The Best Marquez Alternatives in 2026

DataHub describes itself as a context platform for data and AI stacks.

ModelDB tracks machine learning model versions, metadata, and experiments.

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

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Cog packages machine learning models into containers to support deployment across environments.

DJL is a Java framework for running deep learning models across different engines.

Kitaru provides agent traces that teams can run and inspect during application workflows.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).

Manifest connects AI agents and tools to multiple model providers through an LLM gateway.

File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

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

Flexible, high-performance serving system for machine learning models.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

Latitude provides observability for AI agents and helps teams trace failures and verify fixes.

MMDeploy provides a model deployment framework from the OpenMMLab project.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.

Instill Core coordinates data, models, and pipelines for AI first applications.

Kedro-Viz visualizes Kedro data and machine learning pipelines and tracks experiments.