Modalities you can deploy
Deploy and scale everything from large language models to specialized audio and video processors.LLMs & SLMs
Serve models like OpenAI OSS, Llama 3 or Mistral for chatbots, agents, and Retrieval-Augmented Generation.
Image & Video Generation
Deploy text-to-image models like Stable Diffusion to generate visuals with a simple API call.
TTS and ASR models
Build powerful speech-to-text services with Whisper or create realistic text-to-speech applications.
Custom Models
Deploy your own custom trained models for any use case such as rerankers, embedders or voice activity detection.
A Complete Platform for AI Workloads
Tensorfuse provides a single platform for the entire model lifecycle. It lets you:- Serve models as auto-scaling web endpoints that handle traffic spikes and scale to zero.
- Run asynchronous jobs for batch inference, data processing, or large-scale model evaluations.
- Launch finetuning runs on your own private data to create powerful, specialized models.
- Spin up interactive GPU-powered development environments with your code pre-loaded for experimentation.
- Manage project secrets and mount persistent volumes for stateful applications.
- Automate your MLOps workflow using our GitHub Actions integration.
How does it work?
Tensorfuse runs entirely inside your own AWS account. It uses a secure cross-account IAM role to automatically provision and manage a dedicated Kubernetes (EKS) cluster within your VPC. Unlike hosted platforms, your proprietary data and models never leave your cloud perimeter. You get the simplicity of a serverless platform with the security and control of owning your infrastructure—without having to manage any of it yourself.Get Started
Go to the Getting Started
Install the CLI and deploy your first application in under 5 minutes.
Explore Examples on GitHub
Browse our repository of ready-to-deploy models for a wide variety of use cases.