Best ML Model Deployment Tools for Docker
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Compare the Top ML Model Deployment Tools that integrate with Docker as of July 2025
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This a list of ML Model Deployment tools that integrate with Docker. Use the filters on the left to add additional filters for products that have integrations with Docker. View the products that work with Docker in the table below.
What are ML Model Deployment Tools for Docker?
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 Docker currently available using the table below. This list is updated regularly.
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1
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure.Starting Price: $0.40 per hour -
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KServe
KServe
Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.Starting Price: Free -
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BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
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TrueFoundry
TrueFoundry
TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.Starting Price: $5 per month -
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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects.
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