Having great tech is only half the battle. To scale to a $70B+ valuation and execute the largest software IPO in history, Snowflake had to completely rewrite the Revenue Operations playbook. With their consumption-based pricing model, a signed contract doesn't guarantee revenue, only continuous product usage does. To solve this and build an unstoppable growth loop, Snowflake destroyed departmental silos. They united Sales, Marketing, and Customer Success around one single source of truth: Real-time data consumption metrics.
Snowflake's $70B Valuation: Scaling Revenue Operations with Real-time Data
This title was summarized by AI from the post below.
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We’ve renewed our Premier Partnership with Snowflake. Since 2017, we’ve been building with Snowflake as their first local partner. That matters. We’ve seen the platform evolve from early adoption to becoming the backbone of modern data stacks across Europe. And we’ve been in the trenches with our clients every step of the way; designing, building, fixing, and scaling what actually works. This renewal isn’t a milestone. It’s continuity. We stay close to the technology. We stay close to our clients. And we keep pushing for systems that deliver real business impact. On to the next phase.
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10 years ago, a small but mighty team at Snowflake came together to write a paper based on a simple idea: the best data platforms aren’t built by adding more—they’re built by removing what gets in the way. That idea became Snowflake. Not more tools. Not more layers. A system designed so data, workloads, and now AI can actually work together. Today, we’re honored to share that our founding Snowflake team has received the prestigious 2026 ACM SIGMOD/PODS “Test-of-Time” Award. It’s an opportunity to revisit the paper and reflect on how those early ideas shaped the company Snowflake is today — and where we're heading. Read more from our co-founders about how we got here and what comes next 👇 https://bit.ly/4c55S6s
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Exactly — that’s a key part of why this whole situation feels like a “tech rollercoaster.” Let me break it down step by step. 1️⃣ Bizarre earnings a year ago Around a year ago, Snowflake reported earnings that looked strong on the surface: • Revenue growth was impressive, driven by enterprise adoption of their cloud data platform. • Analysts were excited about AI-related revenue opportunities, which boosted optimism. But under the hood: • Some of the growth metrics were one-time or heavily backloaded, meaning future quarters wouldn’t necessarily match the hype. • Profit margins were being compressed by heavy investment in R&D and new features, which isn’t unusual for growth SaaS companies, but many investors ignored that. 2️⃣ Stock ran up on “hot air” The market reaction at the time was extreme: • Investors overreacted to the AI narrative and projected out Snowflake’s potential like it was guaranteed. • The stock price spiked, partially driven by sentiment and FOMO rather than underlying fundamentals. • That’s why now, when guidance or product nuances reveal slower adoption or margin pressure, the stock falls hard, even if revenue growth remains decent. 3️⃣ Connection to the lawsuit The lawsuit is essentially saying: “Investors were misled into believing the growth story was stronger and more predictable than it really was.” So in some sense, the “hot air” spike a year ago set the stage — expectations were inflated, and now reality is catching up, which makes the stock look weak and the legal risk more salient. If you want, I can map this into a timeline showing the spike, the bizarre earnings, and where the lawsuit fits in — it makes the whole pattern really clear. Do you want me to do that?
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Invest in a shiny new platform? Or a proven data solution? How do you even decide? DAS42 Principal Consultant Jeff Springer's test: Does it create meaningful, measurable impact on human productivity? "It has to increase productivity in a meaningful and measurable way. It has to impact people's lives." Don't get distracted by bells and whistles. Stick with real value. Snowflake Snowflake Partner Network #TechAdoption #Snowflake #EnterpriseAI
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Snowflake made their product better. Now they're getting sued for it. Not for a data breach. Not for fraud. For efficiency. They improved query performance and introduced tiered storage pricing. Customers used less compute. Revenue took a hit. Investors filed class action lawsuits claiming Snowflake hid the impact. A company did exactly what every product team is supposed to do. Made the product faster, cheaper, more efficient. And Wall Street's response was: you should have warned us your product getting better would hurt the numbers. This is the ugly side of consumption-based pricing that nobody warned you about in the "death of per-seat" discourse. Your incentives are backwards. Every product improvement that saves the customer money costs you revenue. Every optimization your engineering team ships is a headwind your growth team has to overcome. Per-seat pricing had problems, sure. But at least your product team and your revenue team were rowing the same direction. With consumption pricing, they're fundamentally at odds. I work at an AI company(ngram) where usage patterns shift constantly. The question that comes up a lot internally is: are we building for the metric the customer cares about, or the metric the investor cares about? Snowflake tried to do both and the market punished them for it. #Growth #SaaS #AI
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🚀 𝗛𝘂𝗴𝗲 𝗱𝗮𝘆 - 𝗬𝘂𝗸𝗶 𝗻𝗼𝘄 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 https://lnkd.in/dX6bpUZU 🚀 When we started building Yuki, - we focused on Snowflake. As we grew, we started hearing something new from customers: they didn’t just want optimization - they wanted flexibility. More teams today are running hybrid data stacks. More companies are actively avoiding vendor lock-in. The reality is simple: teams don’t want to commit to one data platform anymore. That’s why this milestone matters - Yuki now supports Google BigQuery! Huge shoutout to our entire engineering team - this was a massive effort, and I couldn’t be more proud! This is an important step toward something bigger: helping teams avoid vendor lock-in - while keeping their data infrastructure efficient, predictable, and scalable. And we’re just getting started.
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A little over a month ago, I wrapped up five years at Snowflake. What a five years it was. I had the privilege of building and leading the Customer Experience Engineering (CXE) team, surrounded by some of the brightest, most driven people I've ever worked with. Over the past year alone, the CXE team fundamentally reimagined how Snowflake's CX organization operates with AI, delivering substantial gains in customer self-service and efficiency. I couldn't be more proud of what we built together. But every great chapter eventually leads to a new one. Back in late February, I took the leap and began co-founding a company alongside my longtime friend and colleague Linden Hillenbrand. In the first week of March, we officially incorporated as CustOS AI (pronounced 'KOOS-tohs'). Our mission: helping enterprises transform their Customer Operations with AI and data. It's an intersection I've spent the last 15 years hyper-focused on within Cloudera and then Snowflake. I’m ready to bring this to the broader market. Since day one, we've been heads-down meeting with leaders across our networks, connecting with potential design partners, pressure-testing our thesis, and sharpening our thinking with every conversation. The energy has been incredible. If you’re interested in learning more, please don’t hesitate to reach out. While I know this journey won’t be easy, I genuinely believe we're working on a problem that can drive real impact for the market. This is just the beginning. More to come. 🚀
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Had a great morning at Snowflake's 'Data for Breakfast' - strong speakers and a topic that cuts to the heart of where enterprises are right now: how do you make AI actually work, not just work in demos? Snowflake's framing was solid: AI success requires AI in every workflow, business logic and context embedded in the platform, and a unified data foundation underneath it all. Hard to argue with that. What struck me most was a conversation afterward. Several attendees are already planning to build full DataOps platforms with agentic capabilities. Bold ambition. But the questions I'd want them pressure-testing first: → How long does it realistically take to build and maintain? → What's the TCO when you factor in reliability, governance, and iteration? → What happens when the business requirements change in month 8? After a long data journey, here's what genuinely excites me right now: for the first time, I think we can actually have it all. Enterprise-grade. Unified. And fast enough to match the pace of the business. That trifecta has always been the trade-off nobody could solve - until metadata-driven development changed the rules. With ADE and ADA, you're not rebuilding the foundation every time. The metadata layer gives data teams a governed, contextual understanding of the data estate from day one - delivering that elusive single version of truth alongside the agility the business has always asked for. The goal isn't to avoid building. It's to build smart - and get to business value before the window closes. This is exactly what our team is focused on. If it's a challenge you're navigating too, I'd love to talk - or if you'd rather start with some reading, swing by https://lnkd.in/dG27gJMg. Good event. The best ones always leave you with more questions than you arrived with. ☕ #DataStrategy #EnterpriseAI #Snowflake #Metadata #DataOps ❄️ Markus Salo Olli Ek Janne Viitala
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𝐘𝐨𝐮𝐫 𝐥𝐞𝐠𝐚𝐜𝐲 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐰𝐚𝐬 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐫𝐞𝐩𝐨𝐫𝐭𝐬. 𝐍𝐨𝐭 𝐟𝐨𝐫 𝐀𝐈. 𝐓𝐡𝐚𝐭 𝐠𝐚𝐩 𝐜𝐨𝐬𝐭𝐬 𝐲𝐨𝐮 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤. Andersen Lab is now a Snowflake Registered Technology Partner. 🤝 Here’s what changes for your team: 🔸 We migrate legacy warehouses to Snowflake. Structured, semi-structured, historical data – all of it. 🔸 Snowflake Cortex runs LLMs directly on your data. Nothing leaves your perimeter. 🔸 Role-based access, masking, and audit trails – built in, not bolted on. Siemens, AT&T, and Western Union run on Snowflake. Your competitors might too. The longer you wait, the further they pull ahead. What's the one thing your current stack can't do? #𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 #𝐃𝐚𝐭𝐚𝐌𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐀𝐧𝐝𝐞𝐫𝐬𝐞𝐧 #𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩 #𝐀𝐈𝐃𝐚𝐭𝐚
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