Morgan Stanley reiterates Snowflake stock rating on AI product momentum By Investing.com

Snowflake grew revenue 29% TTM to $4.7B and reported $1.23B in product revenue this quarter (+30% YoY); remaining performance obligations rose 42% to $9.77B including a $400M deal, while the stock trades at $153, down 45% from its 52-week high of $281. Morgan Stanley reiterated Overweight with a $245 price target and expects new AI products (Cortex Code, Snowflake Intelligence, Project SnowWork) to drive FY2027 revenue and a path to profitability (analysts project $1.95 EPS for fiscal 2027), although analyst views are mixed (Macquarie cut PT to $177 Neutral; BofA $275 Buy; Barclays $192 Equalweight; Stifel $205 Buy).

Analysis

Snowflake sits at the intersection of two durable trends: enterprises consolidating data platforms and the incremental shift from manual ETL to natural-language driven data workflows. If developer-facing tooling meaningfully cuts migration and integration friction, expect shorter sales cycles and a measurable uplift in incremental query volumes within 6–18 months as more projects move from proof-of-concept to production.

A key second-order beneficiary is the hyperscaler ecosystem: higher query intensity translates into outsized variable cloud consumption (storage + compute) even if gross margins compress at the platform layer, creating a revenue tailwind shared between Snowflake and its cloud hosts. Conversely, professional services and third-party integrators face margin compression as automation reduces billable implementation hours, pressuring smaller integration specialists but creating scale advantages for native or large SI partners.

Primary risks are execution and external shock to enterprise AI confidence. A high-profile model/data breach or regulatory clampdown could delay enterprise rollouts, reversing any near-term adoption lift; similarly, rapid increases in cloud compute pricing or an aggressive competitive bundling from hyperscalers would blunt revenue leverage. Monitor near-term metrics that lead revenue (migration cohort conversion rates, average query per customer, and compute cost per query) — these will determine whether product traction turns into durable financial upside over the next 12–24 months.

Key Decisions for Investors

• Long SNOW via a 12–15 month call spread (buy near-the-money call, sell 20–30% higher strike) sized to 1–2% of fund AUM risk. Rationale: asymmetric upside if migration acceleration occurs; capped cost limits downside to premium paid. Exit/roll if trailing three quarters show sequential deceleration in queries or compute margin compression.
• Pair trade — long SNOW equity vs short SMCI sized 0.5–1% net risk (delta-neutral by notional). Rationale: thematic tilt to cloud consumption over on‑prem AI hardware; unwind if SMCI reports materially stronger on‑prem enterprise bookings or if Snowflake signals materially higher infra pass‑through costs.
• Buy a protective near-term put (6–9 months) on SNOW as hedging around the next two earnings/guide calls, allocation 0.25–0.5% of AUM. Rationale: protects against headline-driven AI safety/regulatory shocks that historically compress multiples sharply and quickly.
• Monitor governance/product metrics and be ready to add on sequential positive inflection: increase SNOW exposure to 3–4% notional if three successive quarters show improving migration conversion and stable compute economics, otherwise take profits on options positions after first positive guide beat.