BentoML is an open-source platform for AI application developers, providing a unified framework to build, ship, and scale production-ready AI services with any model from any framework.
BentoML is a specialized framework designed to streamline the process of moving machine learning models from development to production. It enables data scientists and ML engineers to package trained models from any major framework—like PyTorch, TensorFlow, or Scikit-learn—into a standardized format called a 'Bento'. This Bento contains the model, its dependencies, and serving logic, which can then be deployed as a high-performance API endpoint. The platform's core value is abstracting away complex MLOps infrastructure, allowing teams to achieve scalable and reliable model serving on various targets like Docker, Kubernetes, or serverless platforms. BentoML primarily serves ML engineers, data scientists, and AI application developers who need a systematic, code-first approach to operationalize AI models without a heavy DevOps burden.
ML Engineers, Data Scientists, AI Application Developers, and DevOps/Platform Engineers responsible for deploying and managing machine learning models in production.
Based on 0 reviews
2019
San Francisco, USA
Community (Open Source)
The self-hosted, open-source framework with unlimited usage. Requires you to manage your own infrastructure for deployment and scaling.
Free
Solo (BentoCloud)
For individual developers and hobbyists. Includes 1 user, 1 concurrent endpoint, 2 vCPU cores, and 4Gi RAM on the managed cloud platform.
Free
Starter (BentoCloud)
For small teams starting to build AI applications. Includes up to 5 users, 2 concurrent endpoints, 4 vCPU cores, 8Gi RAM, and team collaboration features.
$29/mo
Growth (BentoCloud)
For growing teams scaling their applications. Includes up to 10 users, 4 concurrent endpoints, 8 vCPU cores, 16Gi RAM, and advanced features.
$69/mo
Enterprise (BentoCloud)
For organizations requiring advanced security, support, and custom resource configurations. Includes features like SSO, private networking, and dedicated support.
Custom
Choose Seldon Core if you need advanced Kubernetes-native deployment patterns like multi-armed bandits, explainers, and outlier detectors out-of-the-box.
KServe is a strong alternative if you are heavily invested in the Knative and Kubernetes ecosystems and want a standardized serverless inference solution.
You might prefer MLflow if you need a single platform to manage the entire ML lifecycle, including experiment tracking and model registry, not just serving.
These are ideal if your organization is exclusively committed to a single framework (PyTorch or TensorFlow) and you prefer a first-party serving solution.
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