Helix provides a private, declarative GenAI stack for building and deploying AI applications, targeting businesses and individuals seeking data security and control. It simplifies complex AI workflows like RAG, API binding, and fine-tuning through a helix.yaml configuration, offering an ergonomic and scalable solution for integrating open-source AI models.

How It Works

Helix utilizes a declarative pipeline approach, allowing users to define AI application logic and model configurations in a helix.yaml file. This abstraction simplifies deployment and management of various AI models, including language and image models. The system is designed to optimize the trade-off between GPU memory and latency, enabling efficient execution of AI workloads.

Quick Start & Requirements

• Install: curl -sL -O https://get.helixml.tech/install.sh && chmod +x install.sh && sudo ./install.sh
• Prerequisites: Docker or Kubernetes. Supports attaching own GPU runners or using external OpenAI-compatible LLMs.
• Access: Dashboard available at http://localhost:8080 by default.
• Docs: Docs

Highlighted Details

• Private GenAI stack for data security and control.
• Declarative pipelines via helix.yaml.
• Supports RAG, API-calling, and fine-tuning.
• Optimizes GPU memory vs. latency trade-offs.

Maintenance & Community

• Community support via Discord.

Licensing & Compatibility

• License similar to Docker Desktop. Free for Personal, Educational, and Small Business Use (<$10M revenue, <250 employees).
• Commercial use outside these terms requires a paid license.
• Prohibits building competing products. Contributions are owned by HelixML, Inc.

Limitations & Caveats

The license restricts commercial use for larger companies and prohibits building competing services, potentially limiting adoption in certain enterprise scenarios. Contributions are owned by HelixML, Inc., which may impact community-driven development.