Developer cuts LLM inference costs 45% using self-hosted vLLM on cloud GPUs

A developer building autonomous AI agents and real-time LLM-driven applications found commercial API costs unsustainable due to high volumes of tool calls and token usage. To address this, they deployed a self-hosted inference cluster on cloud GPU platforms RunPod and Vast.ai using the open-source vLLM framework. The setup leveraged PagedAttention, continuous batching, speculative decoding, and prefix prompt caching to optimize performance and memory usage. Benchmarks across 1,000 synthetic agentic prompts showed average Time-To-First-Token dropped to 172ms, compared to roughly 420ms for commercial APIs. The configuration also reduced inference costs by approximately 45% to 74%, bringing estimated spend per million requests down from $1,850 to around $480.