US inference platform for open, fine-tuned and custom models on autoscaling GPUs, with Model APIs, training and self-hosting.
Model deployment: AI tools compared (October 2026)
How results are ranked: How we review
Results
GPU cloud for training and serving large AI models, with a managed Kubernetes service, Slurm integration and managed inference.
Serverless Python platform for AI inference, training, batch jobs and sandboxes with per-second GPU billing.
Fully managed vector database with integrated embedding models, full-text search and an assistant component for RAG applications.
Open-source vector database from Berlin, available as managed cloud, hybrid cloud in your own infrastructure or private cloud.
Inference platform for open models with per-token serverless billing, dedicated GPU deployments and fine-tuning including RFT.
Managed Hugging Face service that deploys Hub models on dedicated, autoscaling infrastructure on AWS, Google Cloud or Azure.
Prebuilt NVIDIA inference microservices that package models, optimized engines and standard APIs in containers for your own GPUs.
AI cloud for serverless inference of open models, dedicated endpoints, fine-tuning and GPU clusters.
Open source library from Intel's Open Edge Platform for visual anomaly detection, with OpenVINO export.
Open-source platform from Xorbits that serves language, embedding, rerank, image and audio models behind an OpenAI-compatible API.
2022 paper introducing exact, memory-efficient attention on GPUs, now used in many training and inference stacks.
Open-source framework from the ModelScope community for fine-tuning, evaluating and deploying 600+ language models.
Open-source serving framework for language and multimodal models with prefix caching and fast structured output.
SOSP 2023 paper introducing PagedAttention and the vLLM serving system for running large language models.
Open-source library for fast inference and serving of language models with an OpenAI-compatible server and broad hardware support.
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