Incident.io

AI-powered incident management in Slack and Teams

Rootly is an AI-native incident management platform that runs entirely within Slack and Microsoft Teams, automating incident workflows from detection through postmortem. It reduces manual incident overhead with AI-generated summaries, automated role assignments, escalation paths, and postmortem drafts, holding SOC 2 Type II, GDPR, and HIPAA compliance certifications for enterprise use.

Open-source SOAR platform with AI-powered playbooks

Tracecat is a YC S24-backed open-source SOAR (Security Orchestration, Automation and Response) platform that lets security teams build AI-powered playbooks for automated incident response. It provides visual workflow builders for creating response procedures, integrates with common security tools, and handles alert triage, enrichment, and remediation — positioned as an open-source alternative to Tines and Splunk SOAR.

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.