Dagster

Data quality validation framework for Python

Great Expectations is an open-source Python framework for validating, documenting, and profiling data quality. Teams define expectations as expressive unit tests for their data using an intuitive API, then validate datasets against those rules in CI/CD pipelines or production workflows. It connects to pandas, Spark, and SQL sources, generates data documentation automatically, and integrates with orchestrators like Airflow and Prefect for continuous data quality monitoring.

Declarative code-first ELT data integration

Meltano is a declarative, code-first data integration engine with 500+ Singer connectors for building ELT pipelines. It replaces custom API integration code with configuration-driven pipeline definitions that live in version control alongside application code. Integrates with dbt for transformation, supports scheduling and monitoring through a unified CLI, and powers production pipelines at scale.

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.