How .NET Developers Can Cut LLM Costs by Managing Context Length

For .NET teams building chatbots, RAG pipelines, or multi-agent systems, LLM costs scale quadratically with token count, meaning doubling the context can roughly quadruple the bill and increase latency. A real-world fintech support bot that retained 8,000 tokens of conversation history saw its Azure bill rise to $4,800 per month and response times jump from 850ms to 2.1 seconds, breaching its SLA. The root cause was the quadratic nature of self-attention and KV cache growth exhausting per-deployment token-per-minute quotas on Azure OpenAI. Developers are advised to measure baseline token usage, identify which parts of the prompt matter most, and apply token-aware trimming that respects token boundaries rather than cutting text arbitrarily. Strategies such as reusing the KV cache, setting strict token budgets, and monitoring usage can help teams keep both costs and latency predictable in production.