Best of the Year serverless AI AI Tool

Oneinfer is a high-performance AI inference platform for developers. It offers a unified API to access over 15 LLMs like GPT-4 and Claude, simplifying AI integration. The platform features serverless deployment, automatic scaling, enterprise-grade security, and pay-as-you-go pricing. It also provides a marketplace for renting GPU instances for custom AI workloads.

Dank is a JavaScript-native, open-source framework for orchestrating and deploying containerized AI agents. It enables developers to build, manage, and scale multiple AI agents as microservices across any cloud infrastructure, simplifying complex AI deployments with Docker-native architecture and real-time monitoring.

Avian is a high-performance AI inference platform offering world-record speeds for large language models (LLMs). It provides both a serverless API for popular models and dedicated GPU deployments for custom models from HuggingFace. Designed for scalability and production workloads, Avian delivers 3-10x faster inference speeds than the industry average, with enterprise-grade security and competitive pricing.

Zetic.ai is a platform that enables developers to deploy AI models directly on edge devices, eliminating the need for expensive GPU servers. Its automated pipeline, ZETIC.MLange, optimizes and converts models for on-device execution, achieving up to 60x faster performance with NPU acceleration while ensuring data privacy and reducing latency.

SiliconFlow is a unified AI infrastructure platform designed for high-performance inference of Large Language Models (LLMs) and multimodal models. It provides developers and enterprises with scalable, cost-effective, and flexible deployment options, including serverless APIs, reserved GPUs, and fine-tuning capabilities, all accessible through a single, OpenAI-compatible API.

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.