BentoML
Der einfachste Weg, KI-Apps und -Modelle bereitzustellen – erstellen Sie Inference-APIs, Job-Queues, LLM-Apps und Multi-Modell-Pipelines.
Überblick
Anforderungen
Empfohlener VPS
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 8
8 vCPU · 32256 MB · 400 GB
Affiliate-Hinweis
Docker Compose
# Generated by Run This Ai — docker-compose.yml
services:
bentoml:
image: bentoml/model-server:latest
restart: unless-stopped
ports:
- 8080:8080
volumes:
- ./data/bentoml:/data
Verwandte Tools
NextChat
Cross-platform ChatGPT web UI with multi-model support for Ollama, Claude, Gemini, and more
Lobe Chat
Extensible, open-source ChatGPT alternative with plugins, knowledge base, and multi-LLM support
Open WebUI
User-friendly WebUI for LLMs (Ollama, OpenAI API)
text-generation-webui
Run local LLMs with a powerful web interface — text, vision, tool-calling, and OpenAI-compatible API
Streamlit
Build and share data apps in pure Python — fast
Gradio
Build and share delightful machine learning apps in Python
Anleitungen & Artikel
BentoML Tutorial: Deploy Your First LLM Inference API with Docker
Step-by-step: build a Bento, containerize it, and serve an LLM API with BentoML and Docker. Includes resource requirements and monitoring setup.
BentoML Guide: How to Serve AI Models in Production
BentoML turns any Python model into a production-ready inference API. Learn how adaptive batching, job queues, and multi-model pipelines make AI serving simple.