AI Infrastructure · Top 10
The 10 Best MLOps Platforms in 2027
Direct answer
ClearML is the best pick on our rule: its own pages name all 5 lifecycle areas, it has a $0 Community plan and open-source self-hosting, and Pro is $15 per user per month plus usage. Amazon SageMaker AI is #2 and Databricks Managed MLflow is #3, also at 5 of 5. ClearML is also the best value, as the top-scoring pick with an open-source option and a $0 plan. Neptune could not be ranked because its site could not be read.
At a glance
| # | Pick | Price | Best for |
|---|---|---|---|
| 1 | ClearML | Pro is $15 per user per month plus usage; Community is $0; Scale and Enterprise are custom quotes. Example usage rate: $0.1 per 1GB of artifact storage (read 2026-11-08) | Teams that want tracking, a model repository, pipelines and serving in one tool that can be self-hosted for free |
| 2 | Amazon SageMaker AI | Usage-based: you pay for the underlying compute and storage. Example rate: a pricing example lists ml.c5.xlarge at $0.204 per hour (read 2026-11-08) | Teams already on AWS who want managed tracking, registry, pipelines and monitoring on pay-as-you-use compute |
| 3 | Databricks Managed MLflow | Pay as you go with a 14-day free trial; Model Serving is metered in DBUs, for example 10.48 DBUs per hour for a T4 GPU. The dollar price per DBU is not shown in the page text read (read 2026-11-08) | Teams that already run data and ML on Databricks and want MLflow managed there |
| 4 | MLflow | No paid plan: MLflow says it is 100% open source under the Apache 2.0 license and forever free (read 2026-11-08) | Teams that want the open standard for tracking and registry and will host it themselves or buy it managed |
| 5 | Azure Machine Learning | Microsoft says there is no additional charge to use Azure Machine Learning; you pay for compute and other Azure services. Example rate: $1.196 per hour per DS14 v2 VM, with a $0 per core Azure Machine Learning charge (read 2026-11-08) | Teams on Azure who want pipelines, managed endpoints and CI/CD tied into Microsoft tools |
| 6 | Google Vertex AI | Usage-based. Example rates: $0.03 per pipeline run and $3.50 per GB analyzed by Model Monitoring; the pricing page is titled Gemini platform pricing (read 2026-11-08) | Teams on Google Cloud who want registry, pipelines and monitoring next to BigQuery |
| 7 | ZenML Pipelines | Starter is $39 a month with unlimited seats, 1 ML project and up to 100 pipeline runs a month; Enterprise is custom (read 2026-11-08) | Teams that want pipeline orchestration over their existing cloud stack, open source first |
| 8 | Comet | MLOps Pro is $19 per user per month for up to 10 users; the billing period toggle is not shown in the page text read (read 2026-11-08) | Small teams that want experiment tracking and a registry with a free single-user plan |
| 9 | Weights & Biases Models | Pro starts at $60 per month, billed monthly, with a 30 day free trial; Enterprise is custom (read 2026-11-08) | Teams that prioritise experiment tracking and dashboards with a free tier |
| 10 | Kubeflow | No price: Kubeflow is described as an open-source AI platform and no paid plan is listed (read 2026-11-08) | Platform teams that run Kubernetes and want to assemble their own open-source ML stack |
Pick by what you want
Best overall
ClearML
Best value
ClearML
Best for AWS teams
Amazon SageMaker AI
Best for the open standard
MLflow
Best for Kubernetes platform teams
Kubeflow
The ranking
1
ClearML
Answrs VerdictAll five lifecycle areas named, a $0 Community plan, open-source self-hosting and a $15 per user Pro plan.
Price: Pro is $15 per user per month plus usage; Community is $0; Scale and Enterprise are custom quotes. Example usage rate: $0.1 per 1GB of artifact storage (read 2026-11-08)Lifecycle coverage: 5 of 5 areas named: experiment management, model repository, serving (beta), pipelines and model endpoint monitoringDeployment and monitoring: The comparison table lists Serving as beta in every tier and Model Endpoint Monitoring as metrics only, in the Scale and Enterprise tiersFree plan: Community is $0 for teams up to 3, with no credit card, 100GB free artifact storage and 1M API calls a monthOpen source: The pricing page says you can run self-hosted ClearML 100% open source from GitHub
ClearML’s pricing page lists experiment management, a model repository, pipelines, serving (beta) and model endpoint monitoring. Community is $0 for teams up to 3, and Pro is $15 per user per month plus usage. It says you can run self-hosted ClearML 100% open source.
Go ifTeams that want tracking, a model repository, pipelines and serving in one tool that can be self-hosted for free
Skip ifYou need endpoint monitoring beyond metrics, which is listed only on the higher tiers
Why #1 over #2: It names all 5 areas and is the only top-scoring pick with both open-source self-hosting and a $0 plan.
2
Amazon SageMaker AI
Answrs VerdictAll five areas named on AWS own pages, billed by usage, with a time-limited free tier.
Price: Usage-based: you pay for the underlying compute and storage. Example rate: a pricing example lists ml.c5.xlarge at $0.204 per hour (read 2026-11-08)Lifecycle coverage: 5 of 5 areas named: MLflow experiment tracking, Model Registry, deployment, Pipelines and Model MonitorRegistry and pipelines: AWS says SageMaker Model Registry tracks model versions and logs approval workflows, and SageMaker Pipelines automates the end-to-end ML workflow including deploymentFree tier: The AWS Free Tier for SageMaker AI runs for the first 2 months and lists 250 hours of ml.t3.medium on Studio notebooksHosting: A fully managed AWS service; no open-source licence or self-host option is stated for SageMaker on the pages read
AWS names MLflow tracking, Model Registry, Pipelines, deployment and Model Monitor. Billing is usage-based, with a pricing example listing ml.c5.xlarge at $0.204 per hour, and a free tier for the first 2 months.
Go ifTeams already on AWS who want managed tracking, registry, pipelines and monitoring on pay-as-you-use compute
Skip ifYou want one flat monthly price: billing is per instance hour and per resource
Why #2 over #3: It also names all 5 areas, but it states no open-source option and no permanent free plan.
3
Databricks Managed MLflow
Answrs VerdictManaged MLflow with registry, deployment and monitoring, billed per second by usage.
Price: Pay as you go with a 14-day free trial; Model Serving is metered in DBUs, for example 10.48 DBUs per hour for a T4 GPU. The dollar price per DBU is not shown in the page text read (read 2026-11-08)Lifecycle coverage: 5 of 5 areas named: experiment tracking, MLflow Model Registry, low-latency real-time deployment, Workflows integration and real-time monitoringRegistry: Databricks lists MLflow Model Registry with model versioning, role-based approval workflows and CI/CD integrationsBilling basis: Databricks says it uses a pay-as-you-go approach with no up-front costs, billed per secondOpen source: Databricks says Managed MLflow is built on open-source MLflow and lists the MLflow tracking server as self-hosted or fully managed
Databricks lists experiment tracking, MLflow Model Registry, real-time deployment, Workflows integration and real-time monitoring. It is pay as you go with a 14-day free trial, and its GPU Model Serving example is 10.48 DBUs per hour.
Go ifTeams that already run data and ML on Databricks and want MLflow managed there
Skip ifYou want a published dollar price: the DBU dollar rate was not shown on the pages read
Why #3 over #4: It names all 5 areas, but its dollar price per DBU was not shown and no permanent $0 plan is stated.
4
MLflow
Answrs VerdictThe free, Apache 2.0 open-source standard for tracking, registry and deployment.
Price: No paid plan: MLflow says it is 100% open source under the Apache 2.0 license and forever free (read 2026-11-08)Lifecycle coverage: 4 of 5 areas named: experiment tracking, model registry, model deployment tools and monitoring; pipelines are not namedTracking and registry: MLflow lists experiment tracking, model evaluation, a production model registry and model deployment tools for ML modelsFree plan: Free to use with no strings attached; hosting is your own or a vendor that offers managed MLflowOpen source: MLflow says it is backed by the Linux Foundation and licensed under Apache 2.0
MLflow is 100% open source under Apache 2.0, backed by the Linux Foundation, and lists experiment tracking, a model registry, deployment tools and monitoring. There is no paid plan.
Go ifTeams that want the open standard for tracking and registry and will host it themselves or buy it managed
Skip ifYou want a built-in pipeline orchestrator: pipelines are not named on the page read
Why #4 over #5: It names 4 areas, one fewer than the top three, because pipelines are not named.
5
Azure Machine Learning
Answrs VerdictNo service fee on top of compute, with pipelines and managed endpoints, billed through Azure.
Price: Microsoft says there is no additional charge to use Azure Machine Learning; you pay for compute and other Azure services. Example rate: $1.196 per hour per DS14 v2 VM, with a $0 per core Azure Machine Learning charge (read 2026-11-08)Lifecycle coverage: 4 of 5 areas named: MLflow integration, managed endpoints, pipelines and monitoring and auditing features; a model registry is not named on the pages readDeployment and pipelines: Microsoft lists managed endpoints for model deployment and scoring, ML pipeline scheduling, and CI/CD with GitHub Actions or Azure DevOpsFree trial: The pricing page offers free cloud services and a $200 credit to explore Azure for 30 daysHosting: A managed Azure service; no open-source licence is stated for the service on the pages read
Microsoft says there is no additional charge for Azure Machine Learning itself, with an example of $1.196 per hour for a DS14 v2 VM. It lists MLflow integration, managed endpoints, pipelines and monitoring features.
Go ifTeams on Azure who want pipelines, managed endpoints and CI/CD tied into Microsoft tools
Skip ifYou need a named model registry or experiment tracker on the page: neither is named there
Why #5 over #6: It names 4 areas with no open-source statement, so it sits behind MLflow and ahead of Google on name order.
6
Google Vertex AI
Answrs VerdictRegistry, pipelines, prediction and monitoring on usage billing, now branded Gemini platform.
Price: Usage-based. Example rates: $0.03 per pipeline run and $3.50 per GB analyzed by Model Monitoring; the pricing page is titled Gemini platform pricing (read 2026-11-08)Lifecycle coverage: 4 of 5 areas named: Model Registry, batch and online prediction, Pipelines and monitoring for input skew and drift; experiment tracking is not named as suchRegistry: Google says there is no cost for having models in the Model Registry; cost comes from deploying to an endpoint or running a predictionFree credits: New customers get $300 in free credits to try the platform and other Google Cloud productsNaming: The pages at the vertex-ai address now call the product Gemini platform and say it supersedes the legacy AI Platform and AutoML products
Google lists Model Registry, prediction, Pipelines and monitoring, and prices by use, for example $0.03 per pipeline run. New customers get $300 in credits. Its pages call the product Gemini platform.
Go ifTeams on Google Cloud who want registry, pipelines and monitoring next to BigQuery
Skip ifYou want a flat monthly price: each service is billed by use
Why #6 over #7: It names 4 areas with no open-source statement, and sits behind Azure Machine Learning on name order.
7
ZenML Pipelines
Answrs VerdictOpen-source pipelines and registry with a flat $39 a month Starter plan.
Price: Starter is $39 a month with unlimited seats, 1 ML project and up to 100 pipeline runs a month; Enterprise is custom (read 2026-11-08)Lifecycle coverage: 3 of 5 areas named: model registry, pipeline orchestration and run monitoring for failures and drift; no tracking or serving is namedPipelines: ZenML lists pipeline DAGs, an artifact store and a model registry, running on Kubernetes, Vertex, SageMaker or AzureMLFree plan: Open Source is free with unlimited pipeline runs, self-hosted; the paid plans have a 14-day trial with no credit cardOpen source: The pricing table has an Open Source plan; ZenML says open source stays free, forever
ZenML lists pipeline DAGs, an artifact store, a model registry and run monitoring. Starter is $39 a month with unlimited seats and 100 pipeline runs, and Open Source is free.
Go ifTeams that want pipeline orchestration over their existing cloud stack, open source first
Skip ifYou want a built-in tracker or serving layer: neither is named on the page read
Why #7 over #8: It names 3 areas and wins the tie with Comet and Weights & Biases because it states open source.
8
Comet
Answrs VerdictTracking and registry with a free plan and a $19 per user Pro plan.
Price: MLOps Pro is $19 per user per month for up to 10 users; the billing period toggle is not shown in the page text read (read 2026-11-08)Lifecycle coverage: 3 of 5 areas named: experiment tracking, model registry and production monitoring on Enterprise; deployment and pipelines are not namedFree plan: MLOps Free is $0 for 1 platform user, with experiment tracking, dataset versioning, a model registry and 100GB of storageMonitoring: Comet says advanced production monitoring is exclusive to EnterpriseHosting: Enterprise lists on-premise deployment; the open-source option on the page is Opik, the separate GenAI tool, not the MLOps platform
Comet MLOps lists experiment tracking and a model registry, with advanced production monitoring on Enterprise. Free is $0 for 1 platform user and Pro is $19 per user per month.
Go ifSmall teams that want experiment tracking and a registry with a free single-user plan
Skip ifYou need monitoring or deployment on a lower tier
Why #8 over #9: It names 3 areas and wins the tie with Weights & Biases on alphabetical order.
9
Weights & Biases Models
Answrs VerdictTracking and registry with a free plan and a $60 monthly Pro start.
Price: Pro starts at $60 per month, billed monthly, with a 30 day free trial; Enterprise is custom (read 2026-11-08)Lifecycle coverage: 3 of 5 areas named: experiment tracking, an AI assets registry and production monitoring under Weave; deployment and pipelines are not namedFree plan: Free is $0 per month with up to 5 model seats and 5 GB per month of storageRegistry: The Free plan lists AI model experiment tracking and AI assets registry and lineage trackingOwnership: The pricing page is branded CoreWeave Forge Pricing and lists Weights & Biases Models under Training; self-hosting is mentioned but an open-source licence is not stated
Weights & Biases Models lists experiment tracking, an AI assets registry and Weave production monitoring. Free is $0 and Pro starts at $60 per month, billed monthly. The pricing page is branded CoreWeave Forge.
Go ifTeams that prioritise experiment tracking and dashboards with a free tier
Skip ifYou need deployment or pipelines from the same vendor
Why #9 over #10: It names 3 areas and no deployment or pipelines, and ranks last of the 3-point picks on name order.
10
Kubeflow
Answrs VerdictFree, Kubernetes-native pipelines and registry, but you assemble and run it yourself.
Price: No price: Kubeflow is described as an open-source AI platform and no paid plan is listed (read 2026-11-08)Lifecycle coverage: 2 of 5 areas named: Kubeflow Hub model registry and Kubeflow Pipelines; tracking, serving and monitoring are not namedPipelines: Kubeflow Pipelines is described as a platform for building and deploying portable, scalable ML workflows using KubernetesRegistry: Kubeflow Hub, formerly Model Registry, indexes and manages models, versions and ML artifact metadataStatus: The page says Kubeflow is now a CNCF graduated project
Kubeflow offers Kubeflow Pipelines and Kubeflow Hub, formerly Model Registry, on Kubernetes. It is open source and a CNCF graduated project, with no paid plan.
Go ifPlatform teams that run Kubernetes and want to assemble their own open-source ML stack
Skip ifYou want a managed product or a ready tracker and serving layer
Related guides
If you are building the LLM side of your stack, our guides cover prompt management and GPU scheduling.
- What are the best prompt management tools in 2027? · Versioning and testing prompts
- What are the best GPU orchestration tools for Kubernetes in 2027? · Scheduling GPU workloads on Kubernetes
How we ranked these
We gave each platform one point for each of five lifecycle areas its own pages name: experiment tracking, model registry, deployment or serving, pipelines, and monitoring. Ties go to a stated open-source licence or self-hosting option, then a $0 plan, then alphabetical order. Best value is the highest-scoring pick that states both an open-source option and a $0 plan with no time limit; that is ClearML. All facts come from each maker’s own pages, read on 2026-11-08. The chart compares one metric: lifecycle areas named, out of 5, for all 10 picks, with none left off. We did not chart price because only ZenML and Weights & Biases list a flat monthly price; ClearML and Comet price per user, and the cloud platforms and Databricks bill by usage. Neptune’s site could not be read because of a certificate error, BentoML returned a bad gateway error, and Modal, Valohai, DataRobot, Domino Data Lab, H2O.ai, Iguazio and Seldon were not read, so none was ranked. Facts are taken from each maker's own published information; check the official site before you buy.
Questions people ask
What is the best MLOps platform?
ClearML ranks first on our rule: its own pages name all 5 lifecycle areas, it has a $0 Community plan and open-source self-hosting, and Pro is $15 per user per month plus usage. Amazon SageMaker AI and Databricks Managed MLflow also name all 5.
Which MLOps platform is the best value?
ClearML. Best value is the highest-scoring pick that states both an open-source option and a $0 plan with no time limit. ClearML scores 5 and MLflow scores 4, and ClearML’s Pro plan is $15 per user per month.
Which MLOps platforms are open source?
ClearML, MLflow, ZenML and Kubeflow state open-source status on their own pages. Databricks says its managed MLflow is built on open-source MLflow. SageMaker, Azure Machine Learning, Vertex AI, Comet and Weights & Biases do not state an open-source licence for the platform on the pages read.
How are the cloud platforms priced?
Usage-based. SageMaker lists ml.c5.xlarge at $0.204 per hour in a pricing example, Azure lists $1.196 per hour for a DS14 v2 VM with a $0 per core Azure Machine Learning charge, Vertex lists $0.03 per pipeline run, and Databricks lists 10.48 DBUs per hour for a T4 GPU.
Lifecycle areas named on the maker’s own pages, out of 5 (experiment tracking, model registry, deployment or serving, pipelines, monitoring) at a glance
Kubeflow2 areas named of 5
ZenML Pipelines3 areas named of 5
Comet3 areas named of 5
Weights & Biases Models3 areas named of 5
MLflow4 areas named of 5
Azure Machine Learning4 areas named of 5
Google Vertex AI4 areas named of 5
ClearML5 areas named of 5
Amazon SageMaker AI5 areas named of 5
Databricks Managed MLflow5 areas named of 5
Official sites
- ClearML · clear.ml
- Amazon SageMaker AI · aws.amazon.com
- Databricks Managed MLflow · databricks.com
- MLflow · mlflow.org
- Azure Machine Learning · azure.microsoft.com
- Google Vertex AI · cloud.google.com
- ZenML Pipelines · zenml.io
- Comet · comet.com
- Weights & Biases Models · wandb.ai
- Kubeflow · kubeflow.org
Sources
- clear.ml
- aws.amazon.com
- databricks.com
- databricks.com
- azure.microsoft.com
- learn.microsoft.com
- cloud.google.com
Prices, specs and availability change; confirm on the official site before you buy.