Morgan Stanley raises Snowflake’s target to $470 as AI adoption and core-platform consumption drive a third straight quarter of growth acceleration.

Overview

This earnings review argues that Snowflake’s AI portfolio is beginning to create a durable growth flywheel: direct AI monetization increases platform consumption, while broader use cases and buyer personas expand the addressable opportunity. Morgan Stanley retains Overweight and raises its target price to $470 from $300 following stronger results and guidance.

Core views

Snowflake’s 2Q27 results exceeded already elevated expectations. Product revenue rose 37% year on year to $1.492 billion, accelerating from 34% in 1Q27 and beating the guidance midpoint by 5.2% and consensus by 5.0%. The approximately 300bp sequential acceleration occurred despite a roughly 530bp tougher comparison, lifting Morgan Stanley’s calculated two-year growth stack to about 68% from 60%. Supporting indicators were also strong: net revenue retention held at 126%, current RPO growth improved to approximately 42% year on year from 38% in 1Q, net-new customers increased 44% year on year to 692 and brought the customer base to 14,554, and customers generating more than $1 million of trailing-12-month product revenue rose 27% to 828. The institution sees AI as an early but meaningful source of the acceleration. Management attributed about half of the product-revenue acceleration to AI products and the balance to the core platform. More than 9,100 accounts had adopted CoCo, formerly Cortex Code, up by over 2,000 from 1Q27, while CoWork reached 5,800 accounts. Morgan Stanley’s central mechanism is that AI products generate direct revenue and also increase consumption of the core data platform. It further highlights broad-based customer demand rather than industry concentration, limited revenue dependence on AI-native companies, new finance and supply-chain-optimization use cases, and an expanded buyer group including CEOs, CROs, CFOs and CMOs. Modest hiring alongside accelerating revenue is presented as evidence that growth is becoming less tied to headcount. Guidance reinforced the growth case. FY27 product-revenue guidance was raised by $230 million, materially more than the roughly $74 million 2Q beat, and now implies 36% year-on-year growth versus the prior 31% outlook. The 3Q27 midpoint was $85 million above consensus and implies 37–38% growth, compared with consensus at 29%; Morgan Stanley notes that a 3% beat on this midpoint would imply 41% growth. Its scenario assumptions for 4Q27—a 3% 3Q beat, a 4Q sequential guide in line with the prior year’s 3.4%, and a 3% beat on the anticipated 4Q guide—would imply about 42% 4Q product-revenue growth. On this basis, the report sees a clear path to growth above 40% in 2H27. The earnings release also contained near-term profitability trade-offs. Adjusted FCF of $92 million and a 6.0% margin were below Morgan Stanley’s $125 million and 8.4% estimates and consensus of $119 million and 8.0%, respectively; management did not provide a detailed bridge on the preliminary call. Non-GAAP product gross margin declined about 170bp year on year to 74.7%, and FY27 guidance is 74.0%, as fast-growing AI workloads currently carry lower contribution margins. Management nevertheless reiterated FY27 FCF-margin guidance of 23.0% and cited model routing, open-source models and lower frontier-model pricing as potential longer-term offsets. Morgan Stanley reiterates Overweight and raises its price target to $470 from $300 because estimates moved higher after the third consecutive growth acceleration and continued AI contribution. The base case assumes revenue grows at about a 26% CAGR from CY25 to CY30 to roughly $15.1 billion, operating margin reaches about 21.1% in CY30, and CY30 FCF reaches about $4.4 billion with a roughly 28.8% FCF margin. The $470 target applies an approximately 60x multiple to CY30 base-case FCF, discounts it back to CY27 at an unchanged 11.1% WACC, and implies about 22x EV/CY27 revenue. The report views that revenue multiple as a discount to, but more consistent with, high-growth AI software beneficiaries including CrowdStrike, Cloudflare and Palantir. Its scenario framework ranges from a $650 bull case, based on roughly $17.2 billion CY30 revenue, about $5.5 billion FCF and a roughly 65x FCF multiple, to a $200 bear case. The bear case assumes public-cloud competition weighs on growth, with CY30 revenue of about $13 billion, FCF of about $3.5 billion and a roughly 32x multiple. The report’s stated downside themes are competitive pricing and margin pressure, as well as difficulty expanding into adjacent data-management and OLTP workloads.

Analysis framework

Morgan Stanley first compares quarterly revenue and operating indicators with guidance, consensus and prior-quarter growth. It then links AI-product adoption to direct monetization and increased core-platform consumption, tests that thesis with customer and RPO metrics, and uses management guidance plus explicit beat and seasonal assumptions to frame second-half growth. Finally, it values Snowflake using long-term revenue, margin and free-cash-flow assumptions, an FCF multiple and an 11.1% WACC discount rate, alongside bull, base and bear scenarios.

Methodology notes

• Valuation methodsDCF (Discounted Cash Flow)

  Free-cash-flow valuation discounted to CY27 using an 11.1% WACC, with an EV/FCF multiple applied to CY30 FCF.

  The report estimates long-term free cash flow, applies a valuation multiple, and discounts the resulting value back at the stated WACC to derive its price target.

• Industry AnalysisVolume-price decomposition

  Growth analysis using product-revenue acceleration, customer additions, net revenue retention, RPO and larger-customer expansion.

  Morgan Stanley uses customer, retention and backlog indicators to explain whether revenue growth is broad-based and durable rather than relying on the headline revenue figure alone.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Snowflake Inc. (SNOW)

  Primary covered company; Morgan Stanley views AI-product adoption and core-platform consumption as reinforcing growth.

  Strengths

  Third consecutive quarter of product-revenue acceleration; broad-based customer growth; AI adoption expanding use cases and buyer personas.

  Weaknesses

  AI workloads currently have lower contribution margins, contributing to lower product gross-margin guidance.

  Comparison

  The implied ~22x EV/CY27 revenue multiple is described as a discount to, but more in line with, high-growth AI software beneficiaries such as CRWD, NET and PLTR.

  Risks

  Public-cloud competition could pressure pricing, growth and margins; expansion into adjacent data-management and OLTP workloads could prove difficult.

Key data

• 2Q27 product revenue$1.492 billion; +37% YoYAccelerated from +34% in 1Q27; 5.2% above the guidance midpoint and 5.0% above consensus.
• FY27 product-revenue outlook+36% YoYRaised from +31%; the guidance increase was $230 million.
• 3Q27 product-revenue guidance+37–38% YoYMidpoint was $85 million above consensus and implied growth versus consensus at +29%.
• Net revenue retention126%Sustained in 2Q27.
• Current RPO growth~42% YoYImproved from ~38% in 1Q27.
• Customers14,554692 net-new customers in 2Q27, up 44% year on year.
• Adjusted FCF$92 million; 6.0% marginBelow Morgan Stanley’s $125 million / 8.4% estimate and consensus of $119 million / 8.0%.
• Non-GAAP product gross margin74.7%Down about 170bp year on year; FY27 outlook is 74.0%.
• Base-case CY30 FCF~$4.4 billionBased on ~26% CY25–CY30 revenue CAGR, ~21.1% operating margin and ~28.8% FCF margin in CY30.

Impact & implications

Morgan Stanley believes AI adoption is improving Snowflake’s growth durability by broadening monetization, workloads and decision makers while reinforcing core-platform consumption. The raised guidance and higher target reflect that view, but the report notes that AI’s current margin profile creates a near-term profitability trade-off.

Risks

• Competitive vendors could pressure pricing and weigh on growth and margins.
• Expansion into adjacent data-management, including OLTP, use cases could prove difficult.
• Fast-growing AI workloads currently carry lower contribution margins, pressuring product gross margin.

What to watch

• Whether product-revenue growth reaches the report’s path to more than 40% growth in 2H27.
• Net revenue retention, current RPO growth, net-new customers and growth in customers generating more than $1 million of trailing-12-month product revenue.
• Adoption of CoCo and CoWork and evidence that AI workloads are lifting core-platform consumption.
• The pace of AI-margin optimization through model routing, open-source models and lower frontier-model pricing.
• Operating profitability and free-cash-flow performance following the 2Q FCF shortfall.