Docker AI Stack: Self-hosted AI stack with LLM, STT, TTS, and MCP in one Docker Compose file

Docker AI Stack isn't creating new AI models; it's solving the 'integration tax' of self-hosting. By bundling Ollama, LiteLLM, Whisper, Kokoro, and an MCP Gateway into a cohesive compose file, it transforms a fragmented collection of tools into a functional backend. The technical win here is the zero-config approach—specifically the automatic generation of API keys and the internal networking that allows services to communicate by container name without the user manually mapping environment variables for every endpoint. From a product perspective, the inclusion of an MCP (Model Context Protocol) Gateway is a sharp move. It moves the stack beyond a simple chatbot and into a tool-capable agent environment, allowing local models to interact with filesystems and GitHub. The availability of CUDA-specific compose files ensures that those with NVIDIA hardware aren't fighting with driver passthrough issues, which is usually the biggest friction point in local AI deployments. However, the 'zero-config' claim is slightly optimistic. Users still need to manually pull models via `docker exec` before the system is actually functional. Additionally, while the lightweight stacks are a great nod to accessibility, the performance of 3B models on 8GB of RAM remains a bottleneck for professional use. It's a high-utility wrapper that saves hours of DevOps toil, but it remains dependent on the underlying stability of the individual images it orchestrates. This is for the developer who wants the privacy of local AI and the extensibility of MCP without spending a weekend debugging YAML files. It's a pragmatic bridge between 'running a model' and 'running an AI infrastructure.'