Datatron MLOps Platform vs. Paperspace

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
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
Paperspace
Score 0.0 out of 10
N/A
Paperspace, now part of DigitalOcean, is a cloud computing and ML development platform for building, training and deploying machine learning models and AI applications. Paperspace can be used to iterate faster and collaborate on intelligent, real-time prediction engines.
$268
per month
Pricing
Datatron MLOps PlatformPaperspace
Editions & Modules
No answers on this topic
M4000
$268
per month
P4000
$303
per month
RTX4000
$337
per month
P5000
$461
per month
P6000
$647
per month
V100
$1,348
per month
Offerings
Pricing Offerings
Datatron MLOps PlatformPaperspace
Free Trial
YesYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Best Alternatives
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Small Businesses
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Medium-sized Companies
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Score 8.5 out of 10
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Score 8.5 out of 10
Enterprises
Dataiku
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Score 8.6 out of 10
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Score 8.6 out of 10
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User Testimonials
Datatron MLOps PlatformPaperspace
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