At the 2025 Data + AI Summit , Databricks introduced Mosaic AI, a unified platform built to help organizations develop and deploy generative AI applications securely using their own data. Mosaic AI combines model training, fine-tuning, and deployment into a single framework, enabling enterprises to transition seamlessly from experimentation to production. Furthermore, companies like Shell, Rolls-Royce, and ADP are already using Mosaic AI to automate workflows, create insights, and improve decision-making across operations.
IDC predicts that by 2026, enterprises will use generative AI and automation to drive $1 trillion in productivity gains . It also forecasts that GenAI will take over 42% of traditional marketing tasks. Moreover, its tight integration with the Databricks Lakehouse Platform enables teams to manage data, analytics, and AI models within a single environment, thereby reducing complexity and enhancing governance.
Continue reading this blog to learn how Databricks Mosaic AI is reshaping enterprise AI with scalability, transparency, and real-world results.
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Key Takeaways Databricks introduced Mosaic AI, a unified platform for building, fine-tuning, and deploying generative AI securely. Mosaic AI integrates model training, agent development, and governance within the Databricks Lakehouse, ensuring scalability and compliance. Core components include Model Training, Agent Framework , Tool Catalog, and AI Gateway for full AI lifecycle management. It supports compound AI systems , enabling collaboration among multiple agents, tools, and models to achieve accurate results. Mosaic AI differs from traditional platforms in that it features built-in governance, an open architecture, and cost-efficient training. Key use cases span enterprise chatbots, data analysis , customer support, healthcare, and manufacturing optimization. The platform ensures faster time to value, reliability, and secure enterprise-wide collaboration. Kanerika’s partnership with Databricks empowers organizations to implement AI-driven, data-centric solutions with strong governance and scalability.
What is Databricks Mosaic AI? Mosaic AI is Databricks’ enterprise-grade platform for building, training, deploying, and governing generative AI applications . It was created after Databricks acquired MosaicML in July 2023 for $1.3 billion. MosaicML was known for its efficient model training and open-source large language models, such as MPT-7B and MPT-30B. Additionally, the acquisition enabled Databricks to combine MosaicML’s model training capabilities with its Lakehouse setup, offering a secure and scalable AI development environment.
At its core, Mosaic AI offers a unified environment that combines data engineering, machine learning, and generative AI into a single platform. Furthermore, it helps enterprises move from experimentation to production quickly, enabling them to create custom AI models and intelligent agents that understand, reason, and act across complex tasks.
Core Components of Databricks Mosaic AI: Model Training: A scalable training framework that allows fine-tuning large language models (LLMs) using enterprise data to achieve domain-specific precision.Agent Framework: Tools for developing intelligent AI agents capable of completing multi-step tasks and interacting with other systems or data sources.Tool Catalog: A library of reusable tools and APIs that boost AI agent capabilities for various workflows.AI Gateway: A centralized governance layer that ensures security, monitoring, and compliance across all AI models and deployments.
The purpose of Databricks Mosaic AI is to make AI development enterprise-ready by combining flexibility, scalability, and transparency. As a result, it enables organizations to build tailored AI systems that are not only powerful but also safe, governed, and aligned with their business goals.
Databricks Mosaic AI stands out from traditional AI platforms by providing a data-focused, integrated, and production-ready environment that encompasses the entire AI lifecycle, from raw data to deployment.
Key Differences: Unified Data and AI Infrastructure: Traditional AI platforms rely on separate systems for data storage, model training, and analytics, which often leads to inefficiencies and data silos. In contrast, Mosaic AI connects directly with the Databricks Lakehouse, bringing together data, analytics, and AI on a single platform for enhanced collaboration and accuracy.
Built-in Governance and Security: Many AI platforms focus on experimentation but lack strong governance. However, Mosaic AI provides an AI Gateway that enforces access controls, monitors model activity, and ensures compliance with enterprise and regulatory standards.
Optimized Model Training: Mosaic AI delivers efficient, scalable training through MosaicML’s advanced optimization techniques, reducing the cost of large-scale training while maintaining high model performance . Therefore, this allows organizations to train or fine-tune large models without the high infrastructure burden common in traditional systems.
Open and Extensible Architecture: While legacy AI platforms are often closed or limited to specific vendors, Mosaic AI supports open-source frameworks and connects smoothly with external tools. Consequently, this enables enterprises to tailor workflows to meet their specific data and model requirements.
Traditional platforms focus mainly on single-model applications. In contrast, Mosaic AI supports compound AI, where multiple agents, models, and data tools collaborate to solve complex problems, providing more contextual and accurate results.
Scalable Collaboration Environment: Mosaic AI is built for enterprise-wide collaboration, enabling teams of data scientists, engineers, and business analysts to work together within the same environment using shared datasets, tools, and governance policies.
Overall, Databricks Mosaic AI bridges the gap between data management and AI innovation, enabling organizations to scale from pilot projects to full enterprise-grade AI systems.
Key Features and Services of Databricks Mosaic AI Databricks Mosaic AI includes a complete suite of features and services that simplify and speed up AI development across industries. Moreover, it provides everything needed to train, fine-tune, deploy, and monitor AI systems at scale.
1. Mosaic AI Model Training Mosaic AI offers a high-performance model training environment built for both large-scale and domain-specific AI models. Furthermore, it supports distributed training on vast datasets, helping teams fine-tune large language models for specialized business use cases.
Accelerated training using optimized hardware and efficient parallelization. Fine-tuning models with proprietary data for accuracy and relevance. Built-in cost optimization for large model training at scale. Integration with Databricks Lakehouse to access high-quality, governed data .
2. Mosaic AI Agent Framework This framework enables developers to build intelligent AI agents that can reason, plan, and execute tasks autonomously. Additionally, these agents can connect with external tools, APIs, and data systems to handle complex workflows across departments.
Predefined templates to speed up agent development . Context-aware task execution for dynamic workflows. Smooth integration with company databases, APIs, and knowledge bases. Real-time monitoring of agent activity and performance.
The Tool Catalog provides access to reusable tools and connectors that boost model capabilities and streamline AI workflows. As a result, developers can quickly add new functions without rebuilding models from scratch.
A library of built-in and third-party tools. Easy customization for domain-specific tasks. Centralized management and version control for all tools and connectors.
4. AI Gateway for Governance and Security The AI Gateway acts as a control layer for governance, compliance, and observability across all AI activities within Databricks. Furthermore, it ensures that AI models are used responsibly and securely.
Centralized access control and monitoring for AI operations. Audit trails to maintain accountability. Policy enforcement to prevent unauthorized use of data or models. Integration with enterprise security systems for compliance.
Mosaic AI’s seamless integration with the Databricks Lakehouse provides organizations with direct access to both structured and unstructured data . Moreover, this integration ensures that models are continuously trained and deployed using high-quality, trusted data.
6. Support for Generative and Compound AI Systems Mosaic AI supports the creation of compound AI systems, where multiple AI components collaborate to facilitate contextual reasoning and informed decision-making.
Enables LLM-based assistants, copilots, and data analytics bots. Combines retrieval-augmented generation (RAG), APIs, and models for real-world intelligence. Built for scalable, production-grade generative AI applications.
Through these features, Databricks Mosaic AI helps enterprises develop, deploy, and scale generative AI responsibly. By bringing together data management , model governance, and AI innovation under one roof, it ensures every organization can move confidently from experimentation to real-world AI impact.
Use Cases and Real-World Examples Databricks Mosaic AI is designed for enterprises seeking to develop intelligent, data-driven solutions that surpass traditional automation. Furthermore, its versatility enables it to serve multiple industries, allowing organizations to deploy generative AI with speed and scalability.
Top Use Cases of Databricks Mosaic AI: Enterprise Chatbots and Virtual Assistants Organizations can create domain-specific chatbots and copilots powered by Mosaic AI’s Agent Framework . Moreover, these agents can access internal databases through retrieval-augmented generation (RAG), providing employees and customers with accurate, context-aware responses.
Automated Data Analysis and Reporting Financial institutions and large corporations utilize Mosaic AI to analyze vast volumes of structured and unstructured data in real-time. Additionally, AI agents create summaries, detect anomalies, and generate insights, enabling teams to make faster and more informed business decisions .
Using fine-tuned large language models , businesses can automate customer support with intelligent systems that understand queries, resolve issues, and efficiently escalate complex cases. Furthermore, all this is achieved while maintaining compliance and governance.
Knowledge Management Systems Enterprises use Mosaic AI to organize and retrieve knowledge from internal documents, policies, and reports. As a result, by indexing and embedding this information, AI agents can deliver relevant answers, improving organizational knowledge flow.
Healthcare and Life Sciences Mosaic AI helps healthcare organizations streamline research, clinical documentation, and compliance by securely training models on sensitive medical data within a governed environment.
Manufacturers use Mosaic AI for predictive maintenance , process optimization, and intelligent monitoring. Moreover, AI systems trained on sensor and operational data can anticipate failures, reducing downtime and costs.
These real-world applications showcase Mosaic AI’s ability to turn raw data into useful intelligence, helping businesses automate workflows, boost customer experiences , and drive innovation.
Benefits of Using Mosaic AI in Databricks Databricks Mosaic AI offers a unique combination of flexibility, governance, and scalability, making it ideal for organizations that want to build enterprise-grade AI solutions without compromising data control or performance.
Mosaic AI combines data, analytics, and AI within Databricks Lakehouse, breaking down silos and enabling teams to manage the entire AI lifecycle in one place. As a result, this unified approach improves collaboration, reduces complexity, and speeds up innovation.
Databricks provides cloud-scale infrastructure optimized for AI workloads. Furthermore, Mosaic AI can handle large-scale model training and serving while maintaining speed and efficiency, helping enterprises iterate faster and deliver results quickly.
3. Built-in Governance and Security Governance is central to Mosaic AI. Moreover, through the AI Gateway and Unity Catalog, enterprises can manage access, audit model usage, and maintain compliance with industry rules. This makes it especially valuable for sectors like finance, healthcare, and government.
4. Flexibility and Openness Mosaic AI supports both open-source and foundation models, allowing organizations to choose or bring their own models. Additionally, the platform’s modular design enables integration with external tools, APIs, and frameworks, ensuring adaptability to diverse enterprise needs.
5. Faster Time to Value By using built-in retrieval, tool integration, and function-calling capabilities, Mosaic AI removes the need to build AI systems from scratch. Therefore, teams can focus on applying AI to real business problems instead of managing infrastructure.
6. End-to-End Lifecycle Support From data ingestion to deployment and continuous optimization, Mosaic AI manages the entire AI lifecycle within one environment. Consequently, this complete structure reduces friction between teams and shortens development cycles.
7. Reliable, Production-Ready AI Systems With its focus on compound AI and agent-based workflows, Mosaic AI helps organizations build AI systems that are not just functional but reliable, governed, and ready for enterprise use.
How Mosaic AI Works: A High-Level Workflow Databricks Mosaic AI provides a structured workflow that guides teams from data ingestion to AI deployment and continuous improvement. Furthermore, its modular design ensures flexibility while maintaining scalability and governance.
Step 1: Data Ingestion and Preparation The process begins with data ingestion into Databricks Lakehouse, where both structured and unstructured data are collected and stored. Additionally, by using Delta Lake and Unity Catalog, organizations can prepare, clean, and standardize their data, ensuring high-quality data and compliance.
Step 2: Indexing, Embedding, and Model Fine-Tuning Once the data is prepared, it can be indexed and embedded using Databricks Vector Search, which enables models to retrieve relevant context for generating responses. Moreover, teams can also fine-tune base models using enterprise data to achieve domain-specific performance.
Step 3: Agent Creation via Agent Framework Using the Mosaic AI Agent Framework , developers define intelligent agents by setting up tools, prompts, and workflows to automate tasks. Furthermore, agents can retrieve data , execute reasoning steps, and perform multi-step tasks. They interact with APIs and external systems through the Tool Catalog, expanding their functionality.
Step 4: Deployment, Monitoring, and Governance AI systems are deployed using Databricks Model Serving, which offers scalable and secure deployment of models and agents. Additionally, the AI Gateway ensures governance by managing access, monitoring activity, and enforcing compliance across the deployment environment.
Step 5: Evaluation and Iteration After deployment, Mosaic AI enables continuous improvement through Agent Evaluation and MLflow Tracing. Moreover, feedback loops help assess model accuracy, agent performance, and user satisfaction. This data is used to refine prompts, retrain models, and boost system reliability over time.
This workflow enables teams to transition from raw data to production-ready AI applications within a unified, controlled, and collaborative environment.
Kanerika’s Partnership with Databricks: Enabling Smarter Data Solutions We at Kanerika are proud to partner with Databricks, bringing together our deep expertise in AI, analytics, and data engineering with their robust Data Intelligence Platform. Furthermore, our team combines deep know-how in AI, data engineering , and cloud setup with Databricks’ Lakehouse Platform. Together, we design custom solutions that reduce complexity, improve data quality, and deliver faster insights. Moreover, from real-time ETL pipelines using Delta Lake to secure multi-cloud deployments , we make sure every part of the data and AI stack is optimized for performance and governance.
Our implementation services cover the full lifecycle—from strategy and setup to deployment and monitoring. Additionally, we build custom Lakehouse blueprints aligned with business goals, develop trusted data pipelines, and manage machine learning operations using MLflow and Mosaic AI. We also implemented Unity Catalog for enterprise-grade governance , ensuring role-based access, lineage tracking, and compliance. As a result, our goal is to help clients move from experimentation to production quickly, with reliable and secure AI systems.
We solve real business challenges, such as breaking down data silos, enhancing data security , and scaling AI with confidence. Furthermore, whether it’s simplifying large-scale data management or speeding up time-to-insight, our partnership with Databricks delivers measurable outcomes. We’ve helped clients across industries—from retail and healthcare to manufacturing and logistics—build smarter applications, automate workflows, and improve decision-making using AI-powered analytics.
Discover the full potential of AI through Databricks Mosaic AI Partner with Kanerika to scale enterprise-grade AI with confidence.
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FAQs 1. What is Mosaic AI? Mosaic AI is a unified platform by Databricks designed to help organisations build, deploy, govern and monitor generative-AI and agent-based applications using their data.
2. What kinds of components does Mosaic AI include? Mosaic AI includes several key components that streamline the development of generative AI solutions. These include Model Training for fine-tuning or building custom models, Vector Search to store and retrieve embeddings, Agent Framework and Evaluation for building and testing retrieval-augmented generation (RAG) systems, and Model Serving and Gateway to deploy models securely, manage access, and monitor performance.
3. What problems does Mosaic AI aim to solve? It addresses challenges like: integrating enterprise data into gen-AI workflows, ensuring governance and traceability of models and data, managing quality of AI outputs, deploying at scale with monitoring, and reducing reliance on external scatter-gun LLM solutions.
4. Who should use Mosaic AI? Teams in enterprises that want to build production-grade AI/agent applications, especially where data governance, model evaluation, operationalisation and organisational scale matter. While data scientists will use many features, other team members (analysts, engineers) benefit too.
5. What are key benefits of using Mosaic AI? Key benefits include: ownership and control over models and data, streamlined vector search and retrieval capability, unified deployment and monitoring of AI systems, built-in governance and guardrails, and faster iteration of AI/agent systems.
