Why One Developer Gave Up Self-Hosting AI Models and Switched to APIs

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A developer spent about three months and roughly $500 on GPU and PC upgrades to run their own large language model locally, motivated by privacy and independence from vendors. They used Ollama with quantized models like Mistral 7B and Llama 2 13B for a semantic search and document summarization side project. Over time, hidden costs emerged, including higher electricity bills, the constant effort of evaluating and swapping in new models, and limits imposed by 12GB of VRAM. The most serious problem was that the locally run model produced plausible but incorrect summaries, while an API-hosted model performed markedly better on the same task. The author now suggests that many people building similar tools should reconsider self-hosting.

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