A Full Analysis of Snowflake ($SNOW)

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Hi everyone,

If you bought Snowflake six months ago, you probably were not feeling great about it. The stock spent most of the last year stuck in the penalty box, sitting down roughly 20% on the year heading into late May while the rest of the AI trade ran away from it. Then the company reported earnings, dropped a number nobody expected, and the stock ripped 37% in a single move. That is the kind of whiplash that makes you wonder whether you missed something or whether the crowd just got carried away. So let us actually dig in.

Stock Deep Dive: Snowflake (SNOW-US, $84B MCAP)

Snowflake is the company that made it almost stupidly easy for big organizations to store all their data in one place and ask questions of it without managing a single server. For years that was the whole pitch, and it was a good one. The catch is that the same consumption-based model that made the stock a darling on the way up also made it brutal on the way down, because when customers tightened their cloud budgets, Snowflake felt it almost immediately. Growth decelerated, the multiple compressed, and the bears declared the hyper-growth era over.

The Q1 fiscal 2027 print, reported in late May, told a different story. Product revenue grew 34%, an acceleration from the prior quarter rather than the continued slowdown everyone had modeled. Net revenue retention ticked up for the first time in five quarters. And management raised full-year guidance instead of trimming it. The reason, in management's telling, is AI: the same data that companies hoarded inside Snowflake is now the fuel for the AI agents they all want to build, and Snowflake is positioning itself as the governed, trusted layer that sits underneath all of it.

The question for an investor today is whether this is a genuine inflection or a one-quarter sugar high in a stock that still trades at a premium and still loses money on a GAAP basis. This issue walks through the business, the numbers, and the very real competitor sitting across the table.

• Why Now? 👉 A 37% Earnings Pop Just Confirmed the AI Story Is Finally Showing Up in the Numbers

• Overview 👉 How a Cloud Data Warehouse Became the Control Plane for Enterprise AI

• How Do They Make Money 👉 Consumption Billing Means They Only Win When Customers Use More

• By The Numbers 👉 34% Growth, 126% Retention, and a Credible Path to Real Profit

• Bonus Deep Dive 👉 The Databricks Problem, and Why Both Can Win (For Now)

• Risks 👉 What Could Break This Thesis

• Wrapping Up…

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Why Now? 👉 A 37% Earnings Pop Just Confirmed the AI Story Is Finally Showing Up in the Numbers

For most of the past year, Snowflake was a show-me story. The stock entered late May down about 20% year to date even as the S&P 500 climbed, and it had spent 2024 falling roughly 70% off its highs before clawing back. The bear thesis was simple and, for a while, correct: growth was decelerating toward 30%, large customers were optimizing their spend, and a fast-growing private rival was eating into the narrative.

Then Q1 fiscal 2027 landed and reset the conversation. Product revenue came in at $1.33B, up 34% year over year, which was an acceleration from the 30% posted the previous quarter and the strongest sequential dollar growth in the company's history. Adjusted earnings of $0.39 beat the $0.32 consensus by roughly 22%, extending a streak of beats to at least five straight quarters. Management raised its full-year product revenue target to $5.84B, implying 31% growth, up from a prior $5.66B that had implied just 27%. The market's reaction was violent: shares jumped 37%.

CEO Sridhar Ramaswamy called it “a milestone quarter” and pointed to the strongest sequential dollar growth the company has ever recorded. But the piece that mattered most was buried in a single metric: net revenue retention rose to 126% from 125%, the first uptick after five quarters of steady decline. That number is the clearest evidence yet that existing customers are spending more, not less, and management tied the turn directly to early AI adoption. The stock had been priced for a slow fade. Q1 said the opposite, and investors who had been starved for proof reacted accordingly.

Overview 👉 How a Cloud Data Warehouse Became the Control Plane for Enterprise AI

Snowflake started life as a cloud data warehouse, which is a fancy way of saying it gave companies one place to dump all their data and run queries on it without buying or babysitting any hardware. Its founding insight was to separate storage from compute, so a customer could store a mountain of data cheaply and only pay for processing power when they actually ran something. That elasticity, combined with the ability to run across all three major clouds rather than being locked into one, made it the default choice for thousands of enterprises.

The platform has since grown well beyond warehousing. Snowflake now sells across analytics, data engineering, AI, and application building, and it runs a marketplace where companies share and even monetize data sets with each other without the data ever leaving its governed environment. As of the most recent quarter the company served 13,912 total customers, including 813 of the Forbes Global 2000, and it added 616 net new customers in Q1 alone, up 38% from a year earlier. Customers with more than $1M in trailing 12-month product revenue grew to 779, with 46 crossing that threshold in the quarter versus 26 a year ago.

The current chapter is all about AI. Management has rebranded the whole thing the “AI Data Cloud” and is pushing a suite called Cortex, which lets customers run AI models directly on their governed Snowflake data without shipping it somewhere risky. Cortex Code, an AI coding agent, is already in use across more than 7,100 accounts, and Snowflake Intelligence, which lets business users query data in plain English, more than doubled quarter over quarter. More than 13,600 accounts are now touching Snowflake's AI features. The acquisition of Observe pushed the company into AI-powered observability, and partnerships with Anthropic, OpenAI, Google Cloud, and SAP give customers native access to leading models on top of their own data.

The pitch to the C-suite is clean: you already trust us with your data, so let us be the safe place where you build your AI agents on top of it. Snowflake's word for this is becoming the “control plane for the agentic enterprise.” Whether that branding sticks or not, the strategic logic is sound. Governed, unified data is exactly what enterprise AI needs, and that happens to be the thing Snowflake has spent a decade building.

How Do They Make Money 👉 Consumption Billing Means They Only Win When Customers Use More

This is the part that makes Snowflake different from your typical software company, and it cuts both ways. Snowflake does not sell seats. It charges for consumption, meaning customers pay for the compute and storage they actually use, billed against capacity contracts they commit to up front. When a customer runs more queries, trains more models, or onboards more teams, Snowflake's revenue rises automatically. There is no renewal negotiation required to grow an account; usage does the work.

The upside is obvious. In a good environment, revenue compounds as customers find new workloads, which is precisely what net revenue retention of 126% measures: the same cohort of customers spending 26% more than they did a year earlier. The downside is just as real. When companies cut cloud budgets, as they did across 2023 and 2024, Snowflake feels it almost in real time, with no multi-year seat licenses to cushion the blow. That sensitivity is why the stock trades so violently around the consumption trend, and why a single quarter of reacceleration moved the shares 37%.

A useful forward indicator here is remaining performance obligations, essentially the contracted revenue Snowflake has booked but not yet recognized. That figure stood at $9.21B at quarter end, up 38% year over year, and the company expects to recognize about half of it within the next twelve months. Roughly 90% of revenue is product; professional services are a small slice. The model is simple to describe and hard to fake: customers only spend more if the platform is genuinely useful to them.

By The Numbers 👉 34% Growth, 126% Retention, and a Credible Path to Real Profit

Let me lay out the financial picture as it stands today, the good and the awkward.

Growth is intact and reaccelerating. Q1 product revenue of $1.33B grew 34%, total revenue of $1.39B grew 33%, and full-year product revenue is now guided to $5.84B. For fiscal 2026 the company did $4.47B in product revenue, up 29%. Gross margins sit around 67%, healthy for a business that has to pay the underlying cloud providers for the infrastructure it resells.

Profitability is the bigger debate. On a non-GAAP basis the company is solidly profitable and improving: operating margin expanded about 300 basis points to 12% in Q1, and management lifted the full-year operating margin target to 13.5% from 12.5%. The cash generation is real, with non-GAAP free cash flow of roughly $1.12B in fiscal 2026 and operating cash flow north of $1.22B.

The catch is GAAP. Snowflake still posted a net loss of about $1.3B in fiscal 2026, and the single biggest reason is stock-based compensation, which has run above 40% of revenue essentially since the IPO and topped $1.5B in fiscal 2025. The company has been buying back stock, roughly $1.9B worth, to blunt the dilution, but this remains the cleanest line in the bear's argument. Trading around $238, against a 52-week range of $118 to $285, the stock is not cheap: it sits near 14x forward product revenue and a far higher multiple once you adjust free cash flow for stock comp.

Bonus Deep Dive 👉 The Databricks Problem, and Why Both Can Win (For Now)

You cannot evaluate Snowflake without talking about Databricks, because the two are now fighting over the same enterprise data budgets. Databricks is the private rival that closed a funding round in December 2025 at a $134B valuation, the largest private software valuation on record, and is widely expected to pursue a blockbuster IPO. Its annualized revenue is running around $5.4B, growing roughly 65%, which is more than double Snowflake's growth rate at a similar revenue scale.

The two companies came at the problem from opposite ends. Snowflake started with the SQL analyst and the clean, managed warehouse, then expanded toward data science and AI. Databricks started with data science, machine learning, and the messier “data lake” workloads, then expanded toward warehousing. They have been converging on each other's turf for years, and the public rhetoric has gotten sharp. The honest read is that each still wins its home segment: Snowflake takes the BI-heavy, mid-market, “give me something that just works” buyer, while Databricks dominates heavy machine learning and engineering-led teams.

So why can both win, at least for now? Because the market they are splitting is enormous and still growing double digits, and because most large enterprises run both rather than picking one. The risk to Snowflake is not that Databricks kills it; it is that Databricks keeps compounding at twice the rate, captures the most valuable AI workloads, and slowly grinds down Snowflake's premium multiple. The Q1 reacceleration is Snowflake's answer that it can defend its lane and monetize AI on its own terms. The AWS relationship reinforces the point: Snowflake committed to spend $6B on AWS infrastructure over five years, its largest such commitment to date, a sign it expects a lot more compute to flow through the platform, not less.

Risks 👉 What Could Break This Thesis

• Valuation leaves little room for error. At roughly 14x forward product revenue with no GAAP profit, any growth stumble could hit the stock hard, as it has before.

• Stock-based comp is heavy. Running above 40% of revenue, it keeps GAAP losses elevated and dilutes shareholders, even with buybacks near $1.9B.

• Consumption billing cuts both ways. A broad pullback in enterprise cloud spending would show up in revenue almost immediately, with no seat licenses to cushion it.

• Databricks is the better grower. It is expanding roughly twice as fast at similar scale and may take the most valuable AI workloads, pressuring both Snowflake's growth and its multiple.

• The hyperscalers compete too. AWS, Microsoft, and Google all offer rival data products and can lean on bundling, even as Snowflake partners with them.

• The AI narrative is still early. If Cortex and Snowflake Intelligence adoption does not convert into durable spending, the reacceleration could prove temporary.

Wrapping Up…

Snowflake spent the last year as the AI stock the AI trade forgot. Q1 was the quarter that started to change that, and the 37% move tells you just how starved investors were for evidence that the consumption engine had turned back on. The reacceleration is real, retention is improving, the AI products are gaining genuine traction, and the company is throwing off over $1B in free cash flow.

But this is not a cheap, sleepy compounder. It is a premium-priced, still-GAAP-unprofitable growth name facing the best-funded private competitor in software history. The bull case is that AI gives Snowflake a second growth act and the multiple holds. The bear case is that Databricks and the hyperscalers grind it down while stock comp keeps the bottom line red. After a 37% pop, you are no longer buying it at the bottom, so the real question is whether you believe the inflection has legs. Watch net revenue retention and AI consumption over the next two quarters; those numbers will settle the debate faster than any analyst note.

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Cheers,

The GRIT Alpha Team

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