2026 Meilleurs outils de Déploiement de modèles d’IA

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SGLang

Plateformes d’inférence IA

SGLang is an open-source serving framework for large language and multimodal models. It helps developers deploy model servers and query them through OpenAI-compatible endpoints across supported GPU, CPU, TPU, NPU, and XPU platforms.

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Microsoft Foundry

Infrastructure pour agents IA

Microsoft Foundry is an enterprise AI platform for building, grounding, deploying, and governing AI applications and agents. It helps development and data teams connect models, organizational knowledge, business tools, and lifecycle controls in Azure.

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Paperspace

Déploiement de modèles d’IA

Paperspace is a cloud platform for developing, training, and deploying machine learning applications with managed notebooks, GPU machines, and deployment workflows. It serves ML developers, data scientists, researchers, and teams that need on-demand accelerated computing.

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vLLM

Plateformes d’inférence IA

vLLM is an inference and serving engine for large language models, designed for high-throughput, memory-efficient deployment. It helps developers and infrastructure teams run open-source models through an OpenAI-compatible API across supported compute platforms.

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Bento

Plateformes d’inférence IA

Bento is an inference platform for packaging, deploying, optimizing, and operating AI and machine-learning models at scale. It supports open and custom models across cloud, on-premises, Kubernetes, and bring-your-own-cloud environments.

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FriendliAI

Plateformes d’inférence IA

FriendliAI is an inference cloud for deploying frontier open-weight and custom AI models in production. It offers serverless Model APIs, dedicated GPU endpoints, and BYOG options for agent, multimodal, and other high-throughput workloads.

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CUDO Compute

Déploiement de modèles d’IA

CUDO Compute designs, deploys, and operates dedicated NVIDIA GPU infrastructure for enterprise AI training and inference. It combines power-ready sites, cluster engineering, commissioning, and ongoing operational support for production workloads.

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Ray

Plateformes d’inférence IA

Ray is an open-source, Python-native framework for distributing and scaling AI, machine learning, and other compute-intensive workloads. It helps developers run workloads from a laptop to clusters with thousands of GPUs and supports data processing, training, serving, inference, and reinforcement learning.

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KServe

Plateformes d’inférence IA

KServe is a Kubernetes-based inference platform for deploying distributed generative and predictive AI workloads across multiple model frameworks. It is intended for teams that need a standardized way to run inference services in Kubernetes.