As generative AI moves from proof of concepts to real world applications, financial services leaders face the pressure to choose wisely whether it is the right model or the right platform. GenAI is redefining how the value is added by delivering document workflow where it extracts documents and provide a concise summary to the platform such as credit memo or overall summary to be check by the operations teams before a loan is disbursed to the borrower.  

In the current landscape, two frontrunners have emerged for enterprises operating within modern data stacks:  

• Databricks Mosaic AI, designed for end-to-end control, customization, and governance. 

• Snowflake Cortex, optimized for speed, simplicity, and built-in enterprise access. 

Why these two? While platforms like AWS Bedrock, Azure OpenAI, or Claude bring strong GenAI capabilities, Mosaic and Cortex stand out because they are deeply embedded in the existing data infrastructure of many enterprises. They offer native integration, strong governance, and production-ready tooling—making them ideal for financial services use cases where performance, trust, and traceability are non-negotiable. 

So, how do you decide between them? 

Both are powerful. But your best-fit platform depends on your enterprise’s data strategy, AI maturity, governance needs, and operational goals. Let’s unpack the decision below. 

Databricks Mosaic AI: Tailored for Customization and Scale 

What is it?  

Mosaic AI by Databricks is a unified platform purpose-built for building, deploying, and governing production-grade Generative AI systems. Built on top of the Databricks Lakehouse, it seamlessly integrates the full AI lifecycle from data preparation and prompt engineering to LLM fine-tuning, deployment, and monitoring with robust enterprise governance. 

It is designed for enterprises that need: 

• Fine-grained control over open-source and proprietary LLMs 

• Scalable infrastructure for real-time and batch inference 

• Integrated tooling for prompt design, vector search, and RAG pipelines 

• Enterprise grade governance, especially in regulated sectors like financial services 

Core Capabilities of Mosaic AI  

Before considering architecture decisions, it helps to see what Mosaic AI already puts on the table: 

1. Model Training, Fine-Tuning, and Deployment 

• Supports open-source models like MPT, LLaMA, and Mixtral—served securely via REST endpoints. 

• Fine-tuning and versioning powered by MLflow for auditability and experimentation. 

• Real-time (e.g., chatbot APIs) and batch inference (e.g., overnight generation of 100K+ loan summaries)  

• Managed, auto-scaling infrastructure for cost-efficient inferencing 

2. Prompt Engineering & Experimentation 

• Integrated Prompt Engineering Studio and AI Playground 

• Full experiment tracking: logs prompt outputs, parameters, and metrics 

• Support for LangChain, Hugging Face, and other libraries in a notebook-native environment 

3. Built-in Vector Search and RAG Support 

• Native vector database to store and query embeddings 

• Indexes can be linked to security contexts and data lineage, crucial for financial audit trails 

These three pillars give teams everything they need to iterate quickly—from raw data to production endpoints—without wrestling with external MLOps glue code. 

Unity Catalogue: GenAI Governance Backbone 

Raw capability is only half the story; at enterprise scale, governance becomes the gating factor. While Unity Catalogue has long governed tables, files, and ML models, its new GenAI extensions close the loop for agentic systems: 

Governance for GenAI Components 

Unity Catalogue now tracks and secures prompts, embeddings, vector indexes, RAG knowledge bases, and model lineage in the very same catalog that auditors already trust. The result: one policy engine, one audit trail.

(Source: Databricks) 

A Business Use Case: Deploying a GenAI-Powered Credit Risk Agent with Mosaic AI 

To see how these pieces click together, imagine a lender aiming to streamline underwriting. 
With Mosaic AI, the institution can build an agent that: 

• Ingests tax returns, financial statements, and banking PDFs 

• Extracts relevant financials and normalizes them 

• Applies internal credit rules to generate scores and risk factors 

• Outputs a concise credit memo for review 

Behind the scenes, the same stack ensures the agent: 

• Is fine-tuned on proprietary loan data 

• Leverages RAG architecture for grounded responses 

• Can be deployed via real-time or batch endpoints 

• Tracks prompt iterations, model versions, and outputs 

• Operates within a fully governed environment—end-to-end 

Net outcome: faster credit decisions, lower manual workload, and an auditable chain-of-trust that satisfies regulators. 

Snowflake Cortex: Designed for Speed and Accessibility 

What is it?  

Snowflake Cortex is a fully managed, serverless GenAI platform natively embedded within the Snowflake Data Cloud. It enables organizations to quickly build and deploy AI-powered solutions using pre-integrated LLMs and natural language interfaces—with zero DevOps and no data movement required. 
It’s ideal for organizations that want: 

• Fast access to GenAI functions with minimal engineering lift 

• Natural language interfaces for querying enterprise data 

• Built-in AI tools for document analysis, insights, and automation 

• Strong governance without the overhead of managing infrastructure 

Core Capabilities of Snowflake Cortex for GenAI Systems 

Think of Cortex in three layers: plug-and-play AI, effortless customization, and rich data interaction — all governed inside the Snowflake control plane. 

1. Plug-and-Play LLM Functions 

• Prebuilt AI functions for summarization, classification, translation, extraction, and sentiment analysis—accessible via SQL, Python, or UI. 

• Supports leading open and proprietary LLMs (e.g., Mistral, Claude, Snowflake Arctic) within the secure Snowflake runtime. 

• No setup required functions can be invoked directly within Snowflake worksheets, stored procedures, or pipelines. 

2. LLM Customization Without Infrastructure 

• Serverless fine-tuning capabilities for customizing models to domain-specific use cases—no infrastructure to manage and optimized for simplicity. 

• Fine-tuned models remain governed and scoped to the enterprise’s Snowflake environment, maintaining data privacy and compliance. 

3. AI-Powered Data Interaction 

• Cortex Analyst: A built-in conversational agent allowing analysts and business users to query enterprise data in natural language—backed by LLMs and SQL generation. 

• Uses context from the enterprise data warehouse, with near real-time response and guardrails for accuracy and traceability. 

4. Cortex Agents and Document Intelligence 

• Cortex Agents enable orchestration of structured and unstructured data, invoking tools and external APIs to deliver automated insights—ideal for operations, finance, or customer service scenarios. 

• Built-in search and document understanding tools allow retrieval and analysis of enterprise files (PDFs, contracts, forms) directly from the platform. 

5. AI Playground for Experimentation 

• No-code environment to test and compare multiple LLMs side-by-side, making it easier to choose the right model for the job. 

• Facilitates prompt experimentation and tuning without requiring developers or MLOps teams. 

Together, these five pillars give teams a friction-free path from prototype to production—without stitching together external MLOps tools or worrying about data egress. 

Governance and Security in Snowflake Cortex 

Raw capability means little without airtight governance. Cortex inherits Snowflake’s unified security model—roles, row-level policies, lineage, and audit logging—so every prompt, model, and result stays inside the same compliance envelope as your tables: 

A Business Use Case: Cortex in an Investment Reporting Workflow 

Imagine an asset management firm using Snowflake Cortex to: 

• Auto-extract insights from portfolio manager reports 

• Enable analysts to ask fund-related questions in natural language 

• Run sentiment analysis on earnings calls and investor transcripts 

• Deploy a Cortex Agent that combines returns data and news to generate real-time alerts 

All of this can be done without leaving the Snowflake environment—no infrastructure setup, no third-party model hosting, and with full enterprise control. 

(Source: Snowflake Cortex AI) 

Decision Criteria: What Financial Services Leaders Should Consider 

Choosing between Mosaic AI and Cortex isn’t just about features—it’s about alignment. Each platform brings strengths, but which one fits best depends on your enterprise’s data architecture, AI maturity, governance needs, and team structure. 

Let’s break it down into three dimensions that matter most in financial services: 

1. Strategic Alignment with Data & AI Capabilities 

Start by asking, “Which platform naturally extends the data estate we already own?” 
If your shop runs a Lakehouse and leans on Spark, Mosaic AI often feels like a native upgrade. If you’re Snowflake-centric and want low-lift GenAI endpoints, Cortex can shorten time-to-impact.

2. Governance, Compliance & Control

Next, flip the lens to regulatory rigor. Capital-markets and banking regulators won’t care how elegant your prompts are—only that every inference is traceable.

• Auditability: Mosaic’s Unity Catalog + MLflow pairs model lineage with row-level data policies.

• LLM Custody: Cortex keeps fine-tuned models inside the Snowflake boundary, eliminating data egress risks.

• User Accessibility vs. Guardrails: Decide whether power users need open notebooks (Mosaic) or guided SQL/UIs (Cortex).

3. Operational Efficiency & Cost Considerations

Finally, pressure-test each option against budgets and staffing.

Align Platform Choice to Strategic Intent 

For most enterprises, the decision around a GenAI platform will be closely tied to existing data platform investments. Organizations that have already standardized on Databricks are well-positioned to extend those capabilities through Mosaic AI, while those operating within the Snowflake ecosystem will find Cortex to be the most natural and seamless evolution.

That said, aligning platform choice with strategic priorities (not just technical compatibility) ensures long-term scalability and value.

Mosaic AI is best suited for enterprises that requires:

• Full control over model development and deployment

• The ability to fine-tune on proprietary datasets for domain-specific outcomes

• Deep integration into Spark-native, data engineering-centric workflows

• End-to-end governance across the AI lifecycle, supported by Unity Catalog and MLflow

Snowflake Cortex is ideal for organizations that prioritizes:

• Fast deployment of GenAI via low-code, prebuilt functions

• SQL-native access for business and analyst teams

• Serverless AI that minimizes operational overhead

• Built-in compliance guardrails with no data egress

Closing the Gap Between Capability and Impact

The GenAI wave is reshaping financial services—faster underwriting, smarter reporting, streamlined operations. But to harness it meaningfully, the question isn’t “What can GenAI do?”—it’s “What are we building, and what do we need to get there?”

Both Databricks Mosaic AI and Snowflake Cortex are enterprise-ready platforms—but they serve different needs:

• Mosaic AI delivers deep control, customization, and lifecycle governance—perfect for AI-native institutions building differentiated, domain-specific intelligence.

• Cortex offers rapid deployment, accessibility, and built-in simplicity—ideal for scaling GenAI across business teams without adding technical debt.

Both Mosaic AI and Snowflake Cortex can move the needle, but only if the rollout is scoped, measurable, and tied to a real business win. Start with one use-case, keep the data domain tight, and iterate fast under clear governance.

The path forward is simple: start small, stay focused, and collaborate with technology partners who can turn promising ideas into real-world results. Curious which GenAI platform best fits your AI strategy?

Let’s connect to explore what a production-grade solution could look like for your business.