Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom business applications.
$0.04
per CCU (hourly rate)
Vertex AI
Score 8.6 out of 10
N/A
Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.
$0
Starting at
Pricing
Cloudera Data Platform
Vertex AI
Editions & Modules
CDP Public Cloud - Data Hub
$0.04
per CCU (hourly rate)
CDP Public Cloud - Data Warehouse
$0.054
per CCU (hourly rate)
CDP Public Cloud - Data Engineering
$0.07
per CCU (hourly rate)
CDP Public Cloud - Operational Database
$0.08
per CCU (hourly rate)
CDP Public Cloud - Flow Management
$0.15
per CCU (hourly rate)
CDP Public Cloud - Machine Learning
$0.17
per CCU (hourly rate)
CDP Private Cloud - Plus Edition
$400
CCU (annual subscription)
CDP Private Cloud - Base Edition
$10,000.00
node + variable (annual subscription)
Imagen model for image generation
$0.0001
Starting at
Text, chat, and code generation
$0.0001
per 1,000 characters
Text data upload, training, deployment, prediction
$0.05
per hour
Video data training and prediction
$0.462
per node hour
Image data training, deployment, and prediction
$1.375
per node hour
Offerings
Pricing Offerings
Cloudera Data Platform
Vertex AI
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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Pricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
More Pricing Information
Community Pulse
Cloudera Data Platform
Vertex AI
Features
Cloudera Data Platform
Vertex AI
AI Development
Comparison of AI Development features of Product A and Product B
I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.
we used Vertex AI on our automation process the model very useful and working as expected we have implemented in our monitoring phase this very helpful our analysis part. real time response is very effective and actively provide detailed overview about our products.this phase is well suited in our org. this model could not applicable for small level projects why because this model not needed for small level projects and without related resource of ML this model not useful. strictly on non cloud org not suitable means on pram not suitable
Vertex AI comes with support for LOTs of LLMs out of the box
MLOps tools are available that help to standardize operational aspects
Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers
Google is always top notch with their security and user interface performance. We use Google's entire suite in our business anyways, so using Vertex became second nature very quickly. I will say, though, that Google does need to come down on the price somewhat with their token allocation. Also, their UI is very robust, so it does require some time for training to really master it.
We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.
IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.
We tend to adapt and use the platform that suits the customers needs the best. We return to Vertex AI because it is the most in-depth option out there so we can configure it any which way they want. However, it is not quick to market and constantly changing or updating it's feature-set. This makes it suitable for bigger customers that have the capital and time to spend on a bigger project that is well researched and not quick to market like some of the other options that feel like a light-version of this.