Key takeaway
- Compare MLOps platforms for enterprises and SMEs: SageMaker, Google Cloud, Azure ML, Kubeflow, MLflow. Retired names, trade-offs and adoption steps.
An MLOps platform is the shared layer that takes a model from experiment to production and keeps it healthy: pipelines, a model registry, deployment, monitoring and an audit trail. The real choice for most enterprises is not which brand is best, but which operating model fits: a managed cloud service, an open-source stack you run yourself, or a commercial AutoML or agent platform. This guide compares the main options as their own sites describe them, with no rankings, and shows how to match one to your estate.
If you are still deciding what MLOps should cover, read our guides to MLOps processes and tools and to ModelOps vs MLOps first. For device fleets, see MLOps for scaling TinyML. Disclosure: XenonStack sells related services, including MLOps managed services, and ElixirClaw and ElixirData are our own products. Vendor statements below are marked "according to its site" and were read on 12 November 2026.
What has changed in the MLOps platform landscape?
Several names that older vendor lists still carry are retired or renamed. Check this table before you shortlist anything.
| Name you may still see | Current status | What to do |
| Google AI Platform | Google's current pages do not use the AI Platform name. Its MLOps tooling is now documented under Vertex AI and, in newer documentation, Gemini Enterprise Agent Platform; some URLs still contain "vertex-ai". | Evaluate the current Google Cloud offering, not the legacy name. |
| Neptune.ai (hosted) | According to its transition hub, the hosted app and API shut down on 5 March 2026, after an agreed acquisition by OpenAI that was described as subject to closing conditions when we read it. | Do not shortlist it for new work; plan a migration if you still use it. |
| Amazon SageMaker | The AWS MLOps page is filed under "Amazon SageMaker AI" while the body text still says SageMaker. | Use the current name in contracts and architecture documents. |
| Kubeflow Model Registry | The Kubeflow site now lists this component as "Hub (formerly Model Registry)", and says Kubeflow is a CNCF graduated project. | Check component names against the current docs before you plan an install. |
Which MLOps platform fits which situation?
The table lists each platform with the capabilities its own page names. It is not a ranking and it does not score vendors. Pricing is left out on purpose because it changes and depends on usage.
| Platform | Model | According to its site | When it fits |
| Amazon SageMaker AI | Managed, AWS | Lists Projects templates, Pipelines to automate the end-to-end workflow, lineage tracking, Model Registry, Model Monitor for model and concept drift, and managed MLflow tracking servers. | Data and workloads already sit on AWS. See our SageMaker MLOps guide. |
| Google Cloud (Vertex AI / Gemini Enterprise Agent Platform) | Managed, Google Cloud | Describes MLOps tools to "automate, standardize, and manage ML projects", including Model Evaluation, Pipelines, Model Registry, Feature Store and monitoring for input skew and drift. | Your analytics estate is on Google Cloud and you want one managed lifecycle. |
| Azure Machine Learning | Managed, Azure | Lists reproducible pipelines, model registration and versioning, monitoring and alerts, and lineage data for lifecycle governance such as who published a model and when it was deployed (page updated 6 October 2025). | You run on Azure and need lineage for governance reviews. |
| Kubeflow | Open source, Kubernetes | Calls itself the Kubernetes-native stack for data and AI workloads, with Pipelines, Trainer, Katib for hyperparameter tuning and a model Hub. | You already operate Kubernetes well and want portability across clouds and on-premises. |
| MLflow | Open source, Apache 2.0 | Describes itself as an open-source AI engineering platform with experiment tracking, evaluation and a production model registry, plus observability for LLM apps and agents. | You need a tracking and registry layer that works across clouds and frameworks. |
| DataRobot | Commercial | Now positions itself as a platform to build, operate and govern agents, with lifecycle logging, access and approval controls and audit documentation. | You want a vendor-run governed platform and are moving towards agents. Confirm classic MLOps scope in a demo. |
| H2O.ai | Commercial | Positions its Sovereign AI Platform for running predictive, generative and agentic AI in your own environment, up to air-gapped. Its home page does not describe MLOps features. | Data residency or air-gapped deployment is a hard requirement. Verify MLOps scope with the vendor. |
| Seldon | Commercial and open source | Says it helps teams deploy, monitor and explain machine learning models. Its docs describe Seldon Core 2 as a modular framework for scaling ML and LLMOps in production, and MLServer as a lightweight inference server. | Your gap is serving and monitoring, not experiment tracking. Check licence terms for the version you plan to use. |
Should you choose a managed cloud service, open source or a commercial platform?
Start from where your data and compliance boundary already sit, then decide how much platform engineering you can afford.
| Option | Strength | Trade-off | Best for |
| Managed cloud service | Fast start, integrated security and billing, fewer moving parts to run. | Ties pipelines to one provider's services and naming, which changes over time. | Teams standardised on one cloud. |
| Open-source stack | Portability, no licence fee, control over components. | You own upgrades, security patching, scaling and integration. | Mature platform teams, multi-cloud or on-premises estates. |
| Commercial platform | Vendor support and packaged governance features. | Contract dependence and scope that must be checked in a proof of value. | Teams with limited ML engineering capacity and a clear budget. |
A common mixed pattern is an open-source tracking and registry layer such as MLflow on top of a managed training and serving service. Smaller companies can start with managed MLflow or a single cloud's native tooling and add components only when a gap shows up in production.
What criteria should an enterprise use to evaluate MLOps platforms?
| Criterion | Question to ask the vendor or your team |
| Pipelines and deployment | Can training, evaluation and release run as code, with rollback if an endpoint update fails? |
| Registry and lineage | Can you show who approved a model version, what data trained it and where it runs? |
| Monitoring | Does it detect data and concept drift, and who is alerted? |
| Integration | Does it work with your frameworks, data platform, CI/CD and identity provider? |
| Security and compliance | Where does data live, how is access controlled, and can you export audit evidence? |
| Cost and exit | What drives usage cost, and how hard is it to move models and metadata out? |
What should governance, security and cost reviews cover?
- Governance: keep a model inventory, approval records and lineage for every production model. Platforms such as SageMaker AI and Azure Machine Learning document lineage and registry features; confirm they meet your own audit needs.
- Security: review data residency, secrets handling, network isolation and role-based access. Do not assume a certification applies to your configuration; ask the vendor for current evidence.
- Cost: model training, serving and monitoring separately. Idle endpoints and unmanaged experiments are the usual surprises, so set budgets and alerts early.
- Agents: if models will feed autonomous agents, record which decisions they may take and under which policy. AgentGRC, according to its site, keeps autonomous AI agents within set policies and records evidence that auditors and risk teams can review.
How should you sequence adoption?
- Pick one production use case with a named business owner, and define the metric that shows the model is still useful.
- Standardise tracking and registry first. Reproducibility is the cheapest win and works with any platform choice.
- Automate the release path with pipelines, tests and a rollback step.
- Add monitoring and drift alerts with an owner who acts on them.
- Add governance: approvals, lineage and an inventory that risk and audit teams can read.
- Scale to more teams only after the first path runs without manual fixes.
When is a full MLOps platform the wrong choice?
A platform adds cost and operating effort. It is often too much if you have one or two models that retrain rarely, if no one owns monitoring, or if the blocker is data quality rather than deployment. In those cases begin with experiment tracking, a registry and a scripted pipeline, and revisit when the number of models or the regulatory pressure grows. See AIOps vs MLOps if your real need is operations analytics rather than model engineering.
Where do XenonStack, ElixirClaw and ElixirData fit?
XenonStack provides consulting and managed services around MLOps, which is why we say plainly that we are not a neutral party. ElixirClaw is, according to its site, an enterprise platform where AI agents carry out multi-step business workflows across existing systems, under policy limits and a full decision audit trail. It is not an MLOps platform in the sense of the table above. ElixirData Context OS keeps governed context, versioned policy and decision traces in one decision layer above AI agents, so it sits on top of whichever MLOps platform you pick.
To map one model-driven decision your agents should make, and the context and policy it depends on, book a Context Workshop. See what happens in the workshop.
Frequently asked questions
What is an MLOps platform?
It is the shared tooling that moves models from experiment to production and keeps them monitored: pipelines, a model registry, deployment, drift monitoring and lineage. Vendor pages such as AWS, Google Cloud and Microsoft list these same building blocks.
Is Google AI Platform still available?
Current Google Cloud documentation no longer uses the AI Platform name. Its MLOps tooling is documented under Vertex AI and, in newer documentation, Gemini Enterprise Agent Platform. Evaluate the current offering.
Is Neptune.ai still an option?
According to its own transition hub, the hosted app and API shut down on 5 March 2026. Treat it as retired for new projects.
Should we choose open source or a managed service?
Choose a managed service if your data already sits with one cloud and you have little platform engineering capacity. Choose open source such as Kubeflow or MLflow when you need portability and can run the stack yourself.
Do we need an MLOps platform for one model?
Usually not. Start with experiment tracking, a registry and a scripted release pipeline, and add a platform when models, teams or audit demands grow.
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