As the AI boom turns data into the new battleground, investors are once again weighing a classic cloud-data dilemma: buy the focused pure-play (Snowflake) or back the diversified giant (Alphabet) with Google Cloud’s BigQuery.
That debate intensified heading into December 16, 2025, after fresh analyst commentary compared Snowflake (SNOW) and Alphabet (GOOGL) side by side, and as Wall Street continued to digest Citigroup’s recent decision to keep a “Buy” on Snowflake even while trimming its price target. [1]
Meanwhile, a separate headline underscored what might be the most underappreciated driver of cloud winners in 2026: power and infrastructure. Reuters reported today that TotalEnergies signed a 21-year renewable power agreement to support Google data centers in Malaysia, highlighting how the AI-cloud race is increasingly constrained by energy and capacity—not just software features. [2]
Why cloud analytics is becoming the AI economy’s control plane
The investment thesis behind both Snowflake and Alphabet’s cloud-data tooling rests on the same reality: enterprises are shifting from “collecting data” to operationalizing data for AI, and that requires modern analytics platforms, governance, and scalable infrastructure.
One industry forecast frequently cited in today’s market framing pegs the global cloud analytics market at $35.39 billion in 2024, with expectations it could reach $130.63 billion by 2030, implying a 25.5% CAGR from 2025 to 2030. [3]
That growth story is why comparisons between Snowflake and Alphabet keep resurfacing. Both are positioned to benefit, but they’re competing with fundamentally different playbooks:
- Snowflake: a pure-play “AI Data Cloud” platform aiming to sit at the center of enterprise data warehousing, engineering, sharing, and AI workloads.
- Alphabet: a broader ecosystem player where Google Cloud’s BigQuery and adjacent AI services ride on top of massive global infrastructure and a multi-business cash engine. [4]
Snowflake’s pitch: a pure-play AI Data Cloud with accelerating adoption signals
Snowflake’s bullish narrative in late 2025 is no longer just “cloud data warehouse.” It’s increasingly “data platform for AI,” and analysts are pointing to adoption metrics to support that storyline.
In the Zacks analysis circulated via Nasdaq, Snowflake’s net revenue retention rate was cited at 125% for fiscal Q3 2026, a sign that existing customers are expanding their usage. The same analysis noted Snowflake had 12,621 customers in that quarter (up 20% year over year) and 688 customers generating more than $1 million in trailing 12‑month product revenue (up 29% year over year). [5]
AI monetization, in particular, has become the headline catalyst:
- The analysis said AI influenced 50% of bookings signed during fiscal Q3 2026. [6]
- It also stated Snowflake hit a $100 million AI revenue run rate one quarter earlier than expected. [7]
- And it pointed to rapid platform shipping: 370 new generally available product capabilities year-to-date, up 35% from the prior year. [8]
Beyond analyst scorecards, Reuters reporting in early December added color to how Snowflake is trying to keep its AI momentum credible in an increasingly crowded market. Reuters reported Snowflake announced a multi-year $200 million agreement with Anthropic to bring Claude models to Snowflake for “advanced reasoning and secure multi-step analysis,” while also deepening ties with Accenture and expanding integrations with AWS (including surpassing $2 billion in sales on the AWS Marketplace, per Reuters). [9]
The near-term friction: guidance, discounts, and hyperscaler pressure
Snowflake’s strength has come with a key caveat: investors have demanded not only strong results, but stronger guidance—especially after a sharp run-up earlier in 2025.
On December 3, Reuters reported Snowflake forecast Q4 product revenue of $1.19 billion to $1.20 billion, representing 27% growth, which was above analysts’ estimates but still disappointed some investors who wanted more acceleration. [10]
A day later, Reuters reported Snowflake shares slid after the company projected slower product revenue growth, noting that discounts on large, long-term deals played a role, and quoting CEO Sridhar Ramaswamy describing more favorable pricing for larger volumes or longer-term contracts. [11]
This is the crux of the Snowflake debate entering 2026: can Snowflake expand AI-driven demand without giving away too much pricing power—especially while competing with hyperscalers that can bundle data, storage, and AI infrastructure? [12]
Alphabet’s pitch: BigQuery plus infrastructure scale—and a $155B backlog signal
Alphabet’s cloud-data case is simpler to describe and harder to dismiss: Google Cloud is growing quickly, BigQuery is a core analytics product, and Alphabet has the infrastructure budget to compete in a world where AI workloads are increasingly compute- and data-hungry.
The same Zacks-via-Nasdaq comparison highlighted Google Cloud’s scale and momentum, including:
- Q3 2025 Google Cloud revenue of $15.16 billion, up about 33.5% year over year (the article’s cited figure). [13]
- Google Cloud backlog of $155 billion, up 46% sequentially, a figure that has become a key datapoint for bulls. [14]
Reuters has also reported the $155 billion backlog figure, explaining it as “backlog of non-recognized sales contracts,” and tying it to demand for AI-powered infrastructure and data analytics services. [15]
Reuters further reported Alphabet boosted projected capital expenditures to $91–$93 billion amid AI demand—an important reminder that Google Cloud’s growth is being pursued with aggressive investment. [16]
December 16’s infrastructure headline: power for Google data centers in Malaysia
Today’s Reuters report about TotalEnergies and Google adds a real-world dimension to the backlog-and-capex story.
According to Reuters, TotalEnergies signed a 21-year power supply deal to provide 1 terawatt hour of renewable energy for Google’s data centers in Malaysia, sourced from a new solar plant scheduled for construction in early 2026, with supply expected to begin in the first quarter of the following year. [17]
For cloud investors, this matters because it highlights a strategic reality: AI-cloud leadership is constrained by the ability to secure energy and expand capacity, not just by shipping features. [18]
Leadership focus on AI infrastructure
Alphabet has also been sharpening the organizational focus on infrastructure. Reuters reported earlier in December that Google appointed longtime executive Amin Vahdat as chief technologist for AI infrastructure, with Google Cloud CEO Thomas Kurian emphasizing infrastructure as a key priority and citing the same $155 billion cloud backlog in the broader spending context. [19]
The key comparison investors are making today: performance and valuation signals
The Zacks comparison that circulated this week didn’t argue Snowflake is “broken.” It argued that, right now, Alphabet’s combination of scale, infrastructure, and earnings power looks more attractive on a relative basis.
Among the datapoints highlighted:
- Over the trailing six months, SNOW was cited as up 4.2%, while Alphabet was cited as up 75%. [20]
- On a forward price-to-sales basis, the analysis cited SNOW at 13.36x versus GOOGL at 9.68x. [21]
- It also noted both names appeared “overvalued” under Zacks’ internal Value Score framework (F for SNOW and D for GOOGL). [22]
Earnings expectations were also part of the framing:
- Snowflake fiscal 2026 EPS estimate cited at $1.20, up 2.5% over the past 30 days, implying 44.58% year-over-year growth (per Zacks consensus). [23]
- Alphabet 2025 EPS estimate cited at $10.52, up 0.28% over the past 30 days, implying 30.85% year-over-year growth (per Zacks consensus). [24]
The conclusion from that analysis: Alphabet’s broader ecosystem, global infrastructure, and more consistent earnings profile make it the “better choice” for investors seeking stability with upside—while Snowflake’s competitive environment remains a headwind. [25]
Citi’s stance on Snowflake: still “Buy,” but with a trimmed target
Even as the SNOW-vs-GOOGL debate heats up, it’s notable that major firms are not stepping away from Snowflake.
In a note published by TheFly and distributed via TipRanks, Citi lowered its price target on Snowflake to $300 from $310 while keeping a Buy rating, citing a lower fiscal Q3 beat relative to expectations but “strong Q4 sales guidance.” [26]
A separate recap of the move similarly summarized the call: Citi (analyst Tyler Radke) maintained “Buy” and reduced the target from $310 to $300. [27]
And in Nasdaq’s Fintel-sourced analyst activity recap, Citi’s “Buy” stance was presented alongside broader price-target context, including an average one-year target price of $286.98 at that time, with a range from $171.70 to $462.00. [28]
Why this matters on December 16
Put simply:
- Zacks’ comparison tilts toward Alphabet as the “edge now” pick. [29]
- Citi’s rating posture suggests Snowflake still has enough strategic and financial upside to remain a Buy in the eyes of at least one major bank—despite the volatility around guidance and deal structure. [30]
For readers trying to interpret today’s cross-currents, the takeaway isn’t that one company “wins” and the other “loses.” It’s that the market is sorting cloud-data stocks into two buckets:
- Infrastructure-and-ecosystem compounders (Alphabet/Google Cloud), where scale and backlog visibility carry weight. [31]
- Product-led pure plays (Snowflake), where execution in AI features, retention, and durable pricing power must overcome hyperscaler competition. [32]
The bigger theme for 2026: AI cloud is becoming an energy and capacity problem
The most “current” signal in today’s news flow may not be a price target at all—it’s the physical buildout underneath AI.
- Google is expanding data center capability globally, and Reuters’ report on a long-term renewable power deal in Malaysia highlights the energy intensity of AI-driven cloud growth. [33]
- Google is also tightening leadership focus on AI infrastructure, as Reuters reported with the Amin Vahdat appointment. [34]
- Snowflake, while not a hyperscaler, is competing in the same AI era by partnering deeply across model providers and platforms, including Anthropic and AWS marketplace motion, per Reuters. [35]
That’s why Snowflake vs Alphabet comparisons resonate right now: the competitive advantage in cloud analytics increasingly depends on who can connect data + AI + infrastructure most seamlessly—and at sustainable margins.
What to watch next for SNOW and GOOGL investors
Here are the practical checkpoints investors and enterprise buyers are watching as this rivalry evolves:
For Snowflake
- Retention and expansion: whether net revenue retention can hold up as budgets normalize and competition intensifies. [36]
- AI monetization durability: whether AI-driven bookings and usage translate into sustained product revenue growth without heavy discounting. [37]
- Partner ecosystem outcomes: whether deals like the Anthropic agreement translate into measurable enterprise wins and stickier workloads. [38]
For Alphabet and Google Cloud
- Backlog conversion: whether the $155B backlog continues to grow and converts efficiently into revenue. [39]
- Capacity buildout: capex is rising, and today’s energy deal is a reminder that power sourcing is now strategic. [40]
- AI infrastructure execution: leadership moves and chip strategy (including Google’s TPU efforts cited by Reuters) could become differentiators as the AI stack industrializes. [41]
Bottom line on December 16, 2025
Today’s Snowflake vs Alphabet conversation is less about a single quarter and more about the shape of the AI-cloud market going into 2026.
- Alphabet is leaning into scale—pairing BigQuery and AI services with a massive backlog, a rising capex plan, and now very visible infrastructure-and-energy moves. [42]
- Snowflake is leaning into specialization—pushing AI features and partnerships to remain the neutral, cross-cloud data layer that enterprises trust, while Wall Street debates whether growth and margins can remain strong in a hyperscaler-dominated era. [43]
For Google News and Discover readers, the essential point is this: cloud analytics is no longer a back-office toolset—it’s becoming the operating system for AI inside enterprises, and the market is rewarding whichever companies can deliver it at scale, with predictable economics, and with the infrastructure to back it up. [44]
References
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