Self-hosted MLOps platforms for lineage, monitoring, and auditability

Vendors worth evaluating

1. Domino Data Lab

(Strongest governance fit)

Enterprise platform · Self-managed, hybrid, and air-gapped options

Domino combines model lineage, reproducibility, a governed model registry, approval workflows, audit trails, and production monitoring for drift and accuracy. A strong candidate for regulated enterprises that want an integrated system of record.domino.ai+2

Official platform ·Reproducibility and lineage

2. Dataiku

Enterprise platform · Deployable in customer-managed environments; verify exact edition and architecture

Provides end-to-end workflow lineage, model versioning, drift analysis, centralized monitoring, and documentation. Its DSS audit trail records user actions, timestamps, and other activity details. Particularly suitable when governance must cover both visual and code-based analytics.doc.dataiku.com+2

MLOps capabilities ·Audit trail documentation

3. ClearML

Self-hostable MLOps · Open-source core and enterprise capabilities

Offers experiment tracking, artifact and model versioning, a model registry, provenance, and lineage across training workflows. A good option for engineering-led teams that want to own their infrastructure. Verify which access controls and audit-log features require an enterprise license.clear.ml+6

Model registry documentation

4. MLflow

Open source · Fully self-hostable

Tracks experiment parameters, code-related metadata, metrics, artifacts, and model versions. Its registry and newer tracing and evaluation capabilities can support lineage and production observability. It is an excellent low-lock-in foundation, but comprehensive enterprise audit governance may require additional components and controls.mlflow.org+2

Self-hosting ·Experiment tracking

5. Kubeflow

Open source · Self-managed Kubernetes

Kubeflow Pipelines records metadata and artifact-execution lineage, while Kubeflow Hub and Model Registry support model registration, versioning, and lifecycle traceability. Monitoring and enterprise-grade audit controls generally require integration with additional infrastructure. Best for Kubernetes-native teams comfortable assembling a platform.www.kubeflow.org+1

Model registry ·Pipeline lineage

6. Comet

Commercial platform · On-premises deployment options

Provides experiment tracking, model registry, version history, and artifact lineage linking trained models to experiments and datasets. Worth evaluating when reproducibility and model provenance are priorities. Confirm the exact self-hosted deployment package and production-monitoring scope with the vendor.www.comet.com+1

Experiment tracking and model lineage

7. Arize Phoenix

Open source · Self-hosted · Especially relevant to LLMs

Captures LLM traces, inputs and outputs, latency, tool and retrieval activity, and evaluation results. Useful for debugging and monitoring AI applications, but it is not a complete replacement for a traditional model registry or enterprise-wide training lineage system. Pair it with MLflow or another lifecycle platform.github.com+2

Phoenix documentation ·Self-hosting and deployment

How they compare

This is a qualitative comparison of documented capabilities, not an independent feature or compliance certification audit.domino.ai+6

My recommendation

• For regulated enterprise ML: start with Domino and Dataiku. Examine how each handles immutable evidence, approval histories, access logs, and retention.
• For a self-hosted, open-source stack: start with MLflow; add Kubeflow if you need Kubernetes-native pipeline orchestration, and Phoenix if you need LLM-specific observability.
• For engineering teams seeking an integrated experiment and model-management system: evaluate ClearML and Comet, with particular attention to enterprise licensing and audit capabilities.

One important caveat: self-hosted does not automatically mean that every feature runs offline, that audit records are immutable, or that the platform satisfies a specific regulation.

Three questions that would narrow the shortlist

1. What models are you operating?

Traditional ML (forecasting, classification, computer vision)

LLMs and AI agents

Both traditional ML and LLMs

2. What does self-hosted mean for you?

Fully on-premises / air-gapped

Private cloud / customer VPC

Either, provided data stays under our control

3. What matters most?

Regulatory auditability (e.g. approvals, audit trails, reproducibility)

Production drift and performance monitoring

End-to-end lineage from data to deployed model

Open source and low vendor lock-in

[Narrow the shortlist]