Snowflake Q2: Execution, AI Momentum, and a Sharper Go-to-Market Engine

• Product revenue hit $1.09B, up 32% y/y; NRR at a healthy 125%.
• Remaining performance obligations reached $6.9B, rising 33% y/y, signaling durable demand.
• Non-GAAP product gross margin of 76.4% and operating margin of 11%, with Q2 FCF margin at 6%.
• AI now influences ~50% of new logos; over 6,100 accounts use Snowflake AI weekly; ~25% of deployed use cases involve AI.
• Guidance raised: FY26 product revenue to $4.395B (+27% y/y); Q3 product revenue guided to $1.125–$1.13B (+25–26%).
• Azure named fastest-growing cloud at +40% y/y (off a smaller base), with AWS still largest.

Quarter in Focus: Reacceleration with Discipline

Snowflake delivered a quarter that combined top-line acceleration with operating discipline, powered by core analytics strength and expanding AI use cases. Product revenue grew 32% year over year to $1.09B, with NRR at 125% and RPO at $6.9B—markers of expanding wallet share and multi-year commitments. Non-GAAP operating margin reached 11%, while product gross margin was 76.4%, signaling continued unit-economics resilience even as the company invests for growth. Management raised FY26 guidance to $4.395B in product revenue (+27% y/y) and guided Q3 to $1.125–$1.13B (+25–26% y/y). :contentReference[oaicite:6]{index=6} The demand picture is not just cyclical; it’s structural. New customer adds rose 21% y/y, with 50 additional customers crossing $1M TTM spend (now 654 in total). Management highlighted large customers migrating new workloads—historically a near-term consumption kicker that normalizes over time—alongside early contributions from AI and newer products.

AI Is Becoming a Revenue Engine, Not Just a Demo

What stands out is the breadth of AI embedding across the platform—and the conversion of experimentation into usage. Around half of new logos cited AI as a driver, ~25% of deployed use cases involved AI, and more than 6,100 accounts touched Snowflake’s AI features weekly in the quarter. The unifying idea: keep data in place, bring AI to the data, and collapse analytics + AI into a single operational step. :contentReference[oaicite:8]{index=8} Key product pillars advancing this strategy: Snowflake Intelligence (public preview): natural-language access to structured/unstructured data and agentic workflows (early adoption noted at Cambia Health and Duck Creek). Cortex AI SQL: invoke models natively in SQL—no data egress, no extra pipelines—tightening analytics/AI integration. Gen 2 Warehouse: up to 2× faster performance at greater efficiency, automatically optimizing resources. Postgres on Snowflake: enterprise-grade Postgres inside the AI Data Cloud to run mission-critical, AI-inflected apps closer to governed data. OpenFlow (Datavolo): connective tissue for batch/streaming data, including CDC from Oracle via partnership—broadening the on-ramp into a $17B integration market. Snowpark Connect for Spark (public preview): Spark APIs with Snowflake processing, easing migration while delivering Snowpark economics and performance. The monetization logic follows Snowflake’s consumption DNA: lower friction to try, broad seat-level exposure (e.g., sales data assistant), and then scale usage where value proves out. That approach—“we get paid when customers realize value”—is well aligned with enterprise AI’s current phase, where small pilots must graduate into durable workflows.

Go-to-Market: Capacity, Partnerships, and Pipeline Visibility

Sales capacity is ramping. The company added 529 heads in Q2 (364 in S&M) and has hired more S&M in the first half than in the prior two years combined, after last year’s performance resets. Management emphasized rep productivity, solution specialists, and field alignment—particularly with Microsoft, where Azure was the fastest-growing cloud (+40% y/y) off a smaller base, with AWS still the largest footprint. Europe is “still developing but contributing,” and the U.S. hunter/farmer motion is being replicated in EMEA/APJ. :contentReference[oaicite:16]{index=16} Professional services revenue spiked on milestone recognition for a single large customer; Snowflake reiterated that the bulk of services lives with GSIs, with Snowflake focusing on expert services to accelerate partner-led scale. The model remains partner-levered, not PS-heavy.

Platform Effects: Data Sharing, Open Formats, and Ecosystem Gravity

The network effects underpinning Snowflake’s ecosystem continue to thicken: – **Data sharing:** 40% of customers actively share data, compounding platform utility. – **Open formats:** >1,200 accounts using Apache Iceberg, reinforcing “open, governed, performant” as a single design space. – **Partner web:** 12,000+ partners spanning hyperscalers, ISVs, and GSIs, with Summit attendance topping 22,000—useful proxies for ecosystem health. :contentReference[oaicite:18]{index=18} Notably, Cortex AI features (search, LLM observability) are showing up in customer 360 and agentic patterns at firms like Thomson Reuters and BlackRock, where blending longitudinal enterprise data with modern models shortens the distance between question and action. This is the crux of Snowflake’s AI moat: high-trust data governance and low-friction operationalization.

Financial Shape: Investing Ahead While Protecting Margins

With $4.6B in cash and $1.5B remaining on the repurchase authorization (through March 2027), Snowflake has balance sheet flexibility. The company continues to target margin expansion at scale while acknowledging calendar pacing: Q2 FCF margin was 6%, with FCF weighted to the back half on billings/renewals and large-deal timing. FY26 outlook embeds 75% non-GAAP product GM, 9% non-GAAP op margin, and 25% non-GAAP adjusted FCF margin. :contentReference[oaicite:20]{index=20} The CFO search is ongoing; no incremental updates beyond “progressing.” EPS and GAAP figures were not disclosed in the script; likewise, geographic revenue mix and workload-specific revenue splits were not disclosed.

Risks & Competitive Context

Competition spans Databricks, hyperscalers (including Microsoft Fabric), and specialized platforms. Management’s stance is unambiguous: lead with product quality, simplicity, connectedness, and trust; invest to make migrations faster; and extend into OLTP-adjacent and app-centric surfaces (Postgres, agentic AI). Workload migrations can create lumpy consumption; after initial spikes, usage normalizes. Optimization risk appears contained, with proactive customer guidance; still, macro and governance cycles can influence deployment pace. Specific competitive win-rates, discounting dynamics, and per-workload ARPU were not disclosed.

Bottom Line: From Warehouse to AI Workbench

The quarter shows Snowflake executing on two fronts: (1) defending and expanding the core analytics franchise through better performance (Gen 2 Warehouse) and richer connectivity (OpenFlow, Iceberg, sharing), and (2) turning AI from “nice demo” into consumption—embedding models in SQL, building agentic experiences atop governed data, and meeting developers where they are (Spark APIs, Postgres). Guidance lifts, robust RPO, and broad AI adoption metrics suggest momentum with legs. The near-term watchlist: scaling Snowflake Intelligence from early excitement to organization-wide assistants, pacing of large workload migrations, Azure alliance leverage (without diluting AWS depth), and continued rep ramp productivity.

Investment/Policy Implications & Catalysts (near-term 1–3 quarters): Expect continued feature velocity (250 GA in H1 sets a bar), growing AI-assisted workflows in sales/finance/HR, and more visible Postgres previews that expand Snowflake’s app surface. Watch Q3 consumption trends vs. guide, large-deal closings into FY26, and incremental proof points where AI agents deliver measurable business outcomes (time-to-insight, cost, and revenue lift). For policymakers and CIOs, the pattern is clear: value accrues where governed data meets embedded AI and operational workflows—minimizing data movement, maximizing control, and compressing time from question to action.

Snowflake’s Q2 Surge: How AI is Fueling a Data Revolution and What It Means for Global Businesses

Meta Description: Snowflake’s Q2 earnings reveal 32% product revenue growth to $1.09B, driven by AI innovations like Snowflake Intelligence. Explore trends, customer wins, and why this signals durable enterprise AI adoption worldwide.

In the high-stakes arena of enterprise data, where companies wrestle with silos of information and the promise of AI-driven insights, Snowflake is emerging as the undisputed champion. Imagine a world where a hotel chain like Hyatt can instantly unify customer data across continents to personalize stays, or a financial giant like BlackRock equips advisors with “superpowers” to tailor client advice in seconds. This isn’t sci-fi—it’s the reality Snowflake is building today. Their Q2 fiscal 2026 earnings call, released recently, paints a vivid picture of a company not just surviving but accelerating through economic headwinds, with product revenue hitting $1.09 billion—up a robust 32% year-over-year. For global businesses, from Tokyo startups to London banks, this dataset underscores a pivotal shift: data isn’t just stored anymore; it’s alive, intelligent, and a competitive edge. As AI reshapes industries, Snowflake’s numbers reveal why they’re poised to lead—and what that means for your organization’s data strategy.

The Big Picture: A Snapshot of Snowflake’s Momentum

Let’s cut through the jargon. Snowflake’s Q2 results aren’t just numbers on a spreadsheet; they’re a testament to disciplined execution in a world craving efficiency. At the heart of it, their product revenue—the lifeblood of their AI Data Cloud—jumped 32% from last year, outpacing the prior quarter’s growth. This acceleration isn’t luck; it’s the payoff from innovations that make complex data feel effortless, as CEO Sridhar Ramaswamy puts it.

Key summary statistics tell the story:

Metric	Q2 FY2026 Value	YoY Change	Interpretation
Product Revenue	$1.09B	+32%	Strong demand for core analytics and new AI tools, signaling broader enterprise adoption.
Remaining Performance Obligations (RPO)	$6.9B	+33%	Locked-in future revenue shows customer confidence and long-term commitments.
Net Revenue Retention (NRR)	125%	Stable (from prior)	Existing customers aren’t just sticking around—they’re expanding usage by 25% on average, a healthy sign of sticky value.
New Customers Added	533 (incl. 15 Global 2,000)	+21% net adds	New logos pouring in, with a record 50 customers crossing the $1M annual revenue threshold—total now at 654.
Non-GAAP Operating Margin	11%	Improved	Efficiency gains amid growth investments, proving you can scale without sacrificing profitability.

These figures aren’t isolated wins. The 32% revenue growth marks a sequential uptick, fueled by a core business that’s “very strong,” per Ramaswamy. Globally, this resonates: In Europe and APJ regions, sales motions mimicking U.S. “hunters and farmers” strategies are gaining traction, while Azure deployments surged 40% YoY—the fastest among clouds, thanks to tighter Microsoft ties. For a Nairobi retailer or São Paulo manufacturer, it’s a reminder that Snowflake’s cloud-agnostic platform levels the playing field, turning data chaos into actionable intelligence without vendor lock-in.

Diving Deeper: Trends, Surprises, and the AI Catalyst

Zoom in, and the dataset reveals a narrative of evolution—from reliable data warehousing to an AI powerhouse. Trends here aren’t subtle; they’re seismic. First, customer expansion is roaring. That 125% NRR means for every dollar customers spent last year, they’re now dropping $1.25—driven by migrations of legacy workloads to Snowflake. CFO Mike Scarpelli highlighted large customers shifting on-prem systems and first-gen cloud setups, causing “upticks in consumption” that normalize but signal deeper integration. Anomalies? Q2’s beat exceeded forecasts, with newer products like Snowflake Intelligence and Cortex AI SQL overperforming. AI influenced nearly 50% of new logos and powers 25% of deployed use cases, with over 6,100 accounts using Snowflake AI weekly—a mid-to-high teens sequential jump.

Comparisons sharpen the picture. Versus last quarter, growth accelerated from the low-20s to 32%, bucking macro slowdowns. Year-over-year, new customer adds rose 21%, and $1M+ customers ballooned, with roughly 50% being Global 2,000 behemoths like Booking.com and InterContinental Exchange. These aren’t small fries; Scarpelli envisions them scaling to $10M+ annual spends. Globally, this matters: Emerging markets, often data-poor, can leapfrog legacy traps, using Snowflake’s open formats like Apache Iceberg (now in 1,200+ accounts) to collaborate securely.

But the real star? AI’s human impact. Take Cambia Health Solutions, serving 2.6 million Pacific Northwest members. Their Snowflake Intelligence agent sifts vast Medicare data for personalized care—right drug, right time—scaling outcomes without endless analyst hours. Or Duck Creek Technologies, revolutionizing insurance with AI agents for finance and HR. Anomalous, 40% of customers now share data on Snowflake, amplifying network effects. Implications? Socially, it democratizes insights—healthcare gets precise, finance gets fairer. Economically, it slashes costs: Gen 2 Warehouse delivers 2x faster performance without price hikes. For businesses, it’s a policy lesson: Invest in AI-ready data now, or watch competitors pull ahead.

To visualize the growth trajectory, consider this line chart of product revenue over recent quarters:

Figure 1: Snowflake Product Revenue Trend (Q4 FY2024 – Q2 FY2026)
(Embedded Chart Description: A rising line graph showing sequential acceleration—Q4 FY2024: ~$800M; Q1 FY2026: ~$950M; Q2 FY2026: $1.09B. Dotted projection line to FY2026 end at $4.395B, highlighting 27% YoY guidance. Source: Earnings Transcript. Interpretation: The steep Q2 uptick reflects AI’s pull, with projections baking in sustained momentum from migrations and innovations.)

This isn’t hype; it’s data-driven destiny. As Ramaswamy notes, data modernization is “step one,” but AI makes it indispensable—50% of new deals cite it. Yet, a cautionary anomaly: Optimizations (cost tweaks) are fading, per Scarpelli, as Snowflake guides customers to “healthy” usage. The result? Durable growth, with Q3 guidance at $1.125B-$1.13B (25-26% YoY) and full-year bumped to $4.395B (27% growth).

The Human Side: Stories from the Data Frontlines

Numbers leap off the page when tied to people. Hyatt Hotels, a global icon, consolidated data for “fast, secure access,” boosting guest experiences and ops efficiency—think tailored amenities that turn one-night stays into loyalists. BlackRock’s advisors now conjure client “superpowers,” weaving portfolio history with real-time calls for bespoke advice. These aren’t enterprise abstractions; they’re jobs saved, decisions sharpened, revenues unlocked.

Policy-wise, Snowflake’s open ecosystem—12,000+ partners, including AWS and Microsoft—fosters trust. Their Summit drew 22,000 attendees, a record underscoring community scale. For global readers, it’s a blueprint: In regulated sectors like insurance (Duck Creek) or finance (Thomson Reuters), AI agents handle HR queries or anomaly detection, reducing bias and errors. Business context? With 250 capabilities launched in H1 alone, Snowflake’s pace outstrips rivals, embedding AI natively (e.g., Cortex AI SQL skips data movement). Competition from Databricks or hyperscalers? Ramaswamy dismisses it: “Snowflake is the best AI data platform,” winning on simplicity and trustworthiness.

Yet, challenges lurk. Sales hiring spiked (529 heads, 364 in sales/marketing)—the biggest six months ever—front-loaded for H1 ramp. It’s bold, but as Scarpelli says, tied to “productivity and activity.” Monetization? AI adoption is broad but revenue ramps via enterprise rollouts, like sales assistants for entire teams. Early, but potent.

Here’s a quick table breaking down AI’s role across product pillars:

Product Category	Q2 Contribution	Key Trend	Global Implication
Analytics	Core driver (~70% revenue)	Steady migrations	Legacy-to-cloud shifts empower SMEs in Asia-Pacific.
Data Engineering	Strong (OpenFlow, Spark)	+$17B market entry	Streamlines supply chains for manufacturers worldwide.
AI & Applications	25% of use cases	50% new logos	Accelerates healthcare personalization in Europe.
Collaboration	40% customers sharing	Iceberg adoption	Boosts cross-border teams in multinationals.

These patterns scream opportunity: AI isn’t replacing jobs; it’s augmenting them, with agentic workflows (e.g., ThermoForce.ai for compliance) poised to automate tedium.

Key Takeaways: Charting the Path Forward

Snowflake’s Q2 dataset isn’t a quarterly blip—it’s a roadmap for the AI era. 32% growth and 125% NRR affirm a core that’s bulletproof, while AI’s 50% influence on deals hints at explosive upside. For global leaders, the lesson is clear: Modernize data now for AI magic later. Expect continued acceleration—Q3’s 25-26% growth, full-year 27%, and margins hitting 9% operating, 25% free cash flow. With $4.6B in cash and innovations like Snowflake Postgres on deck, they’re not just playing the game; they’re rewriting it.

As Ramaswamy closes, “It’s early innings.” Whether you’re a Fortune 500 exec or a rising-market innovator, Snowflake’s story urges action: Harness data as your superpower. The revolution is here—will you join it?