Developer implements PII filter for cloud LLMs, abandoning full self-hosting

The author shares their experience in finding an optimal solution for working with large language models amid rising hardware costs and data privacy risks. After testing powerful GPUs like the RTX 6000 and H200, and analyzing the security implications of sending data to cloud providers, the developer concluded that full self-hosting is not always practical. Instead, they implemented an open-source PII filter in front of cloud-based models. This solution ensures the necessary level of privacy while maintaining access to the capabilities of cloud LLMs. As a result, the author shifted their strategy, keeping a local LLM only as an on-standby backup, which proved to be a more efficient approach for production tasks. The article highlights the importance of balancing data security with computational costs when integrating AI into professional workflows.