Modelship

Modelship is a self-hosted, OpenAI-compatible inference backend for reasoning LLMs, universal tool calling, and the Responses API.

Modelship provides a self-hosted inference backend that supports reasoning, tool calling, server-side conversation state, and Model Context Protocol servers. It runs on hardware you control, using inference backends such as vLLM, llama.cpp, Diffusers, sherpa-onnx, and whisper.cpp. The platform supports chat, text embeddings, speech-to-text, text-to-speech, and image generation through a single OpenAI-compatible endpoint. It features zero-downtime hot-reloads, isolated model deployments, client disconnect detection, and Prometheus metrics. It is designed for developers who want to manage their own AI agent infrastructure with modular hardware allocation.

Modelship at a glance

Best for: Software engineer
Runs on: macOS, Windows, Linux, Docker, Kubernetes, API
Pricing: Free
Released: October 2026
Category: AI coding tool
Website: github.com/modelship-ai/modelship

Key features

Multi-model and multi-GPU serving
CPU-only support
Multiple inference backends
Zero-downtime hot-reloads
Reasoning and tool calling
Server-side MCP tool execution
Built-in observability

Use it for

Serving reasoning LLMs with tool calling
Running multi-modal AI applications
Managing self-hosted AI inference infrastructure
Executing server-side MCP tool loops

Alternatives to Modelship

OpenShip — Builds run locally or in the cloud and stream to your servers over plain SSH.
OpenLLM — It simplifies infrastructure management and optimizes inference speed and cost.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
WebLLM — Runs high-performance language models directly inside web browsers.
Fireworks AI — Run optimized open models at lower cost and higher throughput than closed alternatives.
LiteLLM — One OpenAI-format API for 100+ LLM providers
llmware — Build private, local RAG pipelines with 300+ prepackaged models and integrated document parsing.
Mesh LLM — Run large local models across multiple machines through one OpenAI-compatible endpoint.
OptiLLM — Increases LLM reasoning accuracy by up to 10x without model training.
Xinference — Supports a wide range of model types and hardware, including multi-node setups.
AI SDK — Switch providers easily and stream real-time responses without custom parsing.
ZML — It enables Python-free inference while abstracting hardware complexities.

All Modelship alternatives