Best ML Model Deployment Tools for Git
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Compare the Top ML Model Deployment Tools that integrate with Git as of April 2026
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This a list of ML Model Deployment tools that integrate with Git. Use the filters on the left to add additional filters for products that have integrations with Git. View the products that work with Git in the table below.
What are ML Model Deployment Tools for Git?
Machine learning model deployment tools, also known as model serving tools, are platforms and software solutions that facilitate the process of deploying machine learning models into production environments for real-time or batch inference. These tools help automate the integration, scaling, and monitoring of models after they have been trained, enabling them to be used by applications, services, or products. They offer functionalities such as model versioning, API creation, containerization (e.g., Docker), and orchestration (e.g., Kubernetes), ensuring that the models can be deployed, maintained, and updated seamlessly. These tools also monitor model performance over time, helping teams detect model drift and maintain accuracy. Compare and read user reviews of the best ML Model Deployment tools for Git currently available using the table below. This list is updated regularly.
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JFrog
JFrog
Fully automated DevOps platform for distributing trusted software releases from code to production. Onboard DevOps projects with users, resources and permissions for faster deployment frequency. Fearlessly update with proactive identification of open source vulnerabilities and license compliance violations. Achieve zero downtime across your DevOps pipeline with High Availability and active/active clustering for your enterprise. Control your DevOps environment with out-of-the-box native and ecosystem integrations. Enterprise ready with choice of on-prem, cloud, multi-cloud or hybrid deployments that scale as you grow. Ensure speed, reliability and security of IoT software updates and device management at scale. Create new DevOps projects in minutes and easily onboard team members, resources and storage quotas to get coding faster.Starting Price: $98 per month -
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DVC
iterative.ai
Data Version Control (DVC) is an open source version control system tailored for data science and machine learning projects. It offers a Git-like experience to organize data, models, and experiments, enabling users to manage and version images, audio, video, and text files in storage, and to structure their machine learning modeling process into a reproducible workflow. DVC integrates seamlessly with existing software engineering tools, allowing teams to define any aspect of their machine learning projects, data and model versions, pipelines, and experiments, in human-readable metafiles. This approach facilitates the use of best practices and established engineering toolsets, reducing the gap between data science and software engineering. By leveraging Git, DVC enables versioning and sharing of entire machine learning projects, including source code, configurations, parameters, metrics, data assets, and processes, by committing DVC metafiles as placeholders.
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