BentoML vs. Ray Comparison


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About 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.	About Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.
Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Companies, professionals and developers in search of a solution to simplify model deployment	Audience ML and AI Engineers, Software Developers
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing Free Free Version Supported Free Trial Not Supported	Pricing Free Open source. Consumption-based. Free Version Supported Free Trial Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software

Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Supported Live Online Supported In Person Supported
Company Information BentoML United States www.bentoml.com	Company Information Anyscale Founded: 2019 United States ray.io
Alternatives Domino Enterprise AI Platform Domino Data Lab	Alternatives Anyscale
NVIDIA Triton Inference Server NVIDIA	Horovod
Azure Machine Learning Microsoft	Determined AI
Huawei Cloud ModelArts Huawei Cloud	Neuralhub
Amazon SageMaker Amazon View All	Automaton AI View All
Categories AI Development Supported AI Infrastructure Supported Machine Learning Supported ML Model Deployment Supported	Categories Deep Learning Supported Machine Learning Supported ML Model Deployment Supported

Integrations Amazon SageMaker Supported Amazon Web Services (AWS) Supported Apache Airflow Supported Kubernetes Supported PyTorch Supported TensorFlow Supported Apache Spark Supported Azure Container Registry Supported Azure Kubernetes Service (AKS) Not Supported Dask Not Supported Feast Not Supported Google Cloud Platform Not Supported H2O.ai Supported Keras Supported MLflow Not Supported Prometheus Supported Python Not Supported Snowflake Not Supported ZenML Supported io.net Not Supported Show More Integrations View All 23 Integrations	Integrations Amazon SageMaker Supported Amazon Web Services (AWS) Supported Apache Airflow Supported Kubernetes Supported PyTorch Supported TensorFlow Supported Apache Spark Not Supported Azure Container Registry Not Supported Azure Kubernetes Service (AKS) Supported Dask Supported Feast Supported Google Cloud Platform Supported H2O.ai Not Supported Keras Not Supported MLflow Supported Prometheus Not Supported Python Supported Snowflake Supported ZenML Not Supported io.net Supported Show More Integrations View All 22 Integrations
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