Azure Databricks vs. Vertex AI

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Azure Databricks
Score 8.5 out of 10
N/A
Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. The solution includes autoscaling and auto-termination to improve…N/A
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
Azure DatabricksVertex AI
Editions & Modules
No answers on this topic
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
Azure DatabricksVertex AI
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsPricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
More Pricing Information
Features
Azure DatabricksVertex AI
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Azure Databricks
7.3
4 Ratings
13% below category average
Vertex AI
-
Ratings
Connect to Multiple Data Sources6.14 Ratings00 Ratings
Extend Existing Data Sources7.84 Ratings00 Ratings
Automatic Data Format Detection7.54 Ratings00 Ratings
MDM Integration8.03 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Azure Databricks
6.8
4 Ratings
21% below category average
Vertex AI
-
Ratings
Visualization6.04 Ratings00 Ratings
Interactive Data Analysis7.53 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Azure Databricks
8.6
4 Ratings
5% above category average
Vertex AI
-
Ratings
Interactive Data Cleaning and Enrichment8.24 Ratings00 Ratings
Data Transformations9.04 Ratings00 Ratings
Data Encryption9.44 Ratings00 Ratings
Built-in Processors7.84 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Azure Databricks
8.0
4 Ratings
5% below category average
Vertex AI
-
Ratings
Multiple Model Development Languages and Tools6.54 Ratings00 Ratings
Automated Machine Learning8.64 Ratings00 Ratings
Single platform for multiple model development8.44 Ratings00 Ratings
Self-Service Model Delivery8.44 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Azure Databricks
8.3
4 Ratings
2% below category average
Vertex AI
-
Ratings
Flexible Model Publishing Options8.04 Ratings00 Ratings
Security, Governance, and Cost Controls8.64 Ratings00 Ratings
AI Development
Comparison of AI Development features of Product A and Product B
Azure Databricks
-
Ratings
Vertex AI
9.0
1 Ratings
22% above category average
Machine learning frameworks00 Ratings9.11 Ratings
Data management00 Ratings9.11 Ratings
Data monitoring and version control00 Ratings9.11 Ratings
Automated model training00 Ratings9.11 Ratings
Managed scaling00 Ratings9.11 Ratings
Model deployment00 Ratings8.21 Ratings
Security and compliance00 Ratings9.11 Ratings
Best Alternatives
Azure DatabricksVertex AI
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Jupyter Notebook
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Score 8.6 out of 10
InterSystems IRIS
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Score 7.9 out of 10
Medium-sized Companies
Posit
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Score 10.0 out of 10
InterSystems IRIS
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Score 7.9 out of 10
Enterprises
Posit
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Score 10.0 out of 10
Dataiku
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Score 8.5 out of 10
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User Ratings
Azure DatabricksVertex AI
Likelihood to Recommend
9.7
(3 ratings)
7.7
(13 ratings)
Usability
8.0
(1 ratings)
-
(0 ratings)
Performance
-
(0 ratings)
7.0
(10 ratings)
Configurability
-
(0 ratings)
7.2
(10 ratings)
User Testimonials
Azure DatabricksVertex AI
Likelihood to Recommend
Microsoft
Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.
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Google
In my regular activity, Vertex AI is missing some of the True Positive Alerts due to the ML training and needs to train more data sets, after it has reduced the false positives. To find the Zero day Vulnerability it has low accuracy and sometimes it misses the true positives. Once we have trained with the large data set, it came up with good results.
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Pros
Microsoft
  • Data Processing and Transformations based on Spark
  • Delta Lakehouse when clubbed with an external cloud storage
  • Governance using Unity Catalog to unify IAM
  • Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.
Read full review
Google
  • 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
Read full review
Cons
Microsoft
  • Intuitive interface
  • Ease of use
  • Providing FAQ or QRGs
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Google
  • Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
  • Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
  • Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users
Read full review
Usability
Microsoft
The developers are able to switch between Python and SQL in the Notebook which allows the collaboration of SQL analyst and Data scientist. The integration of Mosaic AI allows users to write complex codes in natural languages. Unity catalog has centralized the security and governance features and simplified the process of maintaining it
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Google
No answers on this topic
Performance
Microsoft
No answers on this topic
Google
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.
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Alternatives Considered
Microsoft
I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.
Read full review
Google
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.
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Return on Investment
Microsoft
  • The support team is amazing, they help you at every stage of the projects, from sales to delivery.
  • On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
  • There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform
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Google
  • It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
  • It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
  • It'll help you in terms of Security wherein we can use row level security access to authorized persons.
Read full review
ScreenShots

Vertex AI Screenshots

Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.Screenshot of gen AI for summarization, classification, and extraction - Text prompts can be created to handle any number of tasks with Vertex AI’s generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI’s PaLM API for text can be used to design prompts with flexibility in terms of their structure and format.Screenshot of Custom ML training overview and documentation - An overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions.Screenshot of ML model training and creation -  A guide that shows how Vertex AI’s AutoML is used to create and train custom machine learning models with minimal effort and machine learning expertise.Screenshot of deployment for batch or online predictions - When using a model to solve a real-world problem, the Vertex AI prediction service can be used for batch and online predictions.