How AI Orchestration Solves Scaling Challenges for Enterprise .NET Applications

Enterprise .NET applications that connect directly to large language models without a structured orchestration layer often face unpredictable costs, high latency, and hallucinated outputs as user traffic grows. A U.S. retail platform experienced these issues firsthand when a GPT-4-powered price-alert feature hit 10,000 concurrent users, blew past its token budget, and began returning inaccurate prices. The team resolved the problems by introducing an orchestration layer using Redis caching, Cosmos DB for state persistence, Azure Service Bus for long-running workflows, and token usage policies, cutting cold-start latency from 1.2 seconds to 500 milliseconds. Best practices identified from production deployments include prompt caching with Redis, batched inference for bulk workloads, OpenTelemetry instrumentation for observability, and strict tenant isolation to prevent data leakage in multi-tenant systems. Experts warn against common pitfalls such as hard-coded API keys, treating AI services like standard REST APIs, and neglecting per-call monitoring of token usage and latency.