Self-Hosting Open AI Models on AWS: Real Costs and Trade-offs Explained

Companies are increasingly exploring self-hosted open AI models such as Meta's Llama 4, DeepSeek, and Alibaba's Qwen as alternatives to costly commercial APIs, with some reporting savings of up to 70%. Open models use publicly downloadable weights that run on a company's own hardware, eliminating per-request fees paid to third-party providers. GPU memory is critical for practical deployment, as CPU-based inference is too slow for team use, generating only 2–5 tokens per second versus 30–80 on a GPU. The recommended software stack includes vLLM as the inference engine and Open WebUI for a browser interface, both open-source and compatible with existing OpenAI-based tools. Chinese open models have grown rapidly, now accounting for over 30% of enterprise traffic on OpenRouter, up from just 4.5% in early 2025.