Developer Based Solutions

We give you a secure, private location to store large AI datasets — including text files, CSVs, images, and more. You’ll get access via SFTP or a Nextcloud web interface, with your data encrypted at rest using LUKS. Each client is set up on an isolated virtual machine with a custom storage quota, so your files aren’t sitting on shared cloud drives or managed by Big Tech. Ideal for training data, shared research sets, or internal labeling workflows.

​Skip environment setup and run your AI code on a clean remote machine. We provide dedicated Ubuntu-based virtual environments with Python, JupyterLab, and VS Code Server pre-installed. You can SSH in or work directly in your browser. These machines are great for prototyping models, running training jobs, or using tools like PyTorch without worrying about CUDA installs or local config issues. CPU and RAM allocations are customizable per machine.

You’ve built a model — now you need a place to run it and serve predictions. We host your Python-based inference code (FastAPI, Flask, etc.) in an isolated container or VM, expose a public endpoint, and give you access via API key. You don’t need to worry about uptime, server maintenance, or cloud billing complexity. Just deploy your code and start serving responses from your own private endpoint.

We’ll host a dedicated vector database instance — like Qdrant, Weaviate, or Milvus — for storing and querying embedding vectors. This is essential for building semantic search, chat-with-your-docs apps, and RAG pipelines. You get a secure endpoint, persistent storage, and optional web UI access, without having to manage Docker or cloud databases yourself. We handle the setup, backups, and server security so you can focus on using your vectors.