Encore

Heroku-like PaaS built on Kubernetes with YC backing

Porter is a YC-backed platform-as-a-service that provides a Heroku-like deployment experience on top of Kubernetes. It abstracts away cluster management while giving teams full access to underlying infrastructure when needed. Supports deploying from Git repos, Docker images, or Helm charts with automatic HTTPS, scaling, and preview environments. Runs on AWS, GCP, or Azure.

Ephemeral preview environments for every pull request

Bunnyshell automates the creation of full-stack ephemeral environments that spin up for every pull request and tear down when merged. Each environment is a complete replica of the application stack including databases, APIs, and frontend services. Integrates with GitHub, GitLab, and Bitbucket to provide shareable preview URLs for testing, QA, and stakeholder review.

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.