise<string>;
}

class LLMFacade {
  constructor(private primary: LLMProvider, private fallback?: LLMProvider) {}

  async complete(prompt: string, options: CompletionOptions): Promise<string> {
    try {
      return await this.primary.complete(prompt, options);
    } catch (error) {
      if (this.fallback) {
        return await this.fallback.complete(prompt, options);
      }
      throw error;
    }
  }
}

Retry with Exponential Backoff

LLM APIs experience rate limits and transient failures. Implement retries with exponential backoff:

• Start with a short delay (e.g., 1 second)
• Double the delay on each retry
• Set a maximum number of retries
• Add jitter to prevent thundering herd

This pattern handles most transient failures without overwhelming the API.

Cost Control Strategies

LLM API costs can escalate quickly. Implement these controls:

1. Token budgets: Set per-request and per-user token limits
2. Prompt caching: Cache responses for identical prompts
3. Model tiering: Use smaller models for simple tasks
4. Usage monitoring: Alert on unusual spending patterns

Streaming for Better UX

For user-facing applications, streaming responses improves perceived performance:

• Users see output immediately rather than waiting
• Long responses feel faster
• You can implement cancel functionality

Most LLM APIs support streaming. The tradeoff is slightly more complex client code.

Error Handling Taxonomy

Different errors require different handling:

| Error Type | Strategy | |------------|----------| | Rate limit | Retry with backoff | | Invalid request | Fail fast, fix prompt | | Server error | Retry limited times | | Context length | Truncate or chunk input | | Content filter | Log and handle gracefully |

Timeout Management

LLM requests can take seconds to minutes. Set appropriate timeouts:

• Connection timeout: 5-10 seconds
• Response timeout: Depends on expected output length
• Total timeout: Cap at user tolerance threshold

Provide feedback to users during long-running requests.

Conclusion

Production LLM integration requires defensive programming. Build abstractions that let you swap providers, implement robust retry logic, control costs proactively, and handle errors gracefully. These patterns make the difference between a demo and a production system.