The backbone of reproducible machine learning — track experiments, version models, audit pipelines, and maintain a single source of truth for every artifact in your ML workflow.
5
Solutions
3
Categories
2026
Updated
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Why ML Metadata Stores Matter
Machine learning metadata stores capture the full lineage of your ML experiments — parameters, metrics, datasets, model artifacts, and pipeline executions. Without a metadata store, teams lose track of which experiment produced which result, making reproducibility impossible and model governance a nightmare. These tools provide the audit trail and governance foundation that every serious ML team needs.
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Neptune
neptune.ai
Cloud Platform
Neptune is a metadata store purpose-built for MLOps, designed for research and production teams that run a large volume of experiments. Per Neptune's support pages and press reports, OpenAI agreed to acquire Neptune and the hosted service was shut down in early March 2026, so it is kept here for reference only; check Neptune's transition notice. It provides a unified workspace to log, compare, and visualize experiment parameters, metrics, model checkpoints, and output artifacts, enabling teams to rapidly identify the best-performing configurations and reproduce any past run. Update, October 2026: OpenAI agreed to acquire Neptune in December 2025, and Neptune's own support page says the hosted service was permanently discontinued in March 2026, so this entry is kept for reference only.
Real-time experiment tracking and comparison dashboards
Integrates with 50+ ML frameworks and libraries
Collaborative workspace for research and production teams
TensorFlow ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows. As part of the TFX (TensorFlow Extended) ecosystem, MLMD uses a graph-based data model to capture artifacts, executions, and contexts — providing a complete lineage trail from raw data ingestion through model serving, essential for pipeline debugging and regulatory auditing. Update, October 2026: the latest GitHub release listed is ML Metadata 1.21.0, from February 2026.
Graph-based metadata model with full artifact lineage
Part of the TFX production ML pipeline framework
Supports multiple backends including SQLite and gRPC
Google Cloud Vertex ML Metadata captures your ML system's metadata as a graph, providing a fully managed service for tracking artifacts, executions, and contexts within the Vertex AI platform. It leverages the open-source ML Metadata library under the hood while adding cloud-native features like IAM integration, regional storage, and seamless connectivity with Vertex Pipelines, Feature Store, and Model Registry. Update, October 2026: Google's documentation for this service now sits under the Gemini Enterprise Agent Platform.
Fully managed graph-based metadata on Google Cloud
Native integration with Vertex AI Pipelines
Enterprise-grade IAM, security, and regional storage
The MLflow Model Registry is a centralized model store, set of APIs, and UI to collaboratively manage the full lifecycle of an MLflow Model. It provides model versioning, aliases, tags, annotations, and approval workflows — making it a widely used choice for model governance in open-source MLOps stacks. Update, October 2026: MLflow's documentation says Model Stages are deprecated and will be removed in a future major release, and recommends aliases and tags instead.
Centralized model versioning with aliases and tags
Collaborative review and approval workflows
Framework-agnostic with broad ecosystem integration
ArangoML provides support for common metadata storage across the entire machine learning lifecycle, enabling reproducibility, monitoring, and auditing for your ML models. Built on ArangoDB's native multi-model database engine, it combines graph, document, and key-value capabilities to capture complex ML pipeline relationships — from data lineage graphs to model dependency trees — in a single queryable system. Update, October 2026: arangodb.com now redirects to arango.ai, where the company presents the Arango Contextual Data Platform.
Native graph database for ML pipeline lineage
Multi-model: graph + document + key-value in one engine
A system that records experiments, parameters, metrics, datasets and model versions so ML work can be tracked, compared, reproduced and audited.
Which solutions are compared?
TensorFlow ML Metadata (MLMD), Vertex AI ML Metadata, MLflow and ArangoML, plus Neptune, whose hosted service has closed (see the Neptune card).
How is this different from a feature store?
A metadata store tracks the history of experiments and models. A feature store manages and serves features for training and inference. See the Feature Store Solutions page.
Is this a ranking?
No. It is a comparison with links to each product. Check each vendor for current capabilities.
Which links may need a manual click?
Links were rechecked in October 2026. The Neptune product link did not respond to automated checks, consistent with the card's note that its hosted service was discontinued in March 2026; it is kept for reference only. For LLM tracking and evaluation, see LLMOps and LLM Evaluation Methods.