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Compare 6 top MLOps platforms

Choosing the right MLOps platform means considering features, pricing and ease of integration into your current machine learning environment. Evaluate six leading options.

Choosing an MLOps platform is no easy task. The process begins with assessing your organization's MLOps maturity and defining key requirements for the platform.

Machine learning operations combines machine learning, DevOps and data engineering to streamline and enhance the lifecycle of ML models. It automates deployment, monitoring and maintenance of models in production, improving operational effectiveness and efficiency.

MLOps helps ML teams reduce the time and resources spent on model deployment and management while improving accuracy and performance. A structured model development framework enables teams to update models in response to data changes, and rigorous monitoring minimizes downtime by flagging potential issues early on.

Implementing an MLOps framework often involves choosing an MLOps platform: a comprehensive tool set designed to automate and manage tasks associated with production ML environments, such as model deployment and monitoring. These platforms help MLOps teams complete their work in a more streamlined, efficient manner. Choosing the right platform involves evaluating each option's features in light of specific organizational requirements.

TechTarget Editorial chose the platforms in this roundup by evaluating vendor content, industry research, trend analysis and vendor demos. This list is not ranked.

6 top MLOps platforms

Here's a breakdown of some of the top MLOps platforms available on the market today, listed in alphabetical order.

Amazon SageMaker

SageMaker is designed to enhance ML workflows, increase productivity and manage costs effectively. Key features include the following:

  • SageMaker Studio, an integrated ML environment that lets users perform all ML development steps in a single web-based visual interface.
  • SageMaker Autopilot, which automates the ML process and enables users with no prior ML experience to build models quickly.
  • SageMaker Debugger, which captures real-time training metrics, offering insights and alerts to improve model accuracy, troubleshoot issues and increase transparency.

SageMaker also provides monitoring and management tools for ongoing training and production data sets and models. Its pricing follows a billing structure based on the services and resources teams use. Consult the SageMaker documentation for more details on how AWS accounts for tool costs.


Databricks is a cloud-native ML and analytics platform built on data lakehouse architecture. It offers a unified set of tools for building, deploying, sharing and maintaining enterprise data applications. Key features include the following:

  • Unified interface, offering one location for data processing scheduling and management; dashboard generation; security management; and data discovery, annotation and exploration.
  • Real-time collaboration across the ML lifecycle.
  • Databricks Lakehouse Monitoring, which enables teams to monitor entire data pipelines.

Databricks' pricing varies based on compute types, pricing tiers, cloud service providers, discounts, commitments and optimization strategies. Use the Databricks pricing calculator to determine precise costs.

DataRobot MLOps

DataRobot MLOps, which automates model deployment, monitoring and governance, is designed for users looking to monitor existing models and manage their production AI lifecycle. Data scientists, data engineers and DevOps teams can use DataRobot MLOps to collaborate throughout the process of bringing their models to production. Key features include the following:

  • Flexibility, giving teams the option to build and run models anywhere, including hybrid and multi-cloud environments.
  • Automated monitoring, streamlining model health maintenance and ML lifecycle management.
  • Embedded governance, ensuring consistency for AI projects across an organization.

Determining pricing for DataRobot's Classic MLOps plan, Pricing 5.0, requires contacting the DataRobot sales team.

Google Cloud Vertex AI

Vertex AI simplifies ML with AutoML and custom model training. It offers a unified environment for model development, integration with Google Cloud services and streamlined deployment of models at scale. Key features include the following:

  • Unified ML workflow, including data ingestion, model training using AutoML and deployment.
  • Versatile data type support, including the ability to handle images, videos and text, and integration with Google Cloud services like BigQuery.
  • Data-to-AI integration via a dashboard that enables access to BigQuery, Dataproc or Spark.
  • Support for open source ML frameworks, including TensorFlow and PyTorch.

Vertex AI's deep integration with Google Cloud services and its emphasis on security are among its top differentiators. Consult the Vertex AI pricing page for details on costs, including generative AI components.


Kubeflow is an open source ML platform for running scalable ML workloads on Kubernetes. Key features include the following:

  • Tools for end-to-end workflows, such as data preprocessing, training, serving and monitoring.
  • Integration with popular ML frameworks, such as TensorFlow and PyTorch.
  • Support for versioning and collaboration, simplifying the deployment and management of ML pipelines on Kubernetes clusters.

Kubeflow stands out for its integration readiness and open source approach, which are likely to be appealing to experienced teams. Although Kubeflow is free, its setup is complex and might require a third-party consultant for organizations that don't have access to significant in-house Kubernetes experience.

Microsoft Azure Machine Learning

Azure ML is an end-to-end cloud platform supporting the full ML lifecycle. Key features include the following:

  • Azure Machine Learning Studio, where users can write and manage ML code with Jupyter notebooks, create models with a no-code designer, and optimize experiments with visualized metrics.
  • MLOps tools to streamline deployments, automate workflows and enable model reproducibility across projects.
  • Infrastructure options, including serverless compute and support for high-performance GPUs and CPUs for ML tasks.
  • Secure collaboration tools to facilitate teamwork across job functions.
  • Support for an open ecosystem, enabling data scientists to bring and operationalize models built with popular frameworks like TensorFlow, PyTorch and Scikit-learn.

Azure ML also offers flexibility in programming languages, supporting Python, R and .NET. Azure Machine Learning's enterprise focus and prebuilt AI services are key differentiators. Consult the Azure pricing calculator to estimate potential costs.

Choosing an MLOps platform

Assessing in-house ML expertise is the first consideration when choosing an MLOps platform. Teams new to machine learning will likely need a user-friendly platform with extensive support, while expert teams might prefer a customizable, open source platform.

Here are some additional areas to consider when choosing an MLOps platform for your organization:

  • Integration. Look for a platform that integrates seamlessly with your existing hybrid infrastructure, data sources, ML frameworks and DevOps environments.
  • Scalability. Evaluate whether the platform's scalability can handle the complexity of your ML models and the data volume that your systems handle.
  • Collaboration. Emphasize collaboration features to ensure that data scientists, DevOps teams and other stakeholders can work together effectively throughout the MLOps lifecycle.
  • Comprehensiveness. Choose a platform that supports the entire ML lifecycle, including data management, model training, deployment, monitoring and updating.

Will Kelly is a freelance writer and content strategist who has written about cloud, DevOps, AI and enterprise mobility.

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