BentoML VS kubeflow

Compare BentoML vs kubeflow and see what are their differences.

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BentoML kubeflow
16 3
6,416 13,552
3.5% 1.3%
9.8 8.5
8 days ago 6 days ago
Python TypeScript
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

BentoML

Posts with mentions or reviews of BentoML. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-04.

kubeflow

Posts with mentions or reviews of kubeflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-23.
  • Machine Learning Orchestration on Kubernetes using Kubeflow
    5 projects | dev.to | 23 Mar 2021
    If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.

What are some alternatives?

When comparing BentoML and kubeflow you can also consider the following projects:

fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production

seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

kserve - Standardized Serverless ML Inference Platform on Kubernetes

haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.

streamlit - Streamlit — A faster way to build and share data apps.

Flask - The Python micro framework for building web applications.

polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

Poetry - Python packaging and dependency management made easy

metaflow - Build and manage real-life data science projects with ease.