Azure ML vs Databricks: Which is Better for Your Data Needs?

Data scientists view both Azure ML and Databricks as top software picks because both solutions offer comprehensive cloud-based machine learning and data platforms. However, their key differences distinguish them, making each a better choice for specific use cases. Databricks is primarily a data intelligence platform for big data processing and analysis that also specializes in data warehousing, business intelligence, and AI. Azure ML is designed to help with machine learning lifecycle management (MLOps) and provides advanced tools for machine learning project tracking, developer productivity, and autoML. The choice boils down to the specific machine learning and data needs of the environment:

Azure ML: Best for machine learning lifecycle management

Databricks: Best for big data processing and analytics

Azure ML vs Databricks at a Glance

The following table shows, at a high level, how these two tools compare in pricing, core features, ease of use, and ease of implementation. Read on for more detailed reviews of each, or skip ahead for alternatives.

Azure MLDatabricksPricingPay-as-you-goDiscounts for usage commitmentsPay-as-you-goDiscounts for usage commitmentsCore Features• AutoML• Prompt Flow• Responsible AI• Managed endpoints• Data preparation• Experiment tracking• Distributed training• AI development tools• Lakehouse architecture with Apache Spark• AI-powered business intelligence• AI developer assistant• Natural language data querying• Data governance• ETL, data warehousing, real-time streamingEase of UseModerate learning curveSteep learning curveImplementationMore out-of-the-box in designDifficult for beginners

TABLE OF CONTENTS
ToggleAzure ML vs Databricks at a GlanceWhat is Azure ML?Key Features of Azure MLWhat is Databricks?Key Features of DatabricksBest for Pricing: Azure MLBest for Core Features: Toss UpBest for Implementation and Ease of Use: Azure MLBest for Integration: DatabricksWhy Shouldn’t You Use Azure ML or Databricks?Alternatives To Databricks and Azure ML Frequently Asked QuestionsBottom Line: Azure ML and Databricks Both Offer Leading Machine Learning Solutions

What is Azure ML?

Developed by Microsoft, Azure ML is a cloud-based machine learning (ML) platform that helps teams manage the entire lifecycle of machine learning models and AI apps, from data prep and development to ongoing maintenance, in a secure, auditable space. The platform’s main users include data scientists, ML engineers, and MLOps specialists.

Within Azure, they can use various tools to automate their machine learning workflows, such as Prompt Flow, a tool for streamlining the development of AI apps built on large language models (LLMs). Whether a business wants to build a generative AI tool or improve its MLOps, Azure ML can help accomplish these goals quickly and efficiently.

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Key Features of Azure ML

A multifaceted data solution, Azure ML offers an app-building tool, an AI dashboard that supports ethical best practices, automated machine learning, and managed endpoint functionality.

Prompt Flow 

Prompt Flow is Azure’s development tool for quickly and effectively designing, experimenting, refining, and deploying LLM-powered AI apps. It offers team collaboration functionality for sharing and debugging flows, as well as large-scale testing and evaluation tools to test out prompt variants. It also includes a library of templates and examples that serve as a foundation for app development.

Using Prompt Flow, users can quickly execute each step of the ML process.

Responsible AI Dashboard

Azure offers tools to reduce AI risk, boost model accuracy, enforce transparency, and safeguard data privacy. For example, you can assess model fairness and bias to produce safe, ethical AI applications. Azure AI Content Safety will automatically monitor text and images for offensive content. It can also conduct error analyses.

Azure ML’s error analysis tool will identify error coverage, success instances, and failure patterns.

Automated Machine Learning

AutoML offers tools to automate the iterative tasks in the ML development process. For instance, during model training, AutoML creates numerous parallel pipelines that monitor various parameters so you don’t have to. It’s ideal for rapidly creating ML models that can handle tasks like classification, regression, vision, and natural language processing (NLP).

AutoML speeds up the ML model design and training process without requiring any new software code.

Managed Endpoints

Azure ML allows users to operationalize model deployment across CPU and GPU machines, which is a helpful choice given the cost difference between these two processor chips. Additionally, it enables task completion using serverless, online, or batch endpoints. The platform also allows fast and efficient log metrics and scoring management.

Pros 

User-friendly interface with low- and no-code development tools

Automated ML functionality

Enterprise-grade security features

Cons

Steep learning curve for advanced features

Can struggle with complex data tasks and structures

Price can rise dramatically for usage-intensive projects

What is Databricks?

Databricks is a unified, cloud-based data intelligence platform that helps data scientists and engineers streamline their big data workflows—from extract, transform, and load (ETL) tasks and data warehousing to data governance and business intelligence—in a secure, streamlined manner. A one-stop-shop for data management needs, it provides tools for consolidating real-time and batched data from various sources, data transformation, data querying, data analysis, and reporting.

It also offers MLOps tools that assist in using data to create generative AI tools and machine learning models. Powered by generative AI, Databricks enables all employees, regardless of technical know-how, to uncover insights from company data using context-aware, natural language search.

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Key Features of Databricks

A data platform well known for its innovative approach, Databricks offers AI development tools, lakehouse architecture, a unified workspace, and AI-based business intelligence.

AI Development

Databricks supports building and deploying generative AI models using data while maintaining control and privacy throughout the process. The platform also offers tools for automating experimentation, governance, and other aspects of AI development. Lakehouse monitoring, for example, tracks and assesses features, AI models, and data in one place, empowering tasks like spot outputs for offensive content or AI errors.

Databricks’ lakehouse monitoring offers a dashboard to track key data metrics and statistical properties.

Lakehouse Architecture

Built atop Apache Spark, a leading solution for big data processing and distributed computing, Databricks’ lakehouse architecture combines the best of a data warehouse and a data lake. This combination enables efficient data integration, processing, and storage, as well as effective querying on various types of data. This efficiency makes data management highly scalable, so pricing for cloud resources doesn’t rise too high as data demands grow.

The data lakehouse can use structured, semi-structured, and unstructured data to conduct BI, reporting, data science, and machine learning.

Unified Workspace

Databricks includes a centralized, collaborative workspace for data engineering, data science, AI development, and data visualization. This collaborative workspace is a core part of the platform’s functionality in that it facilitates group projects by data professionals. The platform also supports large-scale, rapid data processing with Apache Spark and other machine learning workflows.

AI-First Business Intelligence

Designed to make everyone on your team an analyst, Databricks’ business intelligence understands your unique company data and, through its Genie feature, allows business teams to ask questions about that data using a chatbot interface without using any code or relying on data specialists.

Users can ask questions about company data using Databricks’ NLP-powered AI assistant.

Pros

Multilevel data security

Data lakehouse architecture

Rapid data processing of complex data

Cons

Difficult to implement, even for tech pros

Steep learning curve

Expensive for large-scale projects

Best for Pricing: Azure ML

Azure ML is slightly more affordable and offers a 30-day free trial, two weeks longer than Databricks’ free offer. Because of the complexity of these two platforms’ pricing structures, it’s nearly impossible to declare a decisive winner.

The differences in pricing between Databricks and Azure ML are exceptionally complex and situation-dependent, so businesses need to price each project to truly compare. At a high level, Databricks offers pay-as-you-go pricing with no upfront costs. Depending on the product you’re using, you’ll pay a specific price per Databricks Unit (DBU). Databricks also offers Committed Use Contracts, which allows businesses to gain discounts when agreeing to certain usage levels. The solution offers a 14-day free trial. Note that although the trial is free, users are still charged by their cloud provider for resources used in the platform.

Azure ML also offers pay-as-you-go pricing and committed use discounts, which are great for organizations with predictable long-term workloads. The pay-as-you-go pricing ranges depend on the use case, RAM, and other factors. For example, monthly fees for processing with GPUs can range anywhere from $650 to around $3,000. Businesses can save money by signing longer-term contracts. A key perk of Azure’s pricing is that users gain access to its free services, such as AI Search and Azure SQL Database. First-time Azure users also temporarily gain free access to other services, like Azure Virtual Machines—both free for 12 months.

Users are advised to assess the resources they expect to need to support their forecast data volume, processing amount, and analysis requirements. Databricks may be cheaper for some users, but Azure ML will probably be cheaper for most.

Best for Core Features: Toss Up

For those needing robust ELT, data science, and machine learning features within a data lake/data warehouse framework, Databricks is the winner. Azure ML wins for those just wanting to add ML to existing applications.

Azure ML helps data scientists and developers quickly build, deploy, and manage ML and AI models via machine learning operations (MLOps), open-source interoperability, and integrated tools. It streamlines the deployment and management of thousands of models in multiple environments for batch and real-time predictions.

Repeatable pipelines automate workflows for continuous integration and continuous delivery (CI/CD). Developers can use registries for cross-workspace collaboration. Azure ML also offers continuous monitoring of model performance metrics and the detection of data drift, and it can trigger retraining to improve model performance. Azure ML includes features to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis.

Like Azure ML, Databricks is cloud-based. Its management layer is built around Apache Spark’s distributed computing framework, which enables more efficient infrastructure management. Apache also runs faster on Databricks than anywhere else (after all, the founders of Databricks created Apache Spark). It uses a batch in-stream data processing engine for distribution across multiple nodes. Databricks positions itself as a data lake more than a pure ML system, but it incorporates heavy-duty ML capabilities. The emphasis is on use cases such as streaming, ETL, and data science-based analytics/ML. The platform is effective at handling raw, unprocessed data in large volumes.

Databricks is a software as a service (SaaS) solution that can run on all major cloud platforms; an Azure Databricks combination is available. Databricks includes a data plane as well as a control plane for back-end services that deliver instant compute. Its query engine is known to offer high performance via a caching layer. Databricks provides storage by running on top of AWS S3, Azure Data Lake Storage G2, and Google Cloud Storage.

Databricks recently added AI-first business intelligence. This feature enables team members to ask questions about company data in a conversational format, using text rather than code—e.g., “How did sales do last year compared to 2020?” This allows everyone in the organization to query data and get answers to critical business questions when they need it.

Best for Implementation and Ease of Use: Azure ML

Azure ML wins in terms of overall ease of implementation and use, especially if you’re currently using Microsoft as your cloud computing platform.

Azure ML comes with a full menu of out-of-the-box features for machine learning and even offers development templates to get started. Despite limiting customization, its pre-built offerings make Azure an easy tool to implement. For ease of use, it enables users to collaborate with Jupyter Notebooks using built-in support for open-source frameworks and libraries. Users can quickly create accurate and automated ML models for tabular, text, and image. And those familiar with SQL and Azure will find it particularly easy to use.

Unlike Databricks, which is geared toward trained data scientists, Azure offers productivity tools for all developer skill levels, from novices to experts. These include code-first tools like Notebooks, low-code options like AutoML, and even a no-code tool called Designer, a drag-and-drop editor for building ML pipelines. It also offers pre-built development templates that users can deploy as a starting point.

Databricks, in contrast, is best for those familiar with Apache and open-source tools. It takes a data science approach using open-source and machine libraries, which may be challenging for some users. It can run Python, SQL, and other platforms, and it comes packaged with a user interface and tools to connect to endpoints such as JDBC connectors. Some users report that its interface is complex and requires more manual input for cluster resizing clusters or configuration updates. There may be a steep learning curve for some users.

Databricks’ AI Assistant has an intuitive user interface, which appears in Notebooks, SQL Editor, and File Editor. Using natural language, developers can ask the AI chatbot questions about their code and data. They can also use it to perform tasks like auto-fixing errors, explaining tricky code, or running SQL queries.

Best for Integration: Databricks

Azure ML is the winner for Microsoft and Azure shops, but for every other integration, Databricks reigns supreme.

Microsoft does a good job connecting its various ecosystems together. Azure ML, Azure Synapse, and other Azure offerings are well integrated. That also applies to Windows and other Microsoft offerings, including Power BI for analytics. It also does a decent job integrating Apache tools, although not as well as Databricks, which is built solidly on an Apache bedrock.

In comparison, Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. It also supports any format of data, including unstructured data, which gives it an edge over Azure ML in that area.

More recently, Databricks added open-source connectors for Go, Node.js, and Python to simplify access from other applications. A Databricks SQL query federation tool can query remote data sources, including PostgreSQL, MySQL, AWS Redshift, Salesforce Data Cloud, and Snowflake, without extracting and loading the data from the source systems.

Why Shouldn’t You Use Azure ML or Databricks?

Despite their robust features and powerful capabilities, these tools aren’t right for every application or use case.

Who Shouldn’t Use Azure ML

Data scientists and developers looking to easily build unique features might struggle with the limits imposed on customization by Azure’s out-of-the-box nature. While templates and pre-built features are great for getting started, they can be limiting if you have a particular design in mind. Businesses can still custom-build these AI features, but it might take a lot of tinkering, and in some cases, it will require hiring a developer with expertise in Azure ML.

Who Shouldn’t Use Databricks

Developers looking for a beginner-friendly ML tool might want to steer clear of Databricks. The platform has a steep learning curve, especially for users with limited experience working with big data technologies. Although it offers drag-and-drop algorithm functionality for building AI models, many users view this tool as one of the platform’s least effective features. The advanced features, in particular, commonly give new users trouble, and the sheer number of features can be overwhelming.

Alternatives To Databricks and Azure ML 

If Databricks and Azure ML don’t fit your requirements, consider Amazon Sagemaker, an industry leader for building machine learning models, and Snowflake, a top provider of data analytics and data processing services. As with Databricks and Azure ML, pricing for both platforms is highly complex and situation-dependent.

Amazon Sagemaker

Amazon Sagemaker, like Azure ML, is a robust cloud-based MLOps platform for building, training, and deploying machine learning models. It’s deeply integrated with the Amazon Web Services (AWS) product ecosystem, making it ideal for AWS users. Users also comment on how Sagemaker has the edge in customization and flexibility. If you’re familiar with the AWS environment, it’ll also be easier to use than Azure ML. Amazon Sagemaker’s pricing is based on usage, so you only pay for what you need. It also offers a free tier and cost reductions for longer-term commitments.

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Snowflake

Like Databricks, Snowflake is a cloud-based big data analytics platform that supports storage, processing, and analysis. Unlike Databricks, it offers more out-of-the-box analytics features and has an easier learning curve. This hinders its flexibility but makes it easier to implement and fully master than Databricks. Snowflake charges a monthly price for data stored on the platform. The starting price is $2 per Snowflake credit.

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Frequently Asked Questions

What Is the Azure Equivalent of Databricks? Azure Synapse Analytics is similar to Databricks in its features for consolidating, processing, and analyzing enterprise data. Like Databricks, it focuses primarily on big data analytics and data warehousing. Is Databricks Good for Machine Learning? Yes, Databricks offers various tools to help ML engineers and data scientists increase productivity throughout the machine learning lifecycle, from data preparation to model training and deployment. It also helps businesses create and deploy LLMs that are customized for controlling and querying. What Is Databricks’ Biggest Competitor? Snowflake is Databricks’ largest competitor, with around 20 percent of the data warehousing market. Compared to Databricks, it lacks the same level of customization but makes up for it in ease of use, offering more out-of-the-box analytics tools.

Bottom Line: Azure ML and Databricks Both Offer Leading Machine Learning Solutions

Azure ML and Databricks are both comprehensive machine learning platforms, each with its own target users. Azure ML, in addition to being easier to use, is best suited for machine learning engineers looking for tools to help them develop, train, and deploy ML models at a rapid pace. Meanwhile, Databricks is focused on serving data scientists who want to store, process, and analyze large amounts of complex, varied data. Overall, the winner depends on an organization’s specific machine learning needs, current tech stack, and the expertise of its developers and data scientists.

Read our guide to the best machine learning platforms for a comprehensive portrait of today’s ML sector.
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