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The Open Source AI Stack

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As the quality of open source models have bridged the gap with closed source models, a lot of developers and organizations are looking to move to open source models for more ownership, control and economics. This post is a deep dive into the open model AI stack that developers need to consider as they move from closed to open source.

Using open models for agentic software development does not require learning how to train models, buying a rack full of GPUs, or becoming an expert in machine learning. From the perspective of an application developer, the stack is surprisingly familiar to using closed-source models. You have a model that answers prompts and a harness that manages the interaction between you and that model.

If you already know how to use Claude Code, you’re much closer to using open models than you probably think.

The stack can be broken down into these separate parts:

  • Model: The component that interprets your request and decides what to do.
  • Inference: The infrastructure & inference provider where the model actually runs.
  • Gateways and routers: The layer that decides which model or provider handles each request, balancing cost, speed, and capability.
  • Harness: The application that manages the conversation, gives the model access to tools, and connects it to your codebase.
  • Tools (Skills and MCP): Knowledge that tells the model how to do specific tasks, including giving models and the harness access to relevant context

    • These layers are all independent from one another, which opens the door for technical decisions at each layer that better suit your development workflow. In turn, this allows you to experiment with new models as soon as they are released.

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