🚀 Tensorfuse - Deploy and scale LLM pipelines on your own cloud

Tl;dr

Tensorfuse makes it easy to deploy and manage LLM pipelines on your own cloud. Simply connect your cloud to Tensorfuse, select your model, point to your data and click deploy. Tensorfuse will provision and manage the underlying infrastructure for you. Behind the scenes, we manage K8s + Ray clusters, enabling you to scale without LLMOps overhead.

About us

We are Agam and Samagra. We have experience deploying production machine learning systems at scale at Adobe and Qualcomm. You can see some of our work while using Adobe Scan. Additionally, Samagra authored the Java implementation of "AI: A Modern Approach," which is a widely used AI textbook in over 1,500 universities worldwide. Agam worked as a Computer Vision researcher at Qualcomm, where he published a paper and obtained a patent for image upscaling.

Problem

Companies in regulated spaces are constrained to build LLM apps on their cloud to maintain control over their data. However, managing and Scaling LLM infra is hard and it requires LLMOps expertise. Companies face the following issues:

❌ Deployment complexities increase development time and operational overhead

❌ Auto-scaling requires sophisticated solutions and there are not enough LLMOps experts in the market

✅ Solution

Tensorfuse provides a single API to manage your infra. Simply connect your cloud to Tensorfuse, select your model, point to your data and click deploy. Tensorfuse will provision and manage the underlying infrastructure for you.

One of our clients managed to deploy a production-ready retriever in just 6 days, a process that would have otherwise required months of experimentation.

❇️ Ask

1. If you are facing issues in deploying LLM apps on your infra, contact us at founders@tensorfuse.io
2. Follow us on LinkedIn and Twitter for more updates.