Question: I need a way to deploy large language models on-premise for reliability and compliance, is there a platform that supports this?

If you want a more powerful foundation to run large language models on-premise for reliability and compliance, Dify is a good choice. It's a full stack for building and running AI applications with tools for secure data pipelines, prompt design, and custom LLM agents. The platform can be deployed on-premise for data security and compliance, with pricing tiers for different business needs, including a free Sandbox tier.

Another good option is Lamini, which is geared for enterprise-scale use. It can run LLMs on-premise or in the cloud, and offers features like memory tuning for better accuracy, guaranteed JSON output, and high-throughput inference. Lamini lets you pick models, tune them and run inference, so it's good for managing the full model lifecycle.

If you want to control your data and not rely on third-party providers, Prem is an option. It offers a development environment that's easy to use, handles heavy lifting like prompt engineering and deployment, and lets you deploy models on-premise so sensitive data stays in your control. Prem also comes with a library of open-source Small Language Models you can fine-tune and customize for your own use cases, so it's a good option for enterprises.

Last, ClearGPT is worth a look for its security, performance and data governance. The platform offers the highest level of control and corporate IP protection by running on a private network and restricting access to sensitive data. It's got role-based access and data governance, so it's a good option for enterprises that want to use AI without worrying about vendor lock-in and data leakage.