Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now
Just days after Gartner’s stock plummeted 50% on warnings of slowing enterprise technology purchases, Snowflake delivered a resounding counter-narrative. Enterprises aren’t pulling back on data infrastructure. They’re doubling down.
The cloud data platform company reported 32% year-over-year growth in product revenue for its fiscal second quarter, accelerating from the previous quarter and adding 533 new customers. More tellingly for enterprise technology leaders, AI workloads now influence nearly 50% of new customer wins and power 25% of all deployed use cases across Snowflake’s platform.
“Our core business analytics continues to be strong. It’s the foundation of the company,” Snowflake CEO Sridhar Ramaswamy said during the earnings call. But he emphasized something more significant: “This data modernization journey is even more important than before because they realize that AI transformation of workflows of how they interact with their customers is critically dependent on getting their data in a place that’s AI-ready.”
The AI data infrastructure imperative
This dynamic reveals why enterprise data spending appears insulated from broader technology budget constraints. Unlike discretionary software purchases that can be deferred, data infrastructure has become mission-critical for AI initiatives.
AI Scaling Hits Its Limits
Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are:
- Turning energy into a strategic advantage
- Architecting efficient inference for real throughput gains
- Unlocking competitive ROI with sustainable AI systems
Secure your spot to stay ahead:
“Snowflake’s booming growth shows that companies continue to invest in data, analytics, and AI, improving efficiency as a way to meet profit goals in the face of economic headwinds,” Kevin Petrie, VP Research at BARC US, told VentureBeat. “We find that most companies prefer to work with existing vendors as they experiment with and deploy AI.”
Snowflake’s technical metrics underscore this urgency. The company launched 250 new capabilities to general availability in just six months. New features span four key areas: analytics, data engineering, AI and applications and collaboration. Over 6,100 accounts now use Snowflake’s AI capabilities weekly, representing rapid enterprise adoption of production AI workloads.
The company’s new Snowflake Intelligence platform enables natural language queries across structured and unstructured data while powering intelligent agents directly on enterprise datasets. Early adopters, such as Cambia Health Solutions, have deployed it to analyze vast amounts of longitudinal healthcare data. Duck Creek Technologies uses it across finance, sales and HR functions.
Technical architecture driving growth
Several technical developments explain why enterprises are accelerating, rather than slowing, their investments in data platforms.
Unified AI and analytics: Snowflake’s new Cortex AI SQL brings AI models directly into SQL queries. This eliminates data movement and enables real-time AI-powered analytics. The architectural approach addresses a key enterprise concern about AI implementations: data governance and security.
Performance optimization: The company’s Gen 2 Warehouse delivers up to 2x faster performance while automatically optimizing resources. This addresses cost concerns that might otherwise slow adoption.
Migration acceleration: Enhanced tools for moving legacy on-premises systems to cloud platforms reduce implementation timelines. This makes modernization projects more palatable even during uncertain economic periods.
Open standards integration: Support for Apache Iceberg and the new Snowpark Connect for Apache Spark eliminates vendor lock-in concerns that could delay enterprise decisions.
“Many companies already have Snowflake data warehouses, so have a natural inclination to use their tools for AI initiatives,” Petrie noted. “Snowflake’s strength in data warehousing also gives it a leg up in AI initiatives because structured data remain the favorite input for AI/ML models.”
Context: Data vs. discretionary tech spending
The contrast with recent market signals is stark. Gartner’s warning about slowing enterprise technology purchases, combined with MIT research suggesting potential AI bubble conditions, had spooked investors about enterprise technology demand. Yet Snowflake’s results suggest a bifurcation in enterprise spending priorities.
Noel Yuhanna, VP and Principal Analyst at Forrester, sees this as validation of a broader trend. “Snowflake’s results reflect a broader trend: the data market is accelerating, driven by the growing demand for integrated, trusted, and AI-ready data,” Yuhanna told VentureBeat. “As organizations race to operationalize AI, they’re realizing that raw or siloed data isn’t enough. Data must be governed, high-quality, and accessible at scale.”
Market resilience despite AI skepticism
Industry analyst Sanjeev Mohan believes this resilience will persist despite potential corrections in the AI market.
“I am delighted to see Snowflake’s outstanding financial performance and not at all surprised,” Mohan told VentureBeat. “It underscores how enterprises are investing in ensuring that their data is accurate, precise, relevant, and consolidated in a single system.”
Mohan dismissed concerns that AI investment fatigue would affect data platforms.
“Yes, Gartner’s stock dipped as customers tightened discretionary spending,” he said. “But even if AI company growth cools, I believe Snowflake, Databricks, Google Cloud, hyperscalers and other mega vendors will continue to thrive.”
His reasoning reflects the fundamental shift in how enterprises view data infrastructure.
“If the gen AI frenzy has taught us anything, it’s this: without reliable data, there is no moat.”
Strategic implications for enterprise leaders
For technology decision-makers, Snowflake’s performance illuminates several critical trends.
Data infrastructure as competitive moat: Enterprises delaying data modernization risk falling behind competitors who are already deploying AI-powered workflows.
Integration over replacement: Rather than wholesale technology refreshes, successful enterprises are integrating AI capabilities into existing data platforms. This approach reduces risk and accelerates time-to-value.
Governance-first AI strategy: The emphasis on “AI-ready data” suggests that enterprises prioritizing data governance are better positioned for AI success. This means governed, high-quality, accessible datasets rather than raw or siloed information.
The divergence between general technology spending concerns and data platform investment growth creates both risks and opportunities for enterprise leaders. The broader lesson is clear. While some technology investments may face scrutiny in uncertain economic times, data infrastructure has transcended discretionary spending to become a fundamental enterprise capability. Companies that recognize this shift and invest accordingly will be positioned to capitalize on AI opportunities regardless of broader market conditions.
Source link
