How much does an MLOps platform cost per month?

How much does an MLOps platform cost per month? As of November 2026, an MLOps platform can cost anywhere from $0 to $10,000+ per month, depending on the platform, team size, model complexity, cloud infrastructure, and production workload. For a typical small-to-medium business, a reasonable initial budget is $200–$2,000 per month for a relatively lightweight MLOps setup. The important distinction is that some platforms charge a software subscription, while others charge based on compute,…

1. MLflow — Lowest licensing cost: MLflow – open-source model tracking and registry.
2. Kubeflow
3. Comet — Best for experiment tracking: Comet ML – team collaboration and experiment management.
4. ClearML
5. Amazon SageMaker AI
6. Google Vertex AI
7. Databricks — Best for integrated data and ML: Databricks – unified data engineering and machine learning.
8. AWS — Best for AWS-based businesses: Amazon SageMaker – integrated cloud-based ML lifecycle management.

An MLOps platform can cost anywhere from $0 to $25,000+ per month, depending on whether you self-host, use a managed tool, or outsource operations: Self-hosted/open source: $0 in license fees, but you pay for cloud infrastructure and engineering time.

1. MLflow — MLflow, Kubeflow, and ClearML are examples.
2. Kubeflow — MLflow, Kubeflow, and ClearML are examples.
3. ClearML — MLflow, Kubeflow, and ClearML are examples.

The monthly cost of an MLOps platform varies widely from $0 to thousands of dollars , depending on whether you choose open-source tools, per-user SaaS managed platforms, or enterprise cloud consumption models.[](https://www.respan.ai/articles/mlops-tools-and-platforms)[](https://deploybase.ai/articles/mlops-tools-comparison) The market generally splits into three primary pricing tiers based on how the platform is hosted and managed:[](https://deploybase.ai/articles/mlops-tools-comparison) Open-…

1. MLflow — Examples: MLflow, Kubeflow, ZenML (open-source core), and BentoML.
2. Kubeflow — Examples: MLflow, Kubeflow, ZenML (open-source core), and BentoML.
3. ZenML — Examples: MLflow, Kubeflow, ZenML (open-source core), and BentoML.
4. BentoML — Examples: MLflow, Kubeflow, ZenML (open-source core), and BentoML.
5. Weights & Biases — Examples: Weights & Biases (Pro tiers starting around$6 0/m o), Galileo ($1 5 0/m o for Pro), and specialized LLM monitoring tools.
6. Galileo — Examples: Weights & Biases (Pro tiers starting around$6 0/m o), Galileo ($1 5 0/m o for Pro), and specialized LLM monitoring tools.
7. Databricks — Cost: Variable consumption pricing based on compute hours, Databricks Units (DBUs), or volume of logged traces (e.g.,≈$0.0 0 2 per trace or custom enterprise scaling).
8. Amazon SageMaker AI — Examples: Amazon SageMaker, Azure ML, and Databricks.
9. Microsoft Azure Machine Learning — Examples: Amazon SageMaker, Azure ML, and Databricks.