RunPod

Run AI workloads on any cloud with automatic cost optimization

SkyPilot is an open-source framework for running LLMs, AI, and batch jobs on any cloud with automatic cost optimization. It supports AWS, GCP, Azure, Lambda Cloud, and more, automatically selecting the cheapest available GPUs and managing spot instance preemption. Features include multi-cloud job scheduling, managed spot jobs with automatic recovery, and cluster autoscaling with 6,000+ GitHub stars.

Run and deploy ML models via API with simple pricing

Cloud platform that lets developers run thousands of open-source and proprietary public ML models through a simple API without managing GPUs or infrastructure. Replicate hosts models for image, text, audio, and video, supports Cog-based custom deployments and private models, and now operates as a distinct Cloudflare brand with pay-by-time or input/output pricing depending on the model.

Heterogeneous CPU-GPU inference and SFT for large MoE models

Open-source framework for running and fine-tuning large Mixture-of-Experts models with heterogeneous CPU-GPU execution, optimized kernels, limited VRAM and SGLang or LLaMA-Factory integrations.

Official Kubernetes and Helm reference stack built on the vLLM inference engine

Official vLLM reference implementation for scaling the existing inference engine on Kubernetes with Helm, request routing, KV-cache offload, autoscaling and Prometheus/Grafana observability.

Distributed inference orchestration above vLLM, SGLang and TensorRT-LLM

Open-source, datacenter-scale orchestration layer that coordinates vLLM, SGLang and TensorRT-LLM across nodes with disaggregated serving, KV-aware routing, multi-tier cache management and automatic scaling.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Disaggregated KV cache storage and transfer for LLM serving

Open-source infrastructure for disaggregated LLM serving that pools KV caches across prefill and decode workers, with high-performance transfer, distributed storage and integrations for vLLM and SGLang.

Reusable KV cache infrastructure for scalable LLM inference

Open-source KV cache management layer that persists, offloads and reuses model key-value caches across requests and serving engines to reduce repeated prefill work and improve inference throughput.