Generative AI is quickly moving from prototype to production — but most organizations are realizing it’s not enough to just build a great model. To scale responsibly, you need the right foundation: a data platform that prioritizes cost visibility, robust governance, and user trust. That’s where Snowflake shines — and where many projects either thrive or stall.

In this post, we’ll explore how companies are using Snowflake to deploy GenAI responsibly, what challenges tend to emerge at scale, and what best practices we recommend for managing cost, ensuring governance, and preserving trust across the organization.

The Real Challenge Isn’t the Model — It’s the Ecosystem Around It

It’s tempting to think GenAI success comes down to choosing the right model or fine-tuning it perfectly. But that’s only part of the story. Once you launch a GenAI feature or assistant, the bigger questions start showing up:

• Is the data it pulls from accurate and up to date?

• Is the AI following your compliance and privacy policies?

• Can you explain the output if someone challenges it?

• Are you spending more on inference than you’re saving in efficiency?

Snowflake’s data-first architecture makes it possible to answer these questions—before they turn into blockers.

Why Cost, Governance & Trust Should Be Built In — Not Bolted On

1. Cost: GenAI Can Burn Budget Fast

Generative AI, especially when using large models, has a very real cost footprint. In a recent Forrester study, enterprise leaders cited cost management as one of their top concerns with GenAI adoption.

Even modest use cases like summarizing call transcripts or classifying support tickets can lead to:

• High token usage for long-form content

• Redundant calls due to lack of caching

• Poorly scoped prompts that inflate context windows

Best Practices in Snowflake:

• Use Cortex functions inside Snowflake to minimize data movement and reduce integration overhead.

• Log usage by function and user using built-in observability tools to track what’s actually being used.

• Batch long-running or lower-priority tasks to avoid real-time overhead.

• Test prompts for efficiency, not just output quality — every token counts.

2. Governance: GenAI Without Guardrails Is a Liability

Unstructured data is the lifeblood of many GenAI apps — emails, PDFs, chats, notes — but it also carries significant risk. The wrong model response pulled from the wrong source can lead to inaccurate advice, compliance violations, or worse.

Best Practices in Snowflake:

• Leverage data classification and tags to label sensitive data (e.g. PII, financials, contracts) before it ever enters your vector store.

• Apply role-based access controls at the table and column level to ensure only authorized agents see sensitive context.

• Use retrieval-augmented generation (RAG) instead of hard-coded context windows, and trace responses back to the original documents.

• Enforce prompt templates that limit what inputs are allowed and flag unexpected behavior.

Snowflake’s ability to manage both structured and unstructured data in one platform makes this type of governance far easier than with traditional, fragmented pipelines.

3. Trust: If People Don’t Trust the Output, They Won’t Use It

This might be the most underestimated barrier to GenAI adoption. If your users feel unsure about the accuracy, origin, or intention of the model’s response, they’ll stop engaging — no matter how technically impressive it is.

Best Practices in Snowflake:

• Include metadata in GenAI responses—where did the answer come from? When was the source updated?

• Set up feedback mechanisms so users can flag issues, suggest corrections, or request human review.

• Start with hybrid workflows where GenAI supports, but doesn’t replace, human judgment.

• Use output scoring (like confidence scores, relevance checks, and human ratings) to continuously tune performance.

Trust builds slowly — and gets lost quickly. Responsible GenAI teams treat every interaction as an opportunity to earn that trust.

Real-World Example: Automating RFP Summaries with Confidence

One financial services customer we worked with wanted to automate summarizing 100+ page RFPs using LLMs. The upside was obvious: faster turnaround, less analyst fatigue, and quicker go/no-go decisions.

But here’s what made it work:

• They stored all RFPs in Snowflake and used Cortex’s document processing functions.

• They tagged sensitive sections (e.g. legal terms, pricing) to exclude from GenAI processing.

• They added a step where users could review the AI’s summary side-by-side with the source text.

• They tracked edits over time to improve future prompt and model tuning.

The result? A 70% reduction in time-to-summary — and a high level of user trust because every step was observable, explainable, and well-governed.

Scaling Responsibly Is a Business Strategy — Not Just a Technical One

As generative AI moves deeper into the enterprise, success isn’t just about capability. It’s about control—control over cost, compliance, and confidence. Snowflake provides the primitives: centralized data, native AI functions, flexible security layers. But it takes a clear strategy to align them to real outcomes.

At our firm, we work with companies to turn GenAI from scattered pilot projects into durable platforms — ones that are transparent, efficient, and genuinely helpful. We help define the use cases, model behavior, and data strategy that let AI flourish responsibly.

Final Thought: Trust Is the Real Multiplier

You can have the best models, the richest data, and the sleekest UI — but if people don’t trust your GenAI systems, they won’t scale. That’s why the real foundation for ROI isn’t a faster model — it’s a smarter strategy. And that starts in your data platform.

With Snowflake, you’ve already got what you need to build responsibly. The next step is doing it intentionally.