LLM Orchestration at Scale: Managing Model Routing, Fallbacks, and Token Budgets

When your application depends on external LLM APIs, you need orchestration that handles provider outages, cost optimization, and quality routing automatically.

1. Multi-Provider Router

class LLMOrchestrator {
  private providers = [
    { name: "anthropic", model: "claude-sonnet-4", costPer1M: 3, priority: 1 },
    { name: "openai", model: "gpt-4o", costPer1M: 2.5, priority: 2 },
    { name: "google", model: "gemini-2.5-flash", costPer1M: 0.15, priority: 3 }
  ];

  async route(request: LLMRequest): Promise<LLMResponse> {
    // Cost-aware routing
    if (request.complexity === "low") {
      return this.call(this.providers[2]); // Cheapest model
    }

    // Quality routing with automatic fallback
    for (const provider of this.providers) {
      try {
        return await this.call(provider, { timeout: 10000 });
      } catch (error) {
        console.warn(`${provider.name} failed, trying next provider...`);
        continue;
      }
    }
    throw new Error("All LLM providers failed");
  }
}

2. Token Budget Enforcement

class TokenBudgetManager {
  private dailyBudget: number;
  private spent: number = 0;

  async checkBudget(estimatedTokens: number): Promise<boolean> {
    if (this.spent + estimatedTokens > this.dailyBudget) {
      // Downgrade to cheaper model instead of refusing
      return false;
    }
    this.spent += estimatedTokens;
    return true;
  }
}

LLM orchestration is the invisible infrastructure that keeps AI applications reliable, cost-effective, and resilient against provider failures.