Port

Spotify's open-source developer portal framework

Backstage is Spotify's open-source framework for building internal developer portals, now a CNCF incubating project with 27,000+ GitHub stars. It provides a unified software catalog, service documentation, CI/CD pipeline views, and a plugin architecture with 200+ community plugins. Teams use it to create a single pane of glass for service ownership, API documentation, infrastructure management, and developer self-service workflows.

AI-powered production incident resolution

Resolve AI automates production incident investigation, diagnosis, and remediation acting as an AI SRE that participates in every on-call rotation. Autonomously investigates incidents pursuing multiple hypotheses in parallel, validates against real evidence, creates code snippets and drafts PRs, generates post-mortems, and onboards new teammates with instant answers about code and infrastructure. Drives 5x faster MTTR and 87% faster incident investigations.

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.