AI API Integration Guide: Best Practices for Developers

Integrating AI APIs into production applications requires careful planning, robust error handling, and performance optimization. This comprehensive guide covers everything you need to know.

Authentication and Security

• Store API keys securely using environment variables or key management services
• Implement proper access controls and API key rotation
• Use HTTPS for all API communications
• Monitor for unusual usage patterns and potential security breaches
• Implement rate limiting on your application side

Error Handling Strategies

Common Error Types

• Rate limiting (429 errors) - too many requests
• Token limit exceeded (400 errors) - input too long
• Authentication failures (401 errors) - invalid API key
• Service unavailable (503 errors) - provider downtime
• Network timeouts and connectivity issues

Retry Logic Implementation

Implement exponential backoff for rate limits and circuit breaker patterns for service failures:

async function callAIWithRetry(prompt, maxRetries = 3) {
  for (let attempt = 1; attempt <= maxRetries; attempt++) {
    try {
      const response = await openai.chat.completions.create({
        model: "gpt-4o",
        messages: [{ role: "user", content: prompt }]
      });
      return response;
    } catch (error) {
      if (error.status === 429 && attempt < maxRetries) {
        const delay = Math.pow(2, attempt) * 1000; // Exponential backoff
        await new Promise(resolve => setTimeout(resolve, delay));
        continue;
      }
      throw error;
    }
  }
}

Performance Optimization

• Use connection pooling and keep-alive headers
• Implement async/await patterns for concurrent requests
• Cache responses for repeated queries
• Use streaming for long responses when available
• Batch requests when the API supports it