Skip to content 
It’s August 28, 2025. The markets open, and within hours, a single stock rises about 20%. Not a flashy EV startup. Not a crypto exchange. Not even a ChatGPT rival.
It’s Snowflake, a “boring” database company that many people assumed just stored data in the cloud.
But here’s the plot twist that seems to have Wall Street buzzing: this database company is increasingly being described as a potential backbone for AI applications. And the transformation story appears absolutely fascinating.
What Exactly Does Snowflake Do?
Let’s start with the basics, because understanding Snowflake’s business is key to understanding why everyone seems to be talking about its AI potential.
Snowflake is a cloud-based data platform that enables data analysis and simultaneous access of data sets with minimal latency, operating on Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
What makes it distinct is that while most cloud platforms perform analytical computation and storage-related tasks on the same resources, Snowflake’s architecture separates computational load from storage load.
Think of traditional databases like a small restaurant where the kitchen, dining room, and storage are all cramped into one space. If you get a rush of customers, everything slows down because they’re all competing for the same resources.
Snowflake is more like a modern restaurant where the kitchen can scale independently from the dining room, and you can add more chefs without worrying about seating capacity. This flexibility is one of the reasons enterprise customers reportedly embraced it.
Another factor is Snowflake’s consumption-based pricing model, which allows customers to pay only for what they use. This provides flexibility and control to scale up or down with demand, while giving clearer visibility into usage and spend.
The Accidental AI Revolution
When Snowflake went public in 2020, it was often referred to as the “cloud data warehouse that just works.” Companies appreciated it because it solved the age-old problem of storing massive amounts of data without overwhelming budgets or IT teams.
Then the AI boom hit, and something unexpected occurred.
Every company trying to build AI applications ran into the same problem: their data was messy. AI models are incredibly dependent on clean, well-organized, and accessible data. Traditional databases often struggle to handle the volume and variety of data that modern AI requires.
Snowflake had, perhaps unintentionally, built infrastructure that appeared well-suited for the AI era.
The Numbers That Made Wall Street Take Notice
Snowflake’s latest quarter showed more than just strong growth, it suggested a business in transition.
- Revenue: Product sales rose 32% year-over-year to $1.09 billion. Total revenue reached $1.14 billion, reportedly topping analyst estimates by more than 5%.
- AI traction: More than 6,100 customers are said to be using Snowflake AI each week, up 17% from the prior quarter. Roughly half of new customer deals are linked to AI projects, and a quarter of all deployed use cases reportedly now include AI.
- Core business: Net revenue retention stood at 125%, the number of customers paying over $1 million annually climbed to 654, and the company’s non-GAAP operating margin expanded to 11%.
For investors, the numbers matter. But what many observers find notable is the shift they suggest: Snowflake may be generating an increasing share of its business from AI-driven applications, not just storage.
The Consumption Model Goldmine
Snowflake’s business model appears increasingly aligned with AI workloads. Customers are charged based on consumption, credits are spent only when resources are being used, whether running a virtual warehouse, processing through the cloud services layer, or tapping into serverless features.
AI workloads tend to run in short, heavy bursts. They are growing quickly as companies roll out more models. And once enterprises build AI applications on Snowflake, moving away could become more difficult.
That pattern is already visible. Reports note that companies like Thomson Reuters and BlackRock are using Snowflake’s Cortex AI platform to build agents and client tools. Each query, training run, and insight adds to consumption, making Snowflake’s platform function somewhat like a toll road for enterprise AI.
The Competitive Battle
Snowflake is not alone. Competition is active across multiple fronts:
- Database incumbents: Amazon Redshift, Google BigQuery, Microsoft Azure Synapse
- Direct AI rival: Databricks, valued at about $43 billion, pushing AI-first workflows
- Platform challengers: ServiceNow and Salesforce, both expanding AI into their enterprise suites
Analysts suggest Snowflake’s advantage may lie in its starting point. Rivals are building AI tools that depend on data. Snowflake built the data platform first, then layered AI on top. This makes the story less about shiny features and more about infrastructure.
The CEO Who Saw the Shift
CEO Srinivas Ramaswamy placed an early bet that AI would not be a side feature but a fundamental shift in how companies operate. Rather than focusing on demos, he emphasized reliability. Reports indicate that while large models like GPT-4 may deliver only around 45% reliability in enterprise settings, Snowflake’s AI applications have been cited as hitting above 90%.
This bet appeared to gain visibility this year when Sam Altman attended Snowflake’s 2025 Summit. For many observers, the OpenAI chief’s presence suggested that Snowflake’s platform may matter even to the company most closely associated with the AI boom.
The Reality Check
The story also carries risks:
- Valuation: The stock trades around 15 times projected FY26 revenue. Some analysts argue it may need a 20 to 30 percent pullback to look more attractive.
- Competition: Amazon, Google, and Microsoft have both the resources and incentives to push back.
- Execution: While margins hit 11% in Q2, guidance suggests some erosion could occur as the business scales.
- Market sensitivity: Like many high-growth tech names, Snowflake’s stock remains vulnerable to broader market shifts.
Snowflake may have positioned itself as part of the backbone of enterprise AI. The open question is whether that position can withstand both expectations and competition.
The $450 Potential
Some analysts have modeled scenarios where Snowflake’s stock could approach $450 per share, roughly doubling from current levels.
Here’s how the math is presented: If revenues grow at 33% annually over the next three years, driven by AI workloads, Snowflake could reach $8.8 billion in annual revenue by FY2028. If the company achieved 20% net margins by then, that would imply $1.76 billion in net income.
At an 80x earnings multiple, considered high but not unheard of for fast-growing infrastructure companies, that suggests a $140 billion market cap, nearly double the current valuation.
These projections are scenarios, not guarantees, and depend on assumptions about growth, margins, and market conditions.
So, Is This the AI Bet?
Here’s the balanced view: Snowflake is not simply betting on AI, it is being positioned as infrastructure that helps other companies’ AI bets work.
While many competitors focus on applications, Snowflake provides the foundation those applications need to run at scale. It is often likened less to the miners in a gold rush and more to the company selling the picks and shovels.
The AI market is projected to grow from $170 billion to $355 billion, and Snowflake is positioning itself in the middle of that expansion.
The Bottom Line
If AI does transform business operations, as many analysts suggest, then companies that help make AI work reliably, such as Snowflake, may play a significant role.
Still, even strong infrastructure companies face competition, valuation concerns, and execution challenges. Sometimes, though, in the middle of a technological revolution, it is the infrastructure players that become some of the most closely watched names.
