Best model serving AI tools

Baseten is a production-grade inference platform for deploying, scaling, and managing AI models. It offers high-performance runtimes, seamless developer workflows, and flexible deployment options (cloud, self-hosted, hybrid). Ideal for engineering and ML teams building mission-critical AI applications.

PloyD is an enterprise AI operations platform designed to streamline the productionization of AI models and applications. It tackles common challenges like developer velocity bottlenecks, infrastructure complexity, team efficiency, and security compliance, enabling organizations to deploy, manage, and scale AI solutions with confidence and speed.

FriendliAI is a generative AI infrastructure platform designed to accelerate and optimize AI model inference. It offers high-performance, cost-effective solutions for deploying, serving, and scaling large language and multimodal models in production, with flexible options for dedicated, serverless, or on-premise environments.

Predibase is an end-to-end developer platform for efficiently fine-tuning and serving open-source Large Language Models (LLMs). It enables users to build custom AI models that outperform large proprietary models like GPT-4 on specific tasks, while significantly reducing costs and inference latency. The platform features advanced techniques like Reinforcement Fine-Tuning (RFT) and LoRAX for high-speed, multi-model serving.

An enterprise-grade platform for rapidly deploying, managing, and scaling Generative AI applications. It provides a unified infrastructure control plane to streamline LLM deployment, monitor performance, and optimize compute costs, accelerating GenAI adoption securely and efficiently.

UbiOps is a powerful MLOps platform for AI model serving, orchestration, and training. It enables data scientists and AI teams to seamlessly deploy, manage, and scale their models on any infrastructure—local, hybrid, or multi-cloud—without deep engineering expertise. The platform handles containerization, API creation, and auto-scaling, accelerating the path from development to production for various AI applications, including Generative AI and Computer Vision.