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

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.

---

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.

---

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.

---

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.

---

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

Yes. If self-hosted means the vendor’s software can run entirely in your own Kubernetes/on-prem/cloud environment, and you want model lineage + production monitoring + auditability, the strongest options are:

My shortlist

1. MLflow — best overall for an infrastructure-controlled stack.
MLflow can be completely self-hosted, with its tracking server, SQL metadata backend, and artifact store running in your environment. MLflow 3 also brings model/version metadata, evaluation data and production traces into the same platform.

Its big advantage is that you're not buying into a proprietary control plane: the OSS server can sit behind your own authentication, storage, SIEM and audit infrastructure.

2. Seldon — strongest if "auditability" is a first-class requirement.
Seldon Enterprise Platform is explicitly designed to run on a customer-managed Kubernetes cluster. It provides model catalogs/metadata, performance and drift monitoring, and deployment governance.

Its audit logging is particularly compelling: requests to the Seldon platform can be logged with user identity, timestamp, source address, request URI, response status, etc., and sent through Fluentd to storage you control. docs.seldon.ai GitOps also gives you deployment-history audit trails.

3. Arize — strongest monitoring/observability option.
Arize supports self-hosted environments, while its open-source Phoenix product can be hosted locally for tracing and evaluation. arize.com I'd view it primarily as an observability layer, rather than the sole system of record for traditional model lifecycle lineage and governance.

An important distinction

There are really two different kinds of lineage:

• Training/model lineage: dataset → experiment/run → code/config → model version → deployment
• Inference lineage: model/version → request → prediction/trace → outcome/feedback

MLflow is particularly attractive if you need the first and increasingly the second. Seldon is particularly attractive for the production/deployment side, where you need model requests, operational metrics and administrative actions to be auditable. Seldon's monitoring stack exposes model metrics through Prometheus and supports performance/drift monitoring.

If you're evaluating vendors for a regulated enterprise, I'd put Seldon + MLflow at the top of the RFP and consider Arize as the observability specialist. If you tell me whether you're looking for on-prem air-gapped, private cloud, or simply customer-managed Kubernetes, I can narrow this to 3–5 vendors and compare their lineage depth, RBAC, immutable audit logs, retention, SIEM integration, drift monitoring, and licensing.

Several platforms and vendors provide self-hosted (on-premises or private cloud/Kubernetes) capabilities for model lineage, monitoring, and auditability. They range from pure open-source stacks to enterprise-grade commercial platforms that can be fully air-gapped or deployed within your own infrastructure.

• MLflow (Open Source / Databricks): Highly popular for self-hosted setups. Using MLflow Tracking and the Model Registry , it logs parameters, code versions, metrics, and artifacts. When deployed on Kubernetes backed by an internal relational database and object store, it provides comprehensive model versioning and clear lineage. Enterprise auditability can be supplemented via role-based access control (RBAC) layers.
• Kubeflow (Open Source / Kubernetes Native): A container-native platform tailored for Kubernetes. It manages end-to-end ML workflows via Kubeflow Pipelines and utilizes Kubeflow Metadata to automatically track artifact, dataset, and model lineage. Monitoring can be natively bridged using Prometheus and Grafana for infrastructure and custom metric tracking.
• Weights & Biases (W&B) (Commercial with Self-Hosted Option): While widely used as a SaaS tool, W&B offers an enterprise self-hosted/on-premise deployment option. It acts as a system of record for experiment tracking, providing exhaustive artifact lineage (linking datasets, code, and hyperparameters to specific model weights) and robust model registry audit trails.
• Comet ML (Commercial with Self-Hosted Option): Comet provides dedicated on-premises and self-hosted deployment packages. It excels at experiment tracking, code/data lineage visualization, and model registry management, tracking every transition and artifact change to satisfy audit workflows.
• TrueFoundry (Cloud-Agnostic / Kubernetes): A modular, developer-facing MLOps platform that installs directly into your own Kubernetes cluster. It gives organizations total control over data and model security, offering built-in model registry, lineage tracking across workflows, role-based access control (RBAC), and low-latency request logging and observability.
• Pachyderm (Commercial/Open-core on Kubernetes): Best known for combining data version control (think Git for data) with automated pipeline execution. It tracks mathematically proven, automated data and model lineage through every transformation step on Kubernetes, making it ideal for strict compliance and audit requirements.
• Datatron (Enterprise ModelOps): Built specifically for governance, compliance, and lifecycle management. Its Actionable Model Catalog tracks model transparency and lineage from experimentation through production, featuring built-in model drift monitoring, Challenger/Publisher gateways, and full audit logs.

If you want to narrow down these choices, let me know:

• Are you looking for open-source tools you manage yourself, or commercial enterprise software with vendor support?
• Does your self-hosted environment run strictly on Kubernetes (K8s) , or do you need support for bare-metal / virtual machines?

Several MLOps platforms and toolchains support self-hosting (either via open-source or on-premises enterprise editions) to deliver model lineage, experiment tracking, auditability, and production monitoring.

The primary options capable of being run entirely within your own infrastructure (such as a private Kubernetes cluster or on-prem servers) break down as follows:

• MLflow
  • Lineage & Auditability : Tracks parameters, metrics, code versions, and artifacts through its Tracking component, and manages model promotion/versioning via the MLflow Model Registry.
  • Monitoring : Can be paired with open-source tools or integrated with production logging, though native deep production drift monitoring usually requires complementary stacks (like Evidently AI).
  • Self-Hosting : 100% open-source under the Apache 2.0 license and easily self-hosted on local servers or Kubernetes using a backend database (like PostgreSQL) and artifact store (like MinIO or S3-compatible local storage).
• ClearML
  • Lineage & Auditability : Offers automatic experiment logging (capturing code, inputs, exact environment, and artifacts) and robust Git-like dataset/model versioning that forms a complete lineage graph.
  • Monitoring : Provides comprehensive infrastructure and model metrics monitoring dashboards out of the box.
  • Self-Hosting : Can be fully self-hosted on-premises (using their open-source server Docker image which bundles elasticsearch, mongo, and redis), giving you an end-to-end self-hosted system.
• Kubeflow
  • Lineage & Auditability : Uses Kubeflow Metadata to automatically track lineage and artifacts across every step of a Kubeflow Pipeline.
  • Monitoring : Integrates natively with tools like Prometheus and Grafana for cluster and model service monitoring.
  • Self-Hosting : Kubernetes-native by design, meant to be deployed on-prem or on private clouds wherever Kubernetes runs.
• Comet (Enterprise Self-Hosted)
  • Lineage & Auditability : Deeply focuses on governance and compliance audit trails. It logs data provenance, preprocessing steps, and artifact lineage from training straight into production tracking.
  • Monitoring : Extends the training audit trail into production performance monitoring.
  • Self-Hosting : Offers dedicated enterprise on-premises/self-hosted deployment options designed explicitly for regulated industries (finance, healthcare) that require air-gapped compliance.
• Pachyderm
  • Lineage & Auditability : Treats data and model lineage as a core primitive using data-centric Git-like version control running on Kubernetes containers. Every transformation is immutably tracked from raw data to trained model.
  • Monitoring : Tracks pipeline execution health and data shifting, though it acts primarily as the data/model provenance engine rather than an inference-drift monitor.
  • Self-Hosting : Designed explicitly for enterprise on-prem or private cloud Kubernetes environments.

If you have a preference for your underlying infrastructure—such as Kubernetes-native vs. standalone Docker/VMs —or if you need compliance features like SSO/RBAC , let me know and I can help narrow down the ideal stack.

Yes. If by self-hosted you mean the platform can run in your own VPC/on-prem/air-gapped environment—not merely a single-tenant SaaS—the strongest options are:

The standouts

1. Domino Data Lab — probably the closest match to your requirements.
Domino explicitly supports self-managed deployments on AWS, Azure, GCP, or on-prem, including air-gapped environments. Its model registry tracks lineage, code, data, approvals and risk findings; its monitoring covers drift, model quality and endpoint health; and its audit functionality records access and lifecycle evidence.

2. Dataiku — particularly strong if "auditability" means formal governance.
Dataiku's Flow provides model lineage and traceability, while its MLOps layer provides model/version comparison, drift analysis and health monitoring. Its self-managed deployment also has an Event Server for API-node event logs.

3. H2O.ai — strong end-to-end model/deployment lineage.
H2O MLOps maintains lineage from data → experiment → model registry → deployment, keeps metadata for reproducibility, provides deployment event logs identifying who did what and when, and supports model monitoring/drift detection. H2O AI Cloud can be operated in customer-managed cloud or on-prem environments.

4. ClearML — attractive if you want more control and less proprietary infrastructure.
ClearML Server can be deployed yourself via Docker, EC2 or Kubernetes. Its registry provides model provenance/lineage, while its execution records capture source code, commit, packages, container details and logs. Its Enterprise offering adds more formal event/audit capabilities.

5. MLflow — best building block if you don't need a turnkey governance suite.
MLflow is fully open source and explicitly designed for self-hosting. Its tracking server records experiments, runs, artifacts and traces; the Model Registry handles versions and metadata; and self-hosted deployments can use PostgreSQL plus your own artifact storage. Trace archival also supports retaining historical traces for audit/debugging.

6. DataRobot — worth considering for highly regulated environments.
DataRobot has a Self-Managed AI Platform, including on-prem deployments. Its MLOps layer supports deployment monitoring, while its governance capabilities cover lineage, versioning, audit trails and approvals.

How I'd shortlist them

If your requirement is specifically "all three capabilities must live inside our environment", I'd narrow it to:

1. Domino — best integrated governance + lineage + monitoring.
2. Dataiku — best for enterprise process/governance and broad data/ML workflows.
3. H2O.ai — best for model-centric MLOps and production deployment.
4. ClearML — best self-hosted/open-oriented alternative.
5. MLflow — best open foundation, but expect to assemble some governance/monitoring pieces yourself.
6. DataRobot — strong regulated-enterprise option, particularly if you want a more turnkey platform.

One important distinction: "auditability" isn't the same as merely having logs. For a regulated deployment I'd verify that the vendor can preserve an immutable or appropriately controlled history of model version → training run → code → data/features → approval → deployment → predictions/monitoring → subsequent changes, with RBAC and exportable audit evidence. That's where Domino, Dataiku, H2O.ai and DataRobot generally have an advantage over a bare MLflow deployment.