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 8.7 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
8.1
2 Ratings
3% below category average
Vertex AI
-
Ratings
Connect to Multiple Data Sources6.42 Ratings00 Ratings
Extend Existing Data Sources9.02 Ratings00 Ratings
Automatic Data Format Detection9.12 Ratings00 Ratings
MDM Integration8.01 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Azure Databricks
6.2
2 Ratings
30% below category average
Vertex AI
-
Ratings
Visualization5.82 Ratings00 Ratings
Interactive Data Analysis6.72 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Azure Databricks
8.1
2 Ratings
0% below category average
Vertex AI
-
Ratings
Interactive Data Cleaning and Enrichment7.02 Ratings00 Ratings
Data Transformations8.92 Ratings00 Ratings
Data Encryption9.12 Ratings00 Ratings
Built-in Processors7.22 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Azure Databricks
8.3
2 Ratings
1% below category average
Vertex AI
-
Ratings
Multiple Model Development Languages and Tools8.22 Ratings00 Ratings
Automated Machine Learning8.92 Ratings00 Ratings
Single platform for multiple model development8.12 Ratings00 Ratings
Self-Service Model Delivery8.12 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Azure Databricks
8.6
2 Ratings
1% above category average
Vertex AI
-
Ratings
Flexible Model Publishing Options8.02 Ratings00 Ratings
Security, Governance, and Cost Controls9.12 Ratings00 Ratings
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Azure DatabricksVertex AI
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Score 8.6 out of 10
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Medium-sized Companies
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Score 10.0 out of 10
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Score 7.8 out of 10
Enterprises
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Score 10.0 out of 10
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Score 8.2 out of 10
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User Ratings
Azure DatabricksVertex AI
Likelihood to Recommend
9.6
(3 ratings)
7.9
(13 ratings)
Usability
8.0
(1 ratings)
-
(0 ratings)
Performance
-
(0 ratings)
7.1
(10 ratings)
Configurability
-
(0 ratings)
7.2
(10 ratings)
User Testimonials
Azure DatabricksVertex AI
Likelihood to Recommend
Microsoft
Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.
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Google
Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.
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Pros
Microsoft
  • SQL
  • Data management
  • Data access
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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
  • Their pipeline workflow orchestration is pretty primitive. Lacks some common features
  • Workspace UI and navigation requires steep learning curve
  • Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts
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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
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Usability
Microsoft
Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all! Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!
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Google
No answers on this topic
Performance
Microsoft
No answers on this topic
Google
It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.
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Alternatives Considered
Microsoft
Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse
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Google
Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.
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Return on Investment
Microsoft
  • Helped reduce time for collecting data
  • Reduced cost in maintaining multiple data sources
  • Access for multiple users and management of users/data in a single 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.