Databricks Vs Fabric: ML Platform Showdown

The Case for a (Truly) Unified Platform 

Now that you’ve gotten an overview of one of the powerful GenAI offerings in Databricks, I will present my feature-by-feature comparison of Azure Databricks and Fabric as data science platforms. You will notice that many capabilities, while possible to implement in Fabric, require external integrations of additional services in order to work. This is a huge issue because without a unified architecture and platform, you cannot effectively scale, reuse, or govern ML and GenAI models throughout their lifecycle. It’s not enough to just build an AI app – you also need standardized ML environments/architectures to maintain consistent design, governance, and deployment in your data science practice across the whole organization. 

Not only do you require a unified ML and GenAI platform that offers the best frameworks & tools, but you need to have fast adoption. Innovation moves extremely fast in the field of AI, and if you don’t keep up with the latest trends, you will miss opportunities. Your ML projects will get stuck, hurting your ROI and ultimately impacting your bottom line. So, it’s essential to host your projects on a platform that’s constantly innovating with new ML and GenAI tools/offerings, while also making it easy to adopt new tools and integrate them into your current workflows. 

Without further ado, here’s my comprehensive comparison of essential ML and GenAI capabilities in Fabric and Azure Databricks: 

Azure Databricks vs. Fabric: Feature-by-Feature Comparison 

1. Model Serving

Azure Databricks provides built-in model serving with GPU optimization for both open-source and proprietary models (via AI Gateway), ensuring seamless real-time and batch inference at scale. Users can deploy models effortlessly with support for monitoring, versioning, and rollback, all integrated within the Lakehouse platform. In contrast, Microsoft Fabric lacks native model serving capabilities and GPU serving, requiring integration with Azure AI Foundry and Azure ML to enable serving workflows. This additional dependency not only adds complexity and cost but also limits the end-to-end user experience for deploying AI models efficiently. 

2. RAG

Azure Databricks supports Retrieval-Augmented Generation (RAG) natively through its advanced vector search tools in Mosaic AI. This capability allows organizations to implement RAG workflows directly, enabling high-performance, contextual response generation by combining enterprise data with LLMs. Microsoft Fabric, on the other hand, lacks RAG capabilities within its native framework. To implement RAG, users must rely on Azure AI Search + Azure ML, Azure AI Foundry, or Fabric Eventhouse. This requires additional integration steps, or in the case of Eventhouse, requires KQL proprietary syntax and is not purpose-built for a vector database. This fragmented approach slows down workflows and demands expertise in configuring multiple Azure services. 

3. Agent Framework and Evaluation

With Mosaic AI Agent Framework, Azure Databricks provides a complete framework for creating and evaluating AI agents, making it easier to fine-tune, deploy, and assess performance. Additionally, Azure Databricks includes tools for agent evaluation to optimize interactions with underlying LLMs. Microsoft Fabric does not include any native support for creating or evaluating AI agents. Instead, users must leverage Azure AI Foundry or other third-party frameworks, increasing the operational burden. This distinction highlights Databricks’ emphasis on delivering comprehensive solutions out of the box. 

4. AI Functions

Azure Databricks seamlessly integrates AI functions into its Lakehouse platform, enabling users to execute AI-driven transformations directly as part of their workflows. Integrating AI into analysis workflows provides access to information previously inaccessible to analysts, and empowers them to make more informed decisions, manage risks, and sustain a competitive advantage through data-driven innovation and efficiency. Meanwhile, Fabric has finally launched their own AI functions offering as of a few days ago, matching the functionality of AI functions in Azure Databricks. However, there is one key difference: AI functions in Fabric are designed to work with pandas or Spark dataframes, and do not work in a data warehouse due to Fabric’s segregated architecture. Conversely, Azure Databricks allows you to apply AI functions in both their data warehouse and on a dataframe running on a Spark cluster. 

5. LLMOps

Azure Databricks excels in LLMOps with robust tools like MLflow for end-to-end tracking, deployment, and monitoring of large language models (LLMs). These capabilities ensure models remain performant, reliable, and adaptable to changing data over time. Microsoft Fabric has features where users can track and compare their model experiments and runs with MLflow; however, it lacks features like real-time model serving, feature store, model quality monitoring, and model and data governance. Right now, Fabric can only do classical ML and batch prediction. Azure Databricks’ integrated solution significantly reduces operational friction compared to Fabric’s siloed approach. 

6. Structured Output

Azure Databricks supports the generation of structured output from LLMs, allowing users to fine-tune workflows to produce predictable and actionable results. This feature is tightly integrated within the Databricks ecosystem, supporting use cases such as report generation and structured summarization. In contrast, Fabric does not provide native support for structured outputs and relies on tools like Azure AI Foundry, particularly through the “Azure AI Content Understanding” service. This service allows users to extract information from various data types (text, audio, images, video) into customizable structured outputs using generative AI, essentially enabling the generation of well-defined, structured data with the help of JSON schemas. This added complexity in the overall architecture makes Fabric NOT a single unified solution. 

7. GenAI Governance

Azure Databricks leads in GenAI governance, offering unified governance across both data and AI assets via Unity Catalog. This centralized system ensures consistency, security, and auditability for both GenAI and traditional ML models. In contrast, Microsoft Fabric’s governance approach is disjointed, with separate governance mechanisms for each compute engine. The lack of a unified governance layer complicates compliance and creates inefficiencies in managing AI workflows. 

Choose Wisely or Pay Later 

Companies that are still new to machine learning might not realize the limitations of Fabric, at first. But by the time they’ve built some ML and GenAI applications on Fabric and realized the limitations, it might be too late to switch to Databricks without a costly migration effort. Or, they will wind up having a disjointed data science practice using multiple platforms for different projects. 

It’s a safe bet that machine learning and AI are going to become more critical to enterprises over time, not less. So the smart strategy would be to invest in a platform that puts ML & GenAI first, offers the most comprehensive set of native ML features, and is constantly adding more. 

When it comes to building a future-proof and unified AI practice, the evidence shows that Databricks is the superior choice for organizations serious about leveraging AI to grow and gain a competitive edge. In the end, Fabric’s most remarkable feature might be making you regret not choosing Databricks in the first place.