The Best DataHub Alternatives in 2026

Marquez collects, aggregates, and visualizes metadata about a data ecosystem.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

This repository describes technologies for enabling AI capabilities with large and multimodal language models.

OpenMetadata provides data cataloging, discovery, quality, observability, governance, and lineage tools.

Butterfree provides tools for building feature stores and transforming raw data.

DeepLake provides multimodal data infrastructure for AI agents, including retrieval and training.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

Quilt manages scientific data on AWS with versioned data packages for teams and AI.

PySyft supports data science on data that remains on another person’s server.

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

The colibri-core NLP tool and library works with n-grams, skipgrams, and other linguistic patterns.

Gymnasium provides a standard API for single agent reinforcement learning environments and related utilities.

Chunk Tuner benchmarks document chunking strategies for RAG and recommends a configuration through a CLI, Python library, or MCP server.

PromptSite provides prompt version management for tracking, experimentation, and debugging LLM prompts.

DEAP provides a Python framework for prototyping evolutionary computation algorithms and testing new ideas.

OpenLLMetry provides OpenTelemetry-based observability for generative AI and large language model applications.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

ColossalAI provides tools intended to make large AI models cheaper, faster, and more accessible.

DeepEval is an LLM evaluation framework, according to its repository description.

The linfa project provides a machine learning framework written in Rust.

autobatcher batches asynchronous OpenAI-compatible API requests through a drop-in replacement library.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

FATE supports federated learning through an industrial grade framework.