Snowflake and AWS: $6B AI chip deal reshapes the cloud

The timeline of a deal that rewrites cloud priorities

On May 27, 2026, TechCrunch has reported The official news: Snowflake has signed a five-year contract with Amazon Web Services worth six billion dollars. The subject of the contract is CPU chips designed for artificial intelligence workloads. Therefore, this is not a simple commercial renewal, but a structural commitment to the infrastructure.

Snowflake is one of the most popular data cloud platforms among enterprise companies globally. In recent years, the company has heavily invested in native AI features, integrating language models and machine learning pipelines directly into the data analytics environment. Therefore, reliance on high-performing and scalable hardware has become a top priority in their business plan.

The deal with AWS builds on an existing relationship, but takes it to a whole new level. Specifically, the choice to focus on CPU chips—and not just Nvidia GPUs—is a very deliberate architectural move. Plus, it reflects a broader trend that we at SHM Studio we're keeping a close eye on across the global cloud market.

Nvidia put on notice: the hardware showdown background

Nvidia has dominated the AI chip market thanks to its GPUs, which have become the de facto standard for training and inference of complex models. However, this position is no longer unchallenged. Over the past eighteen months, Amazon has accelerated the development of its own proprietary chips: Trainium for training and Inferentia for inference.

The deal with Snowflake strengthens the AWS Graviton ecosystem and Amazon's custom chip family. As a result, Nvidia is facing competition not just from AMD or Intel, but from its very own biggest customers. This phenomenon—known as vertical integration of AI hardware — has already been analyzed by Gartner as one of the defining structural trends of the decade.

Contrary to what you might think, Nvidia isn't facing an immediate crash. In fact, global demand for GPUs is still through the roof. However, every big deal like the Snowflake-AWS partnership chips away at potential market share and reduces systemic reliance on the Silicon Valley giant. So, the message here is more about long-term strategy than short-term operations.

Winners and losers: a multi-layered read

The clear first winner is Amazon. The six-billion-dollar deal brings guaranteed cash flow for five years and solidifies AWS as the go-to platform for the AI workloads of a major data cloud player. Plus, it proves to the enterprise market that Amazon's in-house hardware roadmap is legit.

Snowflake, in turn, gets guarantees of priority access to computing power at a time when AI chip shortages are still a major bottleneck. Therefore, the deal lowers operational risk and makes it easier to plan the rollout of new AI features with more confidence. According to Harvard Business Review , multi-year contracts of this type offer a measurable competitive advantage in terms of time-to-market for AI features.

The hardest-hit losers are alternative cloud infrastructure providers that lack a similar vertical chain. Specifically, those controlling neither the silicon nor the orchestration software struggle to compete on pricing and performance. Similarly, Nvidia hardware resellers serving the enterprise segment could see their pipeline shrink in the coming years.

SHM Studio's take: what's going on beneath the surface

Deals like this rarely just involve the two companies signing them. In fact, they reshape expectations across the whole ecosystem. Italian SMEs building their digital strategy on cloud platforms should keep in mind that the infrastructure choices made by big vendors trickle down—with a lag of six to twelve months—to the costs and features available lower down in the market.

In particular, those using Snowflake for analytics or data warehousing will indirectly benefit from greater availability of AI capacity at potentially lower costs. Consequently, features like the Cortex AI of Snowflake — which allows querying data in natural language — could become more powerful and accessible. This has direct implications for the activities of applied artificial intelligence that companies are building into their everyday workflows.

Besides this, the strengthening of the AWS-Snowflake axis raises a strategic question for SMEs: is it better to spread things across multiple cloud providers or go all-in on an integrated ecosystem? There is no one-size-fits-all answer. Still, market trends show that deep integration usually pays off for those who choose wisely.

What this means for cloud-first SMBs: three things to watch

Italian companies operating in cloud-first environments must monitor three specific areas in the coming quarters.

• AI workload pricing: the availability of proprietary AWS chips could reduce inference costs for those running models on Amazon infrastructure. Therefore, it is worth re-evaluating existing architectures with your technology partner.
• Data platform roadmap: Snowflake will accelerate the development of native AI features. Consequently, those already using the platform should check which new capabilities become available over the next twelve months.
• Single vendor lock-in: exclusive deals of this size really ramp up the vendor lock-in . So, it's a good idea to look into data portability strategies and contracts with solid exit clauses.

To dive deeper into these topics with a perspective on digital strategy and competitive positioning, it's also worth checking out the insights from McKinsey Digital on the infrastructure shifts happening in the enterprise world.

The work still in progress: what remains to be defined

The Snowflake-AWS agreement is public in its general financial terms, but many technical details remain confidential. It is not yet clear, for example, which specific mix of Trainium, Inferentia, and Graviton chips will be used in different application scenarios. Furthermore, it is not known whether the agreement includes exclusivity or simply a minimum spending commitment.

These details matter. In fact, the difference between an exclusivity deal and a committed spend multi-year agreement has very different implications for market competition. In the first case, Snowflake ties itself completely to AWS for AI chips. In the second, it maintains the flexibility to integrate hardware from other vendors.

Finally, the question of how Google Cloud and Microsoft Azure will respond remains open. Both providers are developing proprietary chips—TPUs and Maia, respectively—and might seek similar deals with other major data cloud players. Therefore, the cloud AI chip market is set to become even more fragmented and competitive over the next twenty-four months.

Next moves: finding your way around the cloud AI ecosystem in 2026

For Italian SMEs, the operational message is clear. First of all, it is necessary to accurately map which components of their digital infrastructure depend on suppliers exposed to these dynamics. Next, it is a good idea to check whether current tech choices are still in line with where the market is heading.

The activities of SEO , google ads campaigns and LinkedIn campaigns who use AI tools for automatic optimization depend indirectly on the quality of the underlying cloud infrastructure. Therefore, figuring out where the hardware value is concentrated also means figuring out where the quality of digital marketing tools will be focused in the coming years.

Anyone managing a corporate website or e-commerce on cloud architectures should consider a periodic review of technological dependencies. The services of web development and SEO copywriting they are increasingly integrated with AI tools running on infrastructures like AWS. Therefore, the quality of the final output is partly tied to the infrastructural choices of the big vendors.

For a direct chat on what these moves mean for your business strategy, the team at SHM Studio is available for a consultation . Similarly, the SHM Studio blog regularly publishes analyses on these topics to support the decisions of Italian SMEs in the current digital context.