Kubecost

Open-source Kubernetes cost monitoring (CNCF)

OpenCost is a CNCF-certified open-source tool for real-time Kubernetes cost monitoring that maps cloud spend directly to namespaces, deployments, pods, and labels. It provides granular cost allocation across teams and projects without vendor lock-in, supporting AWS, GCP, Azure, and on-premises clusters as the industry standard for open-source FinOps visibility in cloud-native environments.

AI-powered autonomous cloud cost optimization for AWS

Zesty uses AI to automatically optimize AWS cloud costs by analyzing usage patterns and making real-time resource adjustments. It manages Reserved Instance and Savings Plan portfolios autonomously, right-sizes EC2 instances based on actual utilization, and optimizes EBS volumes and storage costs. Claims average 51% savings on AWS compute spend with no engineering effort required.

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.