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 characterized by effective model training and open-source large language models, including MPT-7B and MPT-30B. Also, the acquisition allowed Databricks to integrate MosaicML model training functionality with its Lakehouse platform, providing an AI development environment that is both safe and scalable.
Fundamentally, Mosaic AI provides a single platform that integrates data engineering, machine learning, and generative AI. In addition, it assists enterprises in transforming experimentation into production as quickly as possible, enabling them to design their own AI models and intelligent agents that think, reason, and act across multifaceted 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 layer of governance, which will guarantee security, monitoring, and compliance of every AI model and deployment.
Databricks Mosaic AI is aimed at ensuring that AI development is enterprise-ready, which is achieved through the combination of flexibility, scalability, and transparency. Consequently, it allows organizations to create custom AI systems, which are not only powerful but also safe, regulated, and business-oriented.
Databricks Mosaic AI is unique compared to other AI platforms as it offers an integrated, data-centered, and production-ready platform, which contains the full AI lifecycle, including raw data to production.
Key Differences:
Unified Data and AI Infrastructure:
Traditional AI systems have distinct data storage, model training, and analytics systems, which in most cases result in inefficiencies and data silos. On the contrary, Mosaic AI is integrated with the Databricks Lakehouse, integrating data, analytics, and AI to a unified platform to work more effectively and more accurately.
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 provides effective and scalable training based on the advanced optimization methods of MosaicML and minimizes the cost of large-scale training without compromising the performance of the model. Hence, this enables organizations to train or fine-tune large models without incurring the high infrastructure requirements typical of conventional 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.
Conventional platforms are more centered on the application of single models. Otherwise, Mosaic AI is an AI that encourages compound AI, meaning that a group of agents, models, and data tools work together to answer complex problems to deliver more contextual and accurate outcomes.
Scalable Collaboration Environment:
Mosaic AI is designed to be collaborative across an enterprise and allow teams of data scientists, engineers, as well as business analysts to operate in the same environment as they share data sets, tools, and governance policies.
Altogether, Databricks Mosaic AI allows filling the data management-innovation gap and allows companies to grow their pilot projects into enterprise-scale 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.
Manufacturing and Supply Chain Optimization
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
Data ingestion into Databricks Lakehouse begins with the accumulation of structured and unstructured data. Moreover, organizations can prepare, clean, and standardize data using Delta Lake and the Unity Catalog, ensuring high-quality data and compliance.
Step 2: Indexing, Embedding, and Model Fine-Tuning
Once the data is ready, it can be indexed and embedded with Databricks Vector Search, enabling models to fetch relevant context to generate responses. Furthermore, enterprise data can be used by teams to refine base models and deliver domain performance.
Step 3: Agent Creation via Agent Framework
Developers use the Mosaic AI Agent Framework to create intelligent agents that build tools, prompts, and workflows and automate tasks. Moreover, agents can access data, reason, and perform multi-step tasks. They communicate with APIs and other systems via the Tool Catalog, thereby increasing functionality.
Step 4: Deployment, Monitoring, and Governance
Databricks Model Serving is utilized to deploy AI systems with scales capable of serving models and agents with high security. Also, the AI Gateway guarantees governance through the provision of access, activity monitoring, and compliance implementation throughout the deployment space.
Step 5: Evaluation and Iteration
Once deployed, Mosaic AI allows perpetual enhancement by means of Agent Evaluation and MLflow Tracing. Furthermore, feedback mechanisms are used to determine the level of accuracy of the model, performance of the agents and user satisfaction. This information is utilized to fine-tune prompts, re-train models and increase system reliability in the long run.
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 are thrilled to collaborate with Databricks and combine our strong background in AI, analytics, and data engineering with their great Data Intelligence Platform. Moreover, our team not only has extensive experience in AI, data engineering, and cloud configuration but also integrates the Databricks Lakehouse Platform. Collectively, we develop tailored solutions that are less complex, produce better data, and provide insights more quickly.
In addition to real-time ETL pipelines based on Delta Lake to protect multi-cloud deployments, we optimize all components of the data and AI stack for performance and governance. Our implementation services cover the entire lifecycle, from strategy and setup to deployment and monitoring. We also create tailored Lakehouse architectures that support business objectives, create reliable data pipelines, and control machine learning processes with MLflow and Mosaic AI.
We further deployed Unity Catalog to achieve enterprise-grade governance, including role-based access, lineage tracking, and compliance. Consequently, we aim to assist clients in transitioning from experimentation to production in a short period of time, with stable 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
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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.
