Question: Do you know of a platform that enables self-hosted LLM pipeline development, ensuring full control over privacy and security?

If you're looking for a platform that lets you develop your own LLM pipeline in your own environment, with full control over privacy and security, Superpipe is a great choice. This open-source platform lets you build, test and run LLM pipelines on your own infrastructure, cutting costs and getting better results. With Superpipe Studio, you can manage datasets, run experiments and monitor pipelines with observability tools, all while having full control over privacy and security.

Another good contender is Zerve. This platform lets you run and manage GenAI and LLMs in your own stack, giving you more control and faster deployment. Zerve marries open models with serverless GPUs and your own data, and it can be self-hosted on AWS, Azure or GCP instances, so you have full control over your data and infrastructure.

For a low-code option, check out Flowise. This open-source tool lets developers build custom LLM orchestration flows and AI agents through a graphical interface. With more than 100 integrations and the ability to run in air-gapped environments, Flowise can be installed and self-hosted on AWS, Azure and GCP, making it easier to build and iterate on your AI solutions.

Last, Lamini is an enterprise-grade platform for building, managing and deploying LLMs on your own data. It offers high accuracy with memory tuning, deployment on different environments and high-throughput inference. Lamini can be installed on-premise or on the cloud, and it runs on AMD GPUs, so it's a good option for managing the model lifecycle while keeping your data private and secure.