Here's a statistic that should make AI startup founders nervous: serving costs kill more AI…

companies than bad models.

Training an LLM is a one-time cost. Serving it to thousands of users 24/7 is an ongoing hemorrhage if you're not careful.

I've seen startups spending $100/day on inference that could cost $5 with proper optimization. The difference is entirely infrastructure engineering.

The serving stack has gotten much better. vLLM with PagedAttention is the fastest open source option. Hugging Face TGI is solid. NVIDIA's Triton for maximum performance. Ollama for local deployment. LiteLLM as a proxy for multiple providers.

The optimization techniques that actually save money: continuous batching (serve multiple requests simultaneously), KV-cache management (don't recompute what you've already computed), quantization (INT4/INT8 for inference), speculative decoding (predict multiple tokens at once), and prefix caching (share computation across similar prompts).

Each of these independently can cut costs 30-60%. Combined, you're looking at 10-20x cost reduction.

The irony: most "AI engineer" job postings focus on model building. But the engineers who understand serving infrastructure are the ones saving companies from bankruptcy.