Kubeflow

Modern workflow orchestration for data pipelines

Prefect is an open-source workflow orchestration framework with 18K+ GitHub stars providing a Python-native approach to building, scheduling, and monitoring data pipelines. Turns any Python function into a schedulable, observable workflow with decorators. Features automatic retries, caching, concurrency controls, event-driven triggers, and a modern dashboard. Easier to adopt than Airflow with less boilerplate. Prefect Cloud provides managed orchestration with team collaboration features.

ML model serving and deployment framework

BentoML is an open-source framework with 7K+ GitHub stars for packaging, deploying, and serving ML models as production-ready APIs. Bundles models, preprocessing, and serving logic into portable Bento archives with auto-generated REST/gRPC endpoints. Features adaptive batching for throughput optimization, GPU scheduling, multi-model inference pipelines, and containerization. Supports all major ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.

Evaluation framework for RAG pipelines

RAGAS is an Apache-2.0 open-source evaluation framework with 14K+ GitHub stars that provides standardized metrics for assessing RAG pipeline quality. It measures faithfulness, answer relevancy, context precision, and context recall to identify whether retrieval, generation, or both are failing. It is framework-agnostic, supports LLM-as-judge evaluation, and its README discloses minimal anonymized Open Analytics with a RAGAS_DO_NOT_TRACK opt-out.

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.