Snowflake raised its fiscal 2027 revenue guidance and signed a $6 billion multi-year deal with AWS for Graviton chip capacity, citing faster-than-expected enterprise AI workload growth. The cloud-data company's updated outlook signals that AI-driven data processing is moving from pilot projects to production scale across its customer base.
The $6B AWS commitment locks in ARM-based Graviton compute for Snowflake's platform through at least fiscal 2027. This follows the company's earlier $6B AWS deal announced in recent weeks, bringing Snowflake's total AWS chip commitments to approximately $12B over the next several years.
Snowflake's revenue guidance increase reflects a measurable acceleration in AI-adjacent workloads. Enterprises are running more vector search, model training, and inference pipelines directly inside Snowflake's data warehouses rather than exporting data to separate AI platforms. The shift reduces data-movement costs and latency, making Snowflake infrastructure stickier.
Key facts
- 01Snowflake signed a $6B multi-year deal with AWS for Graviton chip capacity.
- 02The company raised its fiscal 2027 revenue guidance, citing accelerating enterprise AI adoption.
- 03This is Snowflake's second major AWS chip commitment in recent weeks after an earlier $6B deal.
AWS Graviton chips are gaining share in AI CPU workloads because they deliver better price-performance than x86 alternatives for tasks like data transformation, embedding generation, and batch inference. Snowflake's bet on Graviton mirrors a broader industry move toward ARM-based compute for non-GPU AI tasks.
The deal structure spreads costs across multiple fiscal years, giving Snowflake predictable capacity at pre-negotiated rates while AWS locks in a major customer's long-term demand. Multi-billion-dollar cloud commitments have become standard for AI-heavy workloads as companies hedge against spot-market volatility and chip shortages.
Snowflake competes directly with Databricks, Google BigQuery, and Microsoft Fabric for enterprise AI data infrastructure. Databricks recently raised at a $62B valuation and has pushed aggressively into LLM fine-tuning and retrieval-augmented generation. Snowflake's updated guidance suggests it is holding share despite intensifying competition.
The counterweight: Snowflake's gross margins remain under pressure as it buys more cloud compute to support AI workloads. The company has not disclosed whether the AWS deal includes volume discounts that offset margin compression. Analysts will watch whether revenue growth outpaces cost growth when Snowflake reports next quarter.
This deal confirms that enterprise AI adoption is real and scaling, not stalled in proof-of-concept purgatory. Snowflake would not commit $6B to AWS chips if its customers were merely testing AI features. The question now is whether competitors like Databricks will match with similar hyperscale commitments, or whether Snowflake has locked in a structural cost advantage that others will struggle to replicate.
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