Comparison of Self-Hosted Inference Orchestrators for OpenAI-Compatible Endpoints

Overview of Self-Hosted Inference Orchestrators

This analysis provides a survey of self-hosted inference orchestrators available as of September 2026. The comparison focuses on solutions that enable users with GPU machines to create OpenAI-compatible endpoints. It details the functionalities of each orchestrator across multiple machines and offers guidance on selection for different use cases.

Single-Machine Solutions: Ollama and Open WebUI

Ollama and Open WebUI are identified as the default choice for single-machine setups. Ollama provides a binary for model pulling and a Modelfile format, while Open WebUI adds a chat interface. These tools are suitable for individual laptops or desktops but lack clustering capabilities beyond basic round-robin routing of multiple Ollama URLs.

Core Engines: llama.cpp and vLLM

llama.cpp and vLLM serve as foundational engines that other orchestrators often wrap. They offer multi-machine capabilities, with llama.cpp supporting layer and row splitting, and vLLM utilizing Ray for tensor and pipeline parallelism. These engines primarily focus on model execution across machines and do not manage models, users, or placement beyond a single model per process.

Routing vs. Runtime: LiteLLM

LiteLLM functions as a router, connecting to numerous providers and user-defined endpoints, managing keys, budgets, and spend tracking. It does not run models itself but sits in front of other solutions, making it suitable for environments where multiple teams share inference resources. LiteLLM is not a self-hosting solution for model execution.

Comprehensive Orchestration: LocalAI

LocalAI is presented as a comprehensive orchestrator, offering an OpenAI-compatible API across various modalities including text, image, video, and audio. It aims to provide a broad set of features for managing and serving models.