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BentoML vs. Datatron MLOps Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
BentoML
Score 0.0 out of 10
N/A
BentoML is an open-source platform for application developers used to build, ship, and scale AI applications. It supports model serving, application packaging, and production deployment, and is free and open source under an Apache 2.0 license. And the BentoCloud version is a fully managed platform for building and operating AI applications, bringing agile product delivery to AI teams.N/A
Datatron MLOps Platform
Score 0.0 out of 10
Enterprise companies (1,001+ employees)
Datatron is an MLOps platform that helps businesses deploy, catalog, manage, monitor, & govern ML models in production (on-prem, in any cloud, or integrated feature-by-feature via our API). Datatron is vendor, library, and framework agnostic and supports models built on any stack, including AWS, Azure, GCP, SAS, H2O, Python, R, Scikit-Learn, and Tensor-Flow. Whether users are just getting started in MLOps, or want to remedy or supplement a homegrown solution, Datatron…N/A
Pricing
BentoMLDatatron MLOps Platform
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
BentoMLDatatron MLOps Platform
Free Trial
YesYes
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
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Small Businesses
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Medium-sized Companies
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Enterprises
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User Testimonials
BentoMLDatatron MLOps Platform
ScreenShots

Datatron MLOps Platform Screenshots

Screenshot of "AI Health" Dashboard - See the health of your program in a single pane of glass, including drift, bias, and custom performance metricsScreenshot of Monitor for bias and drift, and data scientists can use Datatron as a starting point to investigate issue root causeScreenshot of Datatron's patented static endpoint allows endless configurations in the gateway, including a/b testing, shadow mode, and canary modeScreenshot of Both real-time inferencing and offline batch jobs can be configured and deployed in the ML gateway.Screenshot of Simplified Kubernetes Management - Provision environments, create clusters, and manage Kubernetes in just a few clicksScreenshot of JupyterHub Integration - Upload, download, register, share, and deploy models right from within your Notebook