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

Several MLOps vendors support self-hosted model lineage, production monitoring, and auditability, including open-source platforms you can run entirely on your own infrastructure and commercial enterprise platforms deployable on-premises or in your own cloud environment.

The key distinction is that self-hosted can mean either running the entire platform yourself or deploying a vendor's commercial software in your VPC. Those options differ in licensing, operational overhead, and how much governance functionality is included.

Leading vendors to evaluate

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1. MLflow

(Best open-source starting point)

Deployment: Self-hosted, open source

Provides experiment tracking, model registry, versioning, run metadata, and lineage-related tracking. Its newer AI observability capabilities include tracing, evaluation, and monitoring of LLM applications. You operate the tracking server, database, artifact storage, and access controls yourself.github.com+2

Official site ·Self-hosting documentation

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2. Domino Data Lab

Deployment: Self-managed, on-premises, hybrid, air-gapped options

A strong enterprise choice when auditability is a first-class requirement. Offers automated experiment and model lineage, reproducibility, a governed model registry, approval workflows, audit trails, and monitoring for drift, accuracy, and operational performance. Commercial platform rather than a purely open-source stack.domino.ai+2

Platform ·Reproducibility and lineage

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3. Dataiku

Deployment: Enterprise deployment options; verify the exact self-managed edition

Its Flow visualizes project and model lineage, while its MLOps functionality covers model versioning, drift analysis, experiment tracking, endpoint health, and monitoring. Useful for organizations that want a broader data science platform rather than assembling separate tools.doc.dataiku.com+6

MLOps documentation

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4. ClearML

Deployment: Self-hosted platform; enterprise features vary by plan

Tracks experiments, artifacts, model versions, provenance, and model lineage. Its model registry provides traceability across the lifecycle, and ClearML can orchestrate training and deployment workflows. A practical candidate for teams that want an integrated MLOps stack with a self-managed option.clear.ml+6

Model registry documentation

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5. Kubeflow

(Best Kubernetes-native option)

Deployment: Self-hosted, open source

Kubeflow Pipelines records pipeline runs, artifacts, executions, and their relationships through ML Metadata. Its model registry manages model versions and artifacts. Production monitoring and comprehensive audit reporting typically require additional components and integration work.www.kubeflow.org+1

Pipeline lineage ·Model registry

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6. Arize AI — Arize AX

Deployment: Commercial self-hosted, VPC, and air-gapped options

Particularly compelling for AI observability. Its self-hosted AX platform supports LLM tracing, evaluations, experiments, and monitoring within your infrastructure. It is more focused on observing model behavior than on replacing the entire training-to-deployment MLOps stack, so pair it with a model registry or pipeline platform if you need end-to-end lineage.arize.com+6

Self-hosted deployment

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7. Fiddler AI

Deployment: Commercial on-premises and private-environment options

Best considered as a monitoring and governance specialist. Provides ML and LLM observability, drift and quality monitoring, explainability, guardrails, and auditable records of AI actions and policy decisions. It is not a complete substitute for a training pipeline and model registry.www.fiddler.ai+1

AI observability

Comparison at a glance

The table distinguishes native capabilities from capabilities that commonly need integration or configuration.

Sources: official documentation from github.comdomino.aidoc.dataiku.comclear.mlwww.kubeflow.orgarize.comwww.fiddler.ai.

My recommendation by use case

• Need the strongest out-of-the-box lifecycle governance: Start with Domino. Its lineage, reproducibility, approvals, and monitoring are integrated rather than requiring you to assemble multiple tools.
• Need open source and maximum infrastructure control: Start with MLflow. Add a production monitoring tool and configure centralized access controls, retention, and audit logging.
• Already standardized on Kubernetes: Evaluate Kubeflow plus MLflow and a dedicated monitoring system.
• Need specialized ML/LLM production monitoring in a private environment: Compare Arize AX and Fiddler AI. Keep your existing registry and pipeline tooling if those are already working.
• Need a broad data science platform: Evaluate Dataiku alongside Domino.

Important due-diligence questions

Before selecting a vendor, confirm these five things in a proof of concept:

1. True self-hosting: Can the full application, metadata, model artifacts, telemetry, and authentication run in your environment, including an air-gapped environment if necessary?
2. Complete lineage: Does it connect source code, datasets, feature versions, training runs, parameters, model artifacts, deployments, and production predictions?
3. Monitoring: Does it detect data drift, concept drift where measurable, quality degradation, and infrastructure failures? Does it support alerts and model-specific thresholds?
4. Auditable records: Can you identify who changed, approved, deployed, or accessed a model, and when? Can records be exported and protected against unauthorized modification?
5. Compliance evidence: Can the system produce reproducible evidence for your specific regulatory requirements? A model registry or monitoring dashboard alone does not establish compliance.

Bottom line: I'd shortlist Domino, MLflow, ClearML, and Arize AX first, with Kubeflow as an infrastructure-oriented alternative and Fiddler as a specialist for monitoring and AI governance. The best choice depends on whether you want one integrated MLOps platform or a modular self-hosted stack.