Enterprise LLM Integration: Patterns That Scale

Integrating LLMs into enterprise systems is fundamentally different from building a chatbot. You need reliability, cost control, and graceful degradation. After deploying LLM integrations for Fortune 500 companies, here are the patterns that actually work in production.

1. The Gateway Pattern

Never call LLM APIs directly from your application code. Instead, route all requests through a centralized gateway that handles:

• Rate limiting: Prevent runaway costs and API throttling
• Request queuing: Smooth out traffic spikes
• Response caching: Avoid redundant API calls
• Model routing: Send requests to the most appropriate model
• Cost tracking: Monitor spend by team/feature/user

class LLMGateway {
  async complete(request: LLMRequest): Promise {
    // Check cache first
    const cached = await this.cache.get(request);
    if (cached) return cached;

    // Apply rate limiting
    await this.rateLimiter.acquire(request.priority);

    // Route to appropriate model
    const model = this.router.selectModel(request);

    // Execute with retry logic
    const response = await this.executeWithRetry(model, request);

    // Cache and track
    await this.cache.set(request, response);
    await this.metrics.track(request, response);

    return response;
  }
}

2. Semantic Caching

Traditional caching uses exact key matching. For LLMs, you want semantic caching—returning cached results for queries that are similar in meaning, not just identical.

We use embedding-based similarity to achieve 40-60% cache hit rates on typical enterprise workloads, dramatically reducing costs and latency.

3. Multi-Model Fallback

Don't depend on a single provider. Implement fallback chains:

1. Try Claude (best quality for complex reasoning)
2. Fall back to GPT-4 if Claude is unavailable
3. Fall back to GPT-3.5 for non-critical paths
4. Return graceful degradation message if all fail

4. Cost Optimization Strategies

Prompt Compression

Long prompts are expensive. Use techniques like context summarization and selective inclusion to reduce token counts by 30-50% without quality loss.

Model Tiering

Not every request needs the most powerful model. Route simple queries to faster, cheaper models and reserve premium models for complex tasks.

Batch Processing

When latency isn't critical, batch multiple requests together. This reduces overhead and often qualifies for volume discounts.

5. Monitoring & Observability

LLM systems need specialized monitoring:

• Latency percentiles: Track p50, p95, p99 response times
• Token usage: Monitor input/output tokens per request
• Error rates: By model, endpoint, and error type
• Quality metrics: User feedback, automated evaluation scores
• Cost attribution: Spend by feature, team, and customer