Snowflake’s most recent earnings call, led by CEO Sridhar Ramaswamy, was filled with signals about where the data cloud is heading, and what it means for the teams tasked with navigating it.
Let’s break down the key takeaways and why they matter for Snowflake users.
1. Innovation Is Accelerating, But So Is Complexity
Snowflake delivered 125+ new product capabilities in Q1, double the number from a year ago. That’s an astonishing pace of innovation, especially with momentum building in open data formats like Apache Iceberg and AI tooling like Cortex.
But here’s the challenge: most Snowflake customers don’t know what features to use, or when.
With so many knobs and levers, every feature choice carries a cost-performance tradeoff. Customers need guidance, not just capabilities. What’s missing is an intelligence layer , something that helps data teams decide when a feature actually delivers ROI vs. when it just adds complexity.
2. Optimization Is Constant – But Not Always Visible
As Ramaswamy put it, “Our customers are constantly optimizing.” And it shows: Snowflake’s Net Dollar Retention remains high at 123%. But this high retention often hides a hidden struggle – customers may be spending more, but are they spending right?
In a world where 20-40% of Snowflake data costs now come from non-compute services, focusing on warehouse efficiency alone is no longer enough. Real optimization starts with full-spend observability, and a clear understanding of how services, not just compute, contribute to cost.
As more teams experiment with AI and advanced analytics, it’s critical they find ways to fund innovation without runaway growth. That means identifying low-value workloads and offsetting new spend through smarter reallocation, not just budget increases.
3. AI Usage Is Soaring – But ROI Remains Elusive
According to Snowflake, over 5,200 accounts are using their AI and machine learning tools on a weekly basis. Cortex AI has moved from niche add-on to foundational offering.
But let’s ask the hard question: how many of those AI models are driving real business impact?
In many cases, AI workloads originate outside the data team, in marketing, ops, or product. That makes it harder to assess whether they’re delivering ROI or simply running in the background. Without cross-team accountability and usage transparency, companies risk a growing gap between AI cost and value.
Final Thought: A Good Problem to Have, If Managed Right
There’s no doubt that Snowflake is shifting decisively toward a technology-led, innovation-driven strategy and that’s a good thing. But for data teams, it comes with a new responsibility: to balance the excitement of new features with the discipline of smart spending.
At Seemore Data, we’re proud to help Snowflake customers see exactly where their data dollars go, and how to align spend with business value. As this ecosystem evolves, the winners will be those who turn observability into action, and cost into clarity.
To Learn more, I invite you to set up 1:1 session with. You can reach me out via email: ariel@seemoredata.io
