Integrating LLMs into Production Systems: A Practical Guide

Large Language Models are transforming how we build software, but integrating them into production systems requires careful architectural thinking that goes beyond simple API calls.

The Architecture Challenge

LLMs introduce unique challenges to traditional software architectures:

• Non-deterministic outputs — The same input can produce different results.
• Latency variability — Response times can range from milliseconds to several seconds.
• Cost at scale — Token-based pricing can become significant at enterprise volumes.
• Rate limits — API providers enforce limits that require careful management.

Patterns That Work

The Gateway Pattern

I always place an abstraction layer between my application logic and the LLM provider. This gateway handles:

• Request queuing and rate limiting
• Response caching for identical or similar inputs
• Fallback logic between providers
• Cost tracking and budget enforcement

Structured Output Validation

Never trust LLM output directly. Always validate against a schema:

const response = await llm.generate(prompt)
const parsed = outputSchema.safeParse(response)

if (!parsed.success) {
  // Retry with refined prompt or fall back to default
  return handleValidationFailure(parsed.error)
}

Async Processing for Heavy Tasks

For tasks like document analysis or batch processing, move LLM calls to background jobs:

• Use a message queue (SQS, Redis) to buffer requests
• Process asynchronously with dedicated workers
• Notify the user when results are ready via webhooks or polling

Cost Optimization

Token costs add up fast. Here are strategies that consistently reduce costs by 40-60%:

1. Prompt optimization — Shorter, more focused prompts use fewer tokens.
2. Response caching — Cache results for repeated or similar queries.
3. Model selection — Use smaller models for simple tasks, reserve large models for complex ones.
4. Batching — Group multiple requests when possible.

Monitoring and Observability

Track these metrics for every LLM integration:

• Response latency (p50, p95, p99)
• Token usage per request and per user
• Error rates and retry counts
• Output quality metrics (if applicable)

Conclusion

Successfully integrating LLMs requires treating them as unreliable, expensive, external dependencies — and building the same resilience patterns you would for any critical third-party service.