The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Vertex AI
Score 8.8 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
Dataiku
Vertex AI
Editions & Modules
Discover
Contact sales team
Business
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Enterprise
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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
Dataiku
Vertex AI
Free Trial
Yes
Yes
Free/Freemium Version
Yes
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.
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
In terms of building Gen AI products, Vertex AI is a solid platform. For example, in an ATS, Gen AI features such as Notes, Call Logs, or Email Summarization, generating Job Descriptions, generating Candidate Summaries, Targeted Email Campaigns, etc.
I do wish that Vertex AI has something like an index of technical terms, so people can learn to use it faster instead of seeing 1 new & strange term at a time
As an organization, we have our extensive practices with DevOps, together with the familiar tools & pipelines, Vertex AI's having its own set of tools doesn't help with making the learning & integrating curves less steep
Similar to lots of other AI offerings, Vertex AI docs tend to have lots of buzzwords yet few technical details, making it seems like reading ads instead of technical docs
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
Vertex AI handles things efficiently. Whatever solution you make is very scalable with such tech, only thing to lookout would be, easy to integrate,easy to use, documentation is pretty good that's the best part
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
I have used OpenAI for their LLM and Vector Embedding service, they are really good at it. But Vertex AI has other better services like training pipeline , depolyment creation etc.