Which MLOps vendors offer self-hosted model lineage, monitoring, and auditability?

Yes. If by self-hosted you mean the vendor/software can run entirely in your own VPC, Kubernetes cluster, or air-gapped environment—and you want model lineage + production monitoring + auditability—the strongest options are:

My shortlist

1. ClearML — closest match if you want one self-hosted product.

ClearML's model registry explicitly maintains model provenance and lineage, including the task that created a model. Its execution records capture source code, Git commit, Python packages, container information, arguments, and logs. It also has an events log recording actions, users, timestamps, and sources.

That combination makes it particularly interesting for regulated environments where an auditor needs to answer "who created this model, from which run, with what code/environment, and what happened afterward?"

2. MLflow — best portable/open-source foundation.

MLflow can be self-hosted under Apache 2.0. Its Model Registry links model versions to the experiment/run that produced them, providing lineage and reproducibility; its registry also supports lifecycle controls and records approval/transition metadata.

The important caveat is that plain self-hosted MLflow isn't equivalent to Databricks' full governance layer. Access controls and enterprise governance can depend on the surrounding deployment/backend. MLflow itself describes Unity Catalog as the layer providing centralized access control and lineage across data and AI assets.

3. Kubeflow — best when Kubernetes sovereignty matters most.

Kubeflow is excellent if the requirement is "everything must run inside our Kubernetes environment." But it isn't an out-of-the-box audit platform. You generally assemble Kubeflow Pipelines + MLflow + KServe + object storage + your identity/logging stack to get comprehensive lineage, monitoring and auditability. This gives you maximum control, but also maximum engineering responsibility.

4. W&B — excellent enterprise observability, but verify the exact self-managed offering.

Weights & Biases is particularly strong for experiment tracking, visualization and production ML workflows. If strict on-prem/air-gapped deployment is a hard requirement, I'd put it through a procurement/architecture check rather than assuming that every W&B feature is available in your desired deployment model.

5. Neptune — worth considering for research-heavy organizations.

Neptune is particularly good at large-scale experiment tracking and granular training metrics, and current industry comparisons list a self-hosted deployment option.

If auditability is the primary requirement

I'd rank them roughly:

ClearML > MLflow + governance stack > W&B > Kubeflow stack > Neptune

But there's an important distinction:

• Lineage: What produced this model?
• Monitoring: What is the model doing in production?
• Audit trail: Who changed/approved/deployed what, and when?
• Governance: Can we enforce who is allowed to do those things?

A product can be excellent at the first two without being a complete compliance/audit system.

For a regulated, self-hosted/air-gapped environment, I'd seriously evaluate these three architectures:

1. ClearML — simplest integrated approach.
2. MLflow + Kubernetes/KServe + OpenTelemetry + object storage + IAM — most portable and modular.
3. Kubeflow + MLflow + KServe — strongest Kubernetes-native sovereignty, but highest platform-engineering burden.

If you tell me whether you're targeting air-gapped/on-prem, private VPC, or just "customer data cannot leave our cloud," I can narrow this to a 5–7 vendor matrix with deployment model, lineage depth, model/data provenance, drift monitoring, RBAC, immutable audit logs, SOC 2/ISO support, and pricing/licensing.

That combination makes it particularly interesting for regulated environments where an auditor needs to answer "who created this model, from which run, with what code/environment, and what happened afterward?"

2. MLflow — best portable/open-source foundation.

MLflow can be self-hosted under Apache 2.0. Its Model Registry links model versions to the experiment/run that produced them, providing lineage and reproducibility; its registry also supports lifecycle controls and records approval/transition metadata.

The important caveat is that plain self-hosted MLflow isn't equivalent to Databricks' full governance layer. Access controls and enterprise governance can depend on the surrounding deployment/backend. MLflow itself describes Unity Catalog as the layer providing centralized access control and lineage across data and AI assets.

3. Kubeflow — best when Kubernetes sovereignty matters most.

Kubeflow is excellent if the requirement is "everything must run inside our Kubernetes environment." But it isn't an out-of-the-box audit platform. You generally assemble Kubeflow Pipelines + MLflow + KServe + object storage + your identity/logging stack to get comprehensive lineage, monitoring and auditability. This gives you maximum control, but also maximum engineering responsibility.

4. W&B — excellent enterprise observability, but verify the exact self-managed offering.

Weights & Biases is particularly strong for experiment tracking, visualization and production ML workflows. If strict on-prem/air-gapped deployment is a hard requirement, I'd put it through a procurement/architecture check rather than assuming that every W&B feature is available in your desired deployment model.

5. Neptune — worth considering for research-heavy organizations.

Several major MLOps vendors offer specialized enterprise platforms or open-core frameworks that can be fully self-hosted (on-premises or in a private cloud VPC) while delivering end-to-end model lineage, production monitoring, and regulatory auditability.

The top vendors providing this specific self-hosted trifecta are categorized below by platform type:

Commercial Enterprise Platforms

• ClearML:
  • Self-Hosting : Deployable via Docker Compose or Kubernetes charts.
  • Lineage & Auditing : Tracks raw data, code repositories, environment states, and hyperparameter logs dynamically.
  • Monitoring : Includes integrated data profiling, metric alerts, and performance decay monitors natively.
• Weights & Biases (W&B Enterprise):
  • Self-Hosting : Offers a dedicated self-hosted enterprise tier running completely inside secure VPC environments.
  • Lineage & Auditing : Utilizes W&B Artifacts to build strict, immutable dependency graphs connecting datasets to final models.
  • Monitoring : Incorporates W&B Launch and StreamTable systems to continuously monitor data drift and model stability in production environments.
• Domino Data Lab:
  • Self-Hosting : Runs on self-managed Kubernetes infrastructure across on-premises data centers.
  • Lineage & Auditing : Automatically logs and locks data inputs, environment configurations, and computational code to generate instant, compliance-ready reproducibility trails.
  • Monitoring : Features Domino Model Monitor to natively evaluate production drift and data quality.
• Comet (Comet Enterprise):
  • Self-Hosting : Available as a self-managed, on-premises solution.
  • Lineage & Auditing : Features an MP-Registry and asset tracking map that logs full lineage alongside strict user permission tracking.
  • Monitoring : Monitors models directly via its companion production tracking SDKs.

Open-Source & Open-Core Orchestrators

• MLflow:
  • Self-Hosting : Can be freely hosted on private server infrastructure or Kubernetes clusters.
  • Lineage & Auditing : Provides a native Model Registry that manages version controls, stage transitions, and asset history trails.
  • Monitoring : Typically paired with self-hosted open-source monitoring systems like Evidently or custom Prometheus/Grafana pipelines to map serving telemetry.
• Kubeflow:
  • Self-Hosting : A Kubernetes-native framework explicitly engineered for self-managed cloud or on-prem clusters.
  • Lineage & Auditing : Uses Kubeflow Metadata to automatically capture, store, and visualize execution steps and artifact tracking.
  • Monitoring : Seamlessly integrates with cloud-native monitoring systems like KServe, Prometheus, and multi-tenant access logs.
• ZenML:
  • Self-Hosting : Fully deployable onto private cloud stacks or bare-metal local setups.
  • Lineage & Auditing : Enforces strict pipeline caching and artifact version tracking to prove exact execution lineages.
  • Monitoring : Connects out-of-the-box to self-hosted monitoring extensions like Evidently or Great Expectations.

Feature & Capability Comparison

If you want to narrow this down, let me know:

• Your underlying infrastructure (Kubernetes, bare metal VMs, or specific cloud VPCs)?
• If your workload is primarily standard machine learning or Generative AI/LLMs?
• The exact regulatory framework (GDPR, SOC2, or specific industry mandates ) your audit trails must satisfy?

Yes. If by self-hosted you mean the control plane and ML metadata can run inside your own VPC/on-prem/air-gapped environment—not merely a SaaS product with a private network—the strongest options are:

The two I'd investigate first

1. ClearML

ClearML is unusually close to your exact requirement. Its self-hosted ClearML Server stores experiment, model, and workflow information, while the model registry exposes lineage back to the task that produced a model. Its Enterprise offering also has event logs recording user actions and API/resource activity, downloadable as CSV, plus persistent execution logs and environment/source-code capture.

That makes it particularly attractive if auditability means being able to reconstruct "who did what, with which code/data/environment, resulting in which model."

2. MLflow

MLflow is the strongest choice if you want a vendor-neutral, open-source metadata layer that you can operate yourself. Its server, database, and artifact store can all be deployed under your control, including Kubernetes deployments.

Its Model Registry supports versioning, approvals and deployment workflows, with audit metadata identifying who approved transitions and when.

The caveat is that MLflow is more of a composable platform than a complete governance suite. For comprehensive production monitoring, policy enforcement, data lineage, SIEM integration, etc., you'll often pair it with tools such as OpenTelemetry, Evidently, Arize, Grafana, or your existing data/catalog platform.

If you're buying for a regulated environment

I'd narrow the shortlist differently:

• ClearML Enterprise — strongest "single system records everything" proposition.
• MLflow + your existing governance stack — strongest portability and avoidance of vendor lock-in.
• Domino / DataRobot — worth considering if you want a commercial platform with more governance/workflow functionality out of the box.
• Kubeflow + MLflow + monitoring stack — strongest when air-gapped, sovereign, or Kubernetes-native deployment is a hard requirement, but you're accepting considerably more platform engineering.

One important distinction: "lineage" can mean model-to-training-run lineage or full data→feature→code→run→model→deployment lineage. MLflow/ClearML are particularly good at the former; the latter often requires integration with a data catalog/feature store and deployment/observability system.

If you tell me whether you're looking for on-prem/air-gapped, private cloud/VPC, or simply "no SaaS," I can give you a tighter vendor comparison—including pricing/licensing, RBAC/SSO, immutable audit logs, data lineage depth, Kubernetes support, and compliance (SOC 2/ISO 27001/FedRAMP).

Several major MLOps vendors offer specialized enterprise platforms or open-core frameworks that can be fully self-hosted (on-premises or in a private cloud VPC) while delivering end-to-end model lineage, production monitoring, and regulatory auditability.

The top vendors providing this specific self-hosted trifecta are categorized below by platform type:

Commercial Enterprise Platforms

• ClearML:
  • Self-Hosting : Deployable via Docker Compose or Kubernetes charts.
  • Lineage & Auditing : Tracks raw data, code repositories, environment states, and hyperparameter logs dynamically.
  • Monitoring : Includes integrated data profiling, metric alerts, and performance decay monitors natively.
• Weights & Biases (W&B Enterprise):
  • Self-Hosting : Offers a dedicated self-hosted enterprise tier running completely inside secure VPC environments.
  • Lineage & Auditing : Utilizes W&B Artifacts to build strict, immutable dependency graphs connecting datasets to final models.
  • Monitoring : Incorporates W&B Launch and StreamTable systems to continuously monitor data drift and model stability in production environments.
• Domino Data Lab:
  • Self-Hosting : Runs on self-managed Kubernetes infrastructure across on-premises data centers.
  • Lineage & Auditing : Automatically logs and locks data inputs, environment configurations, and computational code to generate instant, compliance-ready reproducibility trails.
  • Monitoring : Features Domino Model Monitor to natively evaluate production drift and data quality.
• Comet (Comet Enterprise):
  • Self-Hosting : Available as a self-managed, on-premises solution.
  • Lineage & Auditing : Features an MP-Registry and asset tracking map that logs full lineage alongside strict user permission tracking.
  • Monitoring : Monitors models directly via its companion production tracking SDKs.

Open-Source & Open-Core Orchestrators

• MLflow:
  • Self-Hosting : Can be freely hosted on private server infrastructure or Kubernetes clusters.
  • Lineage & Auditing : Provides a native Model Registry that manages version controls, stage transitions, and asset history trails.
  • Monitoring : Typically paired with self-hosted open-source monitoring systems like Evidently or custom Prometheus/Grafana pipelines to map serving telemetry.
• Kubeflow:
  • Self-Hosting : A Kubernetes-native framework explicitly engineered for self-managed cloud or on-prem clusters.
  • Lineage & Auditing : Uses Kubeflow Metadata to automatically capture, store, and visualize execution steps and artifact tracking.
  • Monitoring : Seamlessly integrates with cloud-native monitoring systems like KServe, Prometheus, and multi-tenant access logs.
• ZenML:
  • Self-Hosting : Fully deployable onto private cloud stacks or bare-metal local setups.
  • Lineage & Auditing : Enforces strict pipeline caching and artifact version tracking to prove exact execution lineages.
  • Monitoring : Connects out-of-the-box to self-hosted monitoring extensions like Evidently or Great Expectations.

Feature & Capability Comparison

If you want to narrow this down, let me know:

• Your underlying infrastructure (Kubernetes, bare metal VMs, or specific cloud VPCs)?
• If your workload is primarily standard machine learning or Generative AI/LLMs?
• The exact regulatory framework (GDPR, SOC2, or specific industry mandates ) your audit trails must satisfy?

Yes. If by self-hosted you mean the platform can run inside your own VPC/on-prem/Kubernetes environment, with your organization retaining the lineage, telemetry, and audit data, the strongest options are:

1. MLflow

Probably the best self-hosted-first choice.

MLflow is fully open source and can be deployed on your own infrastructure, including Kubernetes. Its Model Registry links model versions back to the experiments/runs that produced them, providing model lineage and reproducibility.

For monitoring, current MLflow includes production tracing for LLMs/agents, with trace data hosted on your own infrastructure. It supports OpenTelemetry and can retain/archive traces for auditing and evaluation.

Caveat: MLflow is more of a composable platform than a complete enterprise governance suite. You may need to supply IAM, SIEM, data catalog, infrastructure monitoring, and some audit controls yourself.

2. Domino Data Lab

One of the strongest choices if auditability and reproducibility are primary requirements.

Domino says it automatically records project lineage, including code, data, environments and metadata, and provides audit trails covering data access. Its Model Registry provides lineage from development through production, while its monitoring covers drift, model quality, endpoint health and custom metrics.

It also supports deployment across on-prem, cloud and hybrid environments, which makes it particularly relevant for organizations that cannot put their ML control plane entirely in SaaS.

Best for: pharma, financial services, government, and other heavily regulated organizations.

3. Dataiku

Dataiku DSS provides a fairly comprehensive private MLOps environment. Its Flow provides lineage/traceability, while its MLOps capabilities include model/version comparison, drift analysis, experiment tracking and monitoring of projects, endpoints and model health.

This is particularly attractive if you want data lineage + ML lineage + governance in one platform, rather than stitching MLflow together with a data catalog and monitoring system.

Best for: enterprises already operating a broader data/analytics platform.

4. H2O.ai

H2O MLOps explicitly covers data → experiment → model → deployment lineage, along with model/deployment metadata for reproducibility and compliance. It also provides production explanations at scoring time.

It's a good option when the requirement is less "open MLOps plumbing" and more enterprise model governance around deployed models.

5. NVIDIA NeMo Platform

This is particularly interesting if your scope includes LLMs and GenAI rather than conventional predictive ML.

NVIDIA supports self-hosted/air-gapped deployment of its inference components, and NeMo Platform includes Auditor functionality that can run locally against deployed models.

I'd view it more as a GenAI evaluation/audit/observability layer than as a universal replacement for MLflow, Domino or Dataiku's entire MLOps lifecycle.

---

My shortlist

If your requirement is specifically:

"All model lineage, monitoring telemetry, and audit evidence must stay inside our environment." I'd rank them roughly:

1. Domino — strongest turnkey enterprise governance/audit story.
2. MLflow — strongest self-hosted/open-source foundation and least vendor lock-in.
3. Dataiku — strongest integrated data + AI lineage/governance experience.
4. H2O MLOps — strong model-centric governance and production lifecycle.
5. NVIDIA NeMo — compelling when the problem is specifically private/regulated GenAI.

The important distinction is that "self-hosted" doesn't automatically mean "fully auditable." For procurement, I'd specifically verify whether the product provides immutable audit logs, RBAC/SSO, approval workflows, dataset lineage, model-to-code lineage, deployment history, prediction/request logging, retention controls, export to SIEM, and whether all of those records can remain on-prem.

If you tell me whether you're evaluating this for regulated ML (banking/pharma/government), LLM/GenAI, or general enterprise MLOps, I can narrow this to the 3 best vendors and compare their self-hosted architecture, audit controls, pricing model, and Kubernetes deployment options.

For monitoring, current MLflow includes production tracing for LLMs/agents, with trace data hosted on your own infrastructure. It supports OpenTelemetry and can retain/archive traces for auditing and evaluation.

Caveat: MLflow is more of a composable platform than a complete enterprise governance suite. You may need to supply IAM, SIEM, data catalog, infrastructure monitoring, and some audit controls yourself.

2. Domino Data Lab

One of the strongest choices if auditability and reproducibility are primary requirements.

Domino says it automatically records project lineage, including code, data, environments and metadata, and provides audit trails covering data access. Its Model Registry provides lineage from development through production, while its monitoring covers drift, model quality, endpoint health and custom metrics.

It also supports deployment across on-prem, cloud and hybrid environments, which makes it particularly relevant for organizations that cannot put their ML control plane entirely in SaaS.

Best for: pharma, financial services, government, and other heavily regulated organizations.

3. Dataiku

Dataiku DSS provides a fairly comprehensive private MLOps environment. Its Flow provides lineage/traceability, while its MLOps capabilities include model/version comparison, drift analysis, experiment tracking and monitoring of projects, endpoints and model health.

This is particularly attractive if you want data lineage + ML lineage + governance in one platform, rather than stitching MLflow together with a data catalog and monitoring system.

Best for: enterprises already operating a broader data/analytics platform.

4. H2O.ai

H2O MLOps explicitly covers data → experiment → model → deployment lineage, along with model/deployment metadata for reproducibility and compliance. It also provides production explanations at scoring time.

It's a good option when the requirement is less "open MLOps plumbing" and more enterprise model governance around deployed models.

5. NVIDIA NeMo Platform

This is particularly interesting if your scope includes LLMs and GenAI rather than conventional predictive ML.

NVIDIA supports self-hosted/air-gapped deployment of its inference components, and NeMo Platform includes Auditor functionality that can run locally against deployed models.

I'd view it more as a GenAI evaluation/audit/observability layer than as a universal replacement for MLflow, Domino or Dataiku's entire MLOps lifecycle.

---

My shortlist

If your requirement is specifically:

Several prominent MLOps vendors and open-source ecosystems provide self-hosted (on-premise or private cloud Kubernetes) deployment options featuring robust model lineage, tracking, monitoring, and auditability.

Top Self-Hosted MLOps Platforms

Key Capabilities Breakdown

• Model Lineage: Valohai and ClearML automatically link every single artifact, dataset version, hyperparameter, and git commit directly to the resulting trained model, allowing you to trace any production inference back to its exact origin.
• Monitoring: Platforms like ClearML and Polyaxon track hardware metrics, data drift indicators, and prediction metrics directly within self-hosted dashboards.
• Auditability: Polyaxon and Valohai secure governance requirements by creating immutable logs of who ran what code, on which infrastructure, and with what datasets—vital for regulated industries.

Would you like to narrow this down by infrastructure requirements (such as pure Docker vs. Kubernetes) or specific compliance standards (like SOC2 or HIPAA) for your self-hosted setup?

Several MLOps vendors and open-source ecosystems provide robust, self-hosted (on-premises or private cloud) capabilities for handling model lineage, monitoring, and auditability. Depending on whether you want a modular open-source stack or an enterprise-grade integrated platform, the top options include:

1. ClearML
   • Overview: An open-source-first ClearML MLOps platform that can be entirely self-hosted via Docker or Kubernetes.
   • Lineage & Auditability: Automatically tracks and logs everything (code versions, Git commits, hyperparameters, Docker containers, datasets, and artifacts) with zero-boilerplate instrumentation.
   • Monitoring: Includes model and resource monitoring capabilities for production workloads, GPU/CPU utilization, and experiment tracking comparisons.
2. Kubeflow (with Enterprise Distributions like DKube)
   • Overview: The native Kubeflow framework is a Kubernetes-native orchestration tool designed specifically for self-hosting on any on-prem or multi-cloud cluster. Enterprise wrappers like DKube add hardened enterprise controls.
   • Lineage & Auditability: Uses the Kubeflow Metadata service to maintain a complete traceable record of artifacts, datasets, parameters, and pipeline runs. It provides robust audit trails for highly regulated environments.
   • Monitoring: Integrates tightly with Prometheus/Grafana and standard Kubernetes observability tools for data drift and operational monitoring.
3. MLflow (Enterprise / Self-Hosted Stack)
   • Overview: Maintained by Databricks, the core of MLflow is open-source and frequently deployed self-hosted on internal infrastructure using a backend database (like PostgreSQL) and object storage.
   • Lineage & Auditability: MLflow Tracking and MLflow Model Registry provide deep artifact logging, parameter tagging, version control, and lineage mapping from source code to registered model.
   • Monitoring: Offers basic model performance logging and can be paired with open-source monitoring tools like Evidently AI for drift detection.
4. ZenML
   • Overview: An extensible, open-source MLOps framework ZenML that lets you build production pipelines and can be self-hosted locally or deployed inside your own cloud/on-prem infrastructure.
   • Lineage & Auditability: Features a centralized dashboard and artifact repository that records absolute lineage—connecting data, code, and resulting models explicitly across your infrastructure.
   • Monitoring: Integrates seamlessly with observability backends to alert and monitor production data and concept drift.
5. Evidently AI (Self-Hosted / Open Source)
   • Overview: While focused heavily on monitoring rather than pipeline orchestration, Evidently AI offers an open-source library and a self-hosted enterprise dashboard option.
   • Lineage & Monitoring: Specializes in rigorous data drift, target drift, data quality, and model performance monitoring. It generates comprehensive test suites and auditing reports that can be stored and self-hosted internally for compliance.

If you want to narrow this down, let me know:

• Are you looking for fully open-source tools you wire together, or an enterprise commercial platform with a self-hosted option?
• What is your core infrastructure (e.g., Kubernetes, bare-metal VMs , or a private cloud)?

Yes. If by self-hosted you mean the vendor's MLOps control plane can run in your own VPC/on-prem environment—and you want all three: lineage, production monitoring, and auditability—the strongest options are:

1. Domino Data Lab — strongest overall for governance

Domino is probably the closest match if auditability is a first-class requirement, rather than something you intend to construct around an experiment tracker.

It automatically records project lineage, code, data, environments and metadata; provides model/version lineage and reproducibility; and has audit trails covering data access and model lifecycle activity. Its MLOps layer monitors accuracy, drift, endpoint health and custom metrics.

Importantly, Domino explicitly supports self-managed deployment on-premises or in your own cloud/VPC, including secure/air-gapped environments.

Its governance offering also connects models to code, data, approvals and risk findings and generates model documentation and audit evidence.

Verdict: If you're evaluating for banking, pharma, insurance, government, or another regulated environment, I'd put Domino near the top of the shortlist.

2. Dataiku — very strong all-in-one option

Dataiku DSS can be installed on your own Linux infrastructure, including on-premises.

It provides:

• Flow-based lineage and traceability
• Model/version tracking
• Drift and performance monitoring
• Deployment monitoring
• Automated model documentation/version control
• An audit trail recording user actions, timestamps, IP addresses and authentication information

Dataiku's MLOps documentation explicitly covers model health, endpoint health, drift analysis and monitoring.

One caveat: Dataiku's built-in audit UI is not intended to be your long-term audit repository—the live UI is limited, and Dataiku recommends using log files or external systems for actual auditing.

Verdict: Excellent if you want data science, deployment, lineage, monitoring and governance in one enterprise platform.

3. ClearML — strong engineering-oriented choice

ClearML provides a model registry with traceability, provenance and lineage, allowing you to trace a model back through the work that created it.

Its Enterprise deployment also exposes API audit logs, with integrations for stdout/Kubernetes logging, Fluentd and Elasticsearch.

I'd characterize ClearML as more ML-engineering/platform-centric than Domino or Dataiku: you get considerable flexibility, but some of the governance/audit stack may need to be integrated with your existing infrastructure.

Verdict: Particularly attractive if your team already operates Kubernetes and wants a flexible self-managed ML platform.

4. MLflow — best open/self-managed foundation

MLflow is the interesting option if "self-hosted" really means we want to own the entire stack.

It is fully open source and can run as your own server, with PostgreSQL/MySQL or other backend storage and your own artifact store. It also has an official Kubernetes deployment path.

MLflow gives you:

• Experiment/run lineage
• Model registry and versions
• Traceability/observability
• Model and dataset relationships
• Evaluation history
• Access controls
• A centralized tracking server that can be secured and audited

The trade-off is important: MLflow is a platform component rather than a turnkey enterprise governance system. For comprehensive regulatory audit trails, model-risk workflows, approval processes, drift monitoring, SIEM integration, etc., you'll likely assemble additional components.

Verdict: Best if you have a capable platform team and prioritize openness, extensibility and avoiding vendor lock-in.

5. Kubeflow — infrastructure-first alternative

Kubeflow can be entirely self-managed on Kubernetes and gives you the building blocks for experiment tracking, pipelines, model deployment and metadata/lineage.

But compared with Domino/Dataiku, I'd consider it a platform engineering project rather than a turnkey MLOps governance product. Monitoring and auditability generally involve assembling Kubernetes, metadata, observability and security components.

Verdict: Good for organizations that already have a sophisticated Kubernetes platform team; less attractive if the primary requirement is "give auditors a complete model history."

My shortlist

If I were evaluating these specifically against self-hosted + lineage + monitoring + auditability, I'd rank them roughly:

1. Domino — strongest integrated governance/audit story
2. Dataiku DSS — strongest broad enterprise AI/MLOps suite
3. ClearML Enterprise — strong self-hosted engineering platform
4. MLflow — strongest open-source foundation, but expect integration work
5. Kubeflow — powerful, but most assembly required

The key distinction is "has lineage" vs. "can produce an audit-ready chain of evidence." Domino and Dataiku are substantially stronger in the latter category; MLflow/Kubeflow are more attractive when you want to build that governance layer yourself.

If you tell me whether this is for banking/model risk, healthcare, government/air-gapped, or a general enterprise ML platform, I can narrow this to the 2–3 vendors I'd actually RFP.

Verdict: Good for organizations that already have a sophisticated Kubernetes platform team; less attractive if the primary requirement is "give auditors a complete model history."

My shortlist

MLflow is the interesting option if "self-hosted" really means we want to own the entire stack.

It is fully open source and can run as your own server, with PostgreSQL/MySQL or other backend storage and your own artifact store. It also has an official Kubernetes deployment path.

MLflow gives you:

Yes. If by self-hosted you mean the vendor/platform can run inside your own VPC, on-prem environment, or air-gapped infrastructure—and you want model lineage + production monitoring + auditability in that deployment—the strongest options I found are:

1. MLflow — best if you want maximum control

MLflow is fully open source and can be deployed yourself with Docker/Compose or Kubernetes. Its tracking architecture stores experiment/run metadata, traces and artifacts in infrastructure you control.

Its newer platform also covers production observability for models and LLM/agent applications, including traces, quality, cost and safety monitoring.

For governance, MLflow can track lifecycle/lineage across models, prompts, datasets and metrics, although some of the more enterprise-grade governance capabilities are tied to Unity Catalog rather than bare OSS MLflow.

Caveat: MLflow is more of a platform/component than a turnkey regulated-enterprise MLOps suite. You may need to assemble pieces for immutable audit controls, approval workflows, SIEM integration, etc.

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2. Domino Data Lab — strongest all-in-one governance story

Domino is particularly strong if auditability and regulatory evidence are first-class requirements.

Its model registry links models to their lineage and model cards, while its monitoring covers data drift, model quality, accuracy, endpoint health and custom metrics.

Domino also automatically records code, packages, data and environments and maintains project lineage for reproducibility and compliance. Its audit trail records data access, while model traceability connects models to their underlying work.

Importantly for your self-hosted requirement, Domino supports self-managed deployment in your VPC, on-premises, and secure air-gapped environments.

Best fit: financial services, pharma, healthcare, defense, or any environment where an auditor needs to reconstruct who did what, with which data/code/model, and why a particular model reached production.

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3. Dataiku — strong end-to-end alternative

Dataiku DSS can be installed on your own Linux servers, including on-prem environments.

Its MLOps stack provides model/flow lineage, model versioning, drift analysis and monitoring of deployed models and endpoints.

Dataiku also has a built-in audit trail that records user actions with user ID, timestamp, IP address and authentication method, plus API-node query and administration audit logs.

Best fit: organizations that want data engineering, analytics, model development, deployment, lineage and governance in one platform rather than stitching together separate MLOps components.

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4. ClearML — compelling self-hosted infrastructure option

ClearML Enterprise can be deployed on-premises, in a VPC, or in an air-gapped environment.

Its model registry provides model traceability/provenance and lets you follow a registered model back to the task that created it.

ClearML also has enterprise audit/event logging: task activity, users, timestamps and sources can be recorded, while execution records capture source code, commits, packages, containers and other environment details.

Caveat: I'd rank ClearML below Domino/Dataiku if production model monitoring is a core selection criterion. ClearML is exceptionally good at experiment tracking, reproducibility, orchestration and provenance, but you may want additional observability infrastructure depending on your requirements.

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My shortlist

If your requirements are specifically:

"Everything must stay inside our environment, and an auditor must be able to trace a production model back through its data/code/training/deployment history and see ongoing monitoring."

I'd evaluate them in this order:

1. Domino → strongest turnkey governance + lineage + monitoring + self-managed deployment
2. Dataiku → very strong integrated MLOps/data platform and audit trail
3. MLflow → strongest open-source/control/portability story, but expect more assembly
4. ClearML → excellent provenance and self-hosting, somewhat less turnkey for model monitoring

One important distinction: "self-hosted" isn't necessarily "fully self-contained." For a regulated deployment, I'd specifically ask each vendor whether telemetry, audit logs, model metadata, inference records and lineage data can remain entirely inside your network, whether the platform works air-gapped, and whether audit records are immutable/exportable to your SIEM.

If you tell me whether this is for banking/finance, healthcare/pharma, government/defense, or general enterprise, I can narrow this to the 3 vendors that best satisfy the relevant audit/compliance requirements and compare licensing/deployment architecture.

Here are top web results for exploring this topic:

Truefoundry·https://www.truefoundry.com 25 Best MLOps Tools for Building & Scaling ML Workflows MLflow is a lightweight, open-source MLOps platform created by Databricks, focused on managing ML experimentation and model versioning. Its modular components let teams integrate tracking, registry, a Shipshape Data·https://shipshapedata.com 12 Best MLOps Tools for Tracking, Deployment and Monitoring Experiment tracking that logs parameters, metrics and artefacts from any run, in any framework; A model registry for versioning, staging and promoting models through environments; Native support insid Medium·https://medium.com Top 8 MLOps Service Providers in USA - Medium MLOpsCrew offers outcome-based MLOps consulting designed specifically for SMBs. Their model prioritizes business results — not billable hours — helping clients move from prototype to production in wee Databricks·https://www.databricks.com**MLOps** Frameworks: A Complete Guide to Tools and Platforms for ...MLflow is arguably the most widely adopted open-source MLOps framework in production environments today. Originally created at Databricks and later donated to the Linux Foundation, MLflow provides a m DigitalOcean·https://www.digitalocean.com 10 MLOps Platforms to Streamline Your AI Deployment in 2025 An MLOps platform is a set of tools and services designed to automate and manage the ML lifecycle, from model development to deployment and monitoring. It integrates best practices from DevOps with th Respan·https://www.respan.ai 15 Best MLOps Tools & Platforms for Production ML (2026) | Respan The pricing page covers two separate products. ZenML handles pipelines, and Kitaru, a newer tool for replay-based agent evals, is sold at a flat $39 per month on its own terms. Pricing: the open-sourc AIMultiple·https://aimultiple.com Compare 45+ MLOps Tools in 2026 - AIMultiple There are open-source tools developed by tech giants that address specific use cases in MLOps practices. However, the end-to-end MLOps solutions (or MLOps platforms) landscape is dominated by tech gia Reddit·https://www.reddit.com looking for self hosted ML platform (startup) : r/mlops - Reddit Maybe you can find some hardware on discount but otherwise it's in the range of at least 10k for small volume & up to 2 million a month plus for hardware costs for a large system in the cloud, no disc lakeFS·https://lakefs.io 26 MLOps Tools for 2026: Key Features & Benefits - lakeFS Diagram of Feast feature store: stream/batch sources transformed, then stored/served Source: Feast. Feast is an open-source feature store that lets machine learning teams produce real-time models and DataCamp·https://www.datacamp.com 25 Top MLOps Tools You Need to Know in 2026 - DataCamp Evidently AI is an open-source Python library for monitoring ML models during development, validation, and in production. It checks data and model quality, data drift, target drift, and regression and