SNOW · Q2 2026 Earnings

Bullish

Snowflake

Reported August 27, 2025

30-second summary

Snowflake beat its own Q2 product revenue guide by ~$50M ($1.09B vs. $1.035–1.04B guided) and posted 32% YoY product growth — an acceleration from Q1's 26%. Management raised FY26 product revenue to $4.395B (+27% YoY) and lifted FY operating margin to 9%. The Q3 guide of $1.125–1.130B implies 25–26% YoY, in line with Snowflake's historical sandbag pattern — Q2 was guided to 25% and printed 32%, and Mike noted the company has "consistently been raising by the beat plus more for the last six quarters." NRR held at 125%, AI is now influencing ~50% of new logos, and Azure grew 40% — the underlying narrative is intact and strengthening.

Headline numbers

EPS

Q2 FY2026

$0.35

Revenue

Q2 FY2026

$1.15B

+32.0% YoY

Gross margin

Q2 FY2026

73.0%

Free cash flow

Q2 FY2026

$0.06B

Operating margin

Q2 FY2026

11.0%

Key financials

Q2 FY2026

Metric	Q2 FY2026	YoY	Q1 FY2026	QoQ
Revenue	$1.15B	+32.0%	$1.04B	+9.9%
EPS	$0.35	—	$0.24	+45.8%
Gross margin	73.0%	—	72.0%	+100bps
Operating margin	11.0%	—	9.0%	+200bps
Free cash flow	$0.06B	—	$0.18B	-68.3%

Guidance

Snowflake raised FY2026 product revenue guidance to $4.395B (27% YoY) and operating margin to 9%, driven by Q2 beat on revenue ($1.09B vs. $1.035–$1.04B guided) and operating margin (11% vs. 8% guided).

Guidance is issued for both next quarter and the full year. Both may appear below.

Actuals vs prior guidance

Metric	Period	Prior guide	Actual	Δ	Result
Product Revenue	Q2 FY2026	$1.035 - $1.04 billion	$1.09 billion	+$0.05-0.055 billion above guide	Beat
Product Revenue YoY Growth	Q2 FY2026	25%	32%	+7 percentage points above guide	Beat
Non-GAAP Operating Margin	Q2 FY2026	8%	11%	+3 percentage points above guide	Beat

New guidance

Metric	Period	Guide	YoY
Product Revenue	Q3 FY2026	$1.125 - $1.130 billion	25-26%
Non-GAAP Operating Margin	Q3 FY2026	9%	—

Changes to prior guidance

Metric	Period	Prior guide	New guide	Δ	Result
Product Revenue	FY2026	$4.325 billion	$4.395 billion	+$0.07 billion (+1.6%)	Raised
Product Revenue YoY Growth	FY2026	25%	27%	+2 percentage points	Raised
Non-GAAP Operating Margin	FY2026	8%	9%	+1 percentage point	Raised

Reaffirmed unchanged this quarter: Non-GAAP Product Gross Margin (75%), Non-GAAP Adjusted Free Cash Flow Margin (25%)

Segment performance

Q2 FY2026

Segment	Q2 FY2026	YoY
Product Revenue	$1.09B	+32.0%

Platform metrics

Q2 FY2026

Segment	Q2 FY2026
Net Revenue Retention Rate	125%
Customers with Trailing 12-Month Product Revenue > $1M	654
Customers with Trailing 12-Month Product Revenue > $1M YoY Growth	30%
Forbes Global 2000 Customers	751
Forbes Global 2000 Customers YoY Growth	5%
Remaining Performance Obligations	$6.9B
Remaining Performance Obligations YoY Growth	33%

Profitability

Q2 FY2026

Segment	Q2 FY2026
Non-GAAP Operating Margin	11%

Management tone

Q1 FY2026 anchor → Q2 FY2026 anchor: "AI as foundational pillar" → "AI as logo-acquisition engine."

AI moved from infrastructure positioning to acquisition driver. Last quarter Sridhar framed Cortex as a "foundational pillar of enterprise AI strategies." This quarter the framing has hardened into a quantified go-to-market claim: AI is now influencing ~50% of new logos won, ~25% of deployed use cases involve AI, and 6,100+ accounts use Snowflake AI weekly. The Q&A answer to Wolf Research's Alex Zukin — that customers are funding AI projects with separate budgets when data already lives on Snowflake — is the new substantive claim. If true, this is incremental TAM, not reallocated spend.

The competitive narrative shifted from "data warehouse" to "best AI data platform." Sridhar's verbatim claim in the Citi exchange — that Snowflake is "the best AI data platform" — is a more assertive positioning than the Q1 "end-to-end data lifecycle" framing. The Postgres (Snowflake Postgres, built on the Crunchy acquisition), OpenFlow, and Snowpark Connect for Spark callouts indicate the product surface is widening specifically to compete on AI workloads, not just Databricks' lakehouse turf.

The growth-vs-AI false choice was rejected directly. Where Q1 messaging implied AI was the next leg of growth, this quarter management is explicit that core analytics strength (evidenced by the 125% NRR) and AI uptake are both contributing. Sridhar repeatedly anchored answers in the strength of the core analytics business while citing the ~25% AI use-case figure as evidence of incremental layering. This weakens the bear thesis that Q2's acceleration is one-time AI budget allocation rather than durable consumption growth.

Sales capacity build is the loudest non-AI signal. Mike disclosed that S&M hiring in H1 FY26 exceeded the prior two years combined on a net basis, and emphasized that performance management in H2 FY25 cleared the deck. The implication is that pipeline strength supports the build, with productivity gates in place.

Q&A highlights

Sanjit Singh · Morgan Stanley

On data modernization as a priority for Fortune 500/Global 2000 companies: is this a durable growth driver or primarily a one-time migration benefit from legacy systems?

Sridhar emphasized that data modernization is the beginning of a longer journey. Legacy system migration to Snowflake is step one, but the real value comes from AI transformation. Customers are increasingly realizing that data modernization is critical for AI readiness, positioning Snowflake as the destination for AI-ready data. This extends beyond migration to applications and agentic AI use cases.

Data modernization journey more important than before due to AI transformation needsAI influences nearly 50% of new logos won in Q225% of deployed use cases involve AIOver 6,100 accounts using Snowflake AI weekly

Carl Kirsten · UBS

Was Azure growth outperformance a result of unique execution with Microsoft/Azure customers, or was the outperformance fairly distributed across cloud providers?

Sridhar and Christian reported Azure as the fastest-growing cloud at 40% YoY growth (though off a lower base than AWS). Drivers included better field alignment with Microsoft, Microsoft's strength in EMEA, and deeper collaboration at infrastructure (OneLake), product (Office Copilot, Power BI), and go-to-market levels. AWS remains the largest platform.

Azure grew 40% year-over-yearAWS still largest cloud platformCollaboration at infrastructure, product, and go-to-market levelsEMEA showing strong uptick with large accounts

Alex Zukin · Wolf Research

Was Q2 outperformance driven by normalization of the demand environment and improved execution, or by incremental AI budget allocation and new product uptake? How sustainable is this uplift?

Sridhar and Mike attributed acceleration to core analytics business strength (evidenced by 125% NRR) combined with new AI budget allocation from large customers. AI projects are increasingly funded separately, particularly when data already resides on Snowflake due to ease of use, governance, and trustworthiness. Close to 25% of deployed use cases involve AI. Forecast models are expected to increasingly incorporate these workloads.

Core analytics business remains strong foundationNRR at 125%Large customers allocating budgets specifically for AI projects25% of deployed use cases involve AI

Tyler Rack · Citi

In conversations with million-dollar customers, how are they bucketing and thinking about different platforms (Databricks, Fabric, Palantir, etc.)? Is there less confusion now, helping Snowflake unlock higher velocity?

Sridhar stated Snowflake is the best AI data platform and stands out due to ease of use, connectedness (no data silos), and trustworthiness (reduced hallucinations, governance). While some customers may prefer other platforms in specific areas, Snowflake's strength in core analytics and expanding portfolio (Postgres, OpenFlow, Spark support) drive broad customer confidence. Acceleration across new customers and consumption reflects this positioning.

Snowflake recognized as best AI data platformProduct qualities: ease of use, connectedness, trustworthinessNew offerings: Postgres, OpenFlow, Spark supportAcceleration in new customers, existing customer consumption, and AI adoption

John DeFucci · Guggenheim

Q2 results are strong but driven primarily by core data warehouse/analytics business. Is this core market sustainable as a growth driver, given the massive on-prem opportunity? Are there solutions that could disrupt Snowflake's position as it disrupted on-prem?

Sridhar emphasized it's not either-or: the core business is strong (evidenced by NRR) while AI investments are critical for future value delivery. There is substantial on-prem legacy infrastructure still to migrate, and all cloud players are benefiting. However, Snowflake must innovate on both fronts; AI could disrupt anyone (including Snowflake), so sustained product innovation and migration technology advancement are essential. The long-term runway depends on innovation velocity.

Core analytics business continues to be very strongNRR measured over two-year timeframe supports durabilitySubstantial on-prem systems to migrateAI could disrupt any vendor, including Snowflake

Answers to last quarter's watch list

Whether NRR breaks back above 125% — Held at 125%. Mike attributed the print to existing large customers migrating new workloads. Not a decisive break above 125%, but the directional concern from Q1 is neutralized.

Resolved positively

Q2 FY26 product revenue vs. the $1.035–1.040B guide — Decisive beat. Product revenue came in at $1.09B, ~$50M above the high end of the guide, with growth accelerating to 32% YoY vs. the 25% guided. The FY raise to $4.395B confirms management views the beat as at least partially sustainable.

Resolved positively

Cortex monetization disclosure — Not resolved. Management still does not break out AI-attributable revenue; the disclosure has shifted to influence metrics (50% of new logos, 25% of use cases, 6,100+ weekly AI accounts) rather than dollars. Bulls who want a quantified AI contribution still cannot get one.

Continue monitoring

Operating margin trajectory — Margin printed 11% in Q2, and the FY guide was raised to 9% from 8%. Operating leverage is real even as hiring continues.

Resolved positively

RPO growth rate next quarter — RPO grew 33% YoY to $6.9B, ahead of the 32% product revenue growth rate. Bookings momentum supports the FY raise.

Resolved positively

What to watch into next quarter

Whether Q3 product revenue beats the $1.125–1.130B guide by a comparable margin to Q2's ~$50M beat — given Mike's explicit "beat plus more" framing, anything less than a ~$40M beat would suggest the consumption acceleration is moderating.

NRR direction — 125% this quarter held flat with Q1. A break above 127% would validate the "AI as incremental spend" thesis; a slip back to 123–124% would suggest Q2's large-customer workload migrations were a one-quarter bump.

Sales productivity flow-through — H1 FY26 net S&M hiring exceeded the prior two years combined. The Q3/Q4 prints will start to show whether the productivity gates Mike referenced are translating that capacity into bookings.

Quantified AI revenue disclosure — management continues to frame AI via influence metrics, not dollars. The first quarter they disclose AI-attributable product revenue (or refuse a direct question about it) will reset the bull/bear debate.

Snowflake Postgres ramp — Crunchy contributed modestly this quarter; preview is coming "in the next couple of months." A real Postgres contribution would broaden the workload story beyond analytics + AI.

Sources

Snowflake Q2 FY2026 Press Release & Financial Statements — https://www.sec.gov/Archives/edgar/data/1640147/000164014725000177/fy2026q2earnings.htm
Snowflake Q2 FY2026 earnings call prepared remarks and Q&A (Sridhar Ramaswamy, Mike Scarpelli, Christian Kleinerman)

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