An open-source framework for building, shipping, and scaling production-ready AI applications, standardizing the path from model development to high-performance inference serving in the cloud or on-premises.
BentoML is an MLOps framework designed to bridge the gap between data science and production engineering. It primarily serves ML engineers, data scientists, and DevOps teams who need a standardized, reliable way to deploy machine learning models as scalable applications. The platform allows users to package trained models, application code, and dependencies into a unified format called a 'Bento', which can then be containerized and deployed anywhere. Its unique value proposition lies in its 'model-centric' approach, which simplifies the creation of high-performance API endpoints with features like adaptive batching for improved throughput. By providing both a powerful open-source toolset and a managed cloud platform (BentoCloud), it offers a flexible, end-to-end solution for operationalizing AI.
ML Engineers, Data Scientists, MLOps professionals, and AI application developers looking to streamline model deployment and management.
Based on 0 reviews
2019
San Francisco, USA
Open Source
The core open-source framework. Allows you to build, containerize, and serve ML models on your own infrastructure. Includes all core serving features like adaptive batching and multi-model support.
Free
Cloud Free
Hosted offering for individuals and hobbyists. Includes 1 collaborator seat, 2 core-hours of on-demand runners, and 1 concurrent deployment on a shared cluster.
Free
Cloud Growth
For small teams and startups. Includes 5 collaborator seats, 200 core-hours of on-demand runners, 5 concurrent deployments, and standard support.
$29/mo
Cloud Enterprise
For large organizations requiring advanced security, scalability, and support. Includes SSO, custom roles (RBAC), on-premise/VPC deployment options, and a dedicated success manager.
Custom
Choose MLflow if you need an end-to-end MLOps platform focused on experiment tracking and reproducibility, with simpler serving needs.
A strong alternative if your entire infrastructure is already Kubernetes-native and you need a standardized inference protocol across multiple serving runtimes.
Opt for Seldon Core if your primary requirement is advanced deployment strategies like canary releases, A/B tests, and multi-armed bandits on Kubernetes.
Consider these if you are exclusively using a single framework (PyTorch or TensorFlow) and prefer a solution maintained by the framework's creators.
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