- OpenAI signs a $38.000 billion, seven-year deal with AWS to secure massive computing with Nvidia GB200/GB300 and EC2 UltraServers.
- Full deployment before 2026 and option for later expansion; dedicated capacity already in use and new facilities tailored to your needs.
- Key diversification move beyond Microsoft, against a backdrop of $1,4 trillion in infrastructure commitments and signs of a possible bubble.
OpenAI has made a significant move in the tech world by closing a multi-billion dollar deal with Amazon Web Services to secure long-term computing power. The agreement, valued at $38.000 billion, guarantees immediate and increasing access to the AWS cloud to run and scale the most demanding artificial intelligence workloads of the company led by Sam Altman.
Beyond the figure, what's relevant is the scope: OpenAI will be able to leverage AWS's top-tier infrastructure, with full deployment planned before the end of 2026 and the option to expand from 2027 onwards. The agreement represents a diversification of suppliers compared to its historical dependence on Microsoft, while also strengthening the training and inference capabilities of ChatGPT and the upcoming frontier models that the company has in development.
What exactly have OpenAI and AWS signed?
The agreement is multi-year, with a seven-year horizon, and stipulates that OpenAI will begin using dedicated AWS capacity immediately. According to both companies, some of this dedicated capacity is already operational , and the rest will be activated progressively until completion before the end of 2026, with room to expand in subsequent years depending on demand.
Operationally, OpenAI will leverage existing AWS data centers, while Amazon will simultaneously build facilities specifically designed for its needs and geared towards cloud-native computing . These new locations will incorporate a sophisticated architectural design intended to maximize performance and energy efficiency, a critical aspect when computing demands are growing at a breakneck pace.
The alliance includes the use of Amazon EC2 UltraServers, an architecture that groups large clusters of GPUs within the same network to minimize latency between nodes. In practice, this means that Nvidia's latest generation chips (GB200 and GB300) will be able to communicate with minimal delay, which is crucial for training massive models and serving inference on a global scale without bottlenecks.
The stated objective of the parties is clear: to provide OpenAI with a massive, reliable, and secure computing base to support training of new frontier models, support ChatGPT traffic, and deploy more complex workloads, including agentive ones, with the elasticity that only a hyperscale like AWS can provide.
- Duration and value: $38.000 billion during seven years, with full deployment before 2026 and option for subsequent growth.
- Dedicated capacity: part available from day one and the rest on ramp, supported by current data centers and in new, custom-built facilities.
- Architecture: Amazon EC2 UltraServers with Nvidia GB200/GB300 GPUs and clusters on the same network for ultra-low latencies.
- Intended uses: training of next-generation models, ChatGPT inference and high-density AI workloads.
The technical dimension: chips, clusters and performance
Training language models and multimodal systems of the scale handled by OpenAI requires grouping hundreds of thousands of high-performance GPUs and clusters and connecting them with a very high-speed network. AWS's proposal fits this pattern: it integrates Nvidia GB200 and GB300 accelerators into UltraServers connected within the same mesh, in order to reduce communication latency between chips and accelerate both distributed training and the massive delivery of responses.
When working at massive scale, every millisecond shaved off internal communications can translate into weeks of training time savings. That's why AWS focuses on interconnects, queue management, and resource orchestration to maximize efficiency per chip and per cluster , avoiding wasted capacity and ensuring sustained utilization.
The design also considers mixed scenarios: not only cutting-edge training, but also high-performance inference for production products like ChatGPT. As the user base grows and features become more complex, cost per request and perceived latency for the end user become essential metrics for maintaining competitiveness.
In this context, AWS boasts a track record of operating massive clusters in sectors such as biotechnology, climate research, and finance. That experience—in projects requiring more than half a million chips in coordinated operation—is now being applied to a client demanding a level of stability and security that is unusual even by enterprise cloud standards.
The result is a platform designed to scale rapidly, capable of absorbing peak demands and sustaining continuous training and inference loads for months. For OpenAI, having this level of flexibility and availability is not a luxury, but a prerequisite for continuing to push the boundaries of cutting-edge AI without hindering its roadmap.
Strategic movement and relationship with other cloud providers
From 2019 to 2023, OpenAI contracted all its computing with Microsoft, its largest investor. That agreement included a right of first refusal, meaning it could only use other cloud providers with Redmond's approval. This restriction was recently lifted after renegotiated terms, allowing OpenAI to freely sign agreements with any cloud platform—a decision that opens the door to deals like the one we're discussing with AWS.
In parallel, OpenAI has sealed massive purchase commitments with several players in the value chain: an additional agreement with Azure valued at $250.000 billion has been revealed , another with Oracle for $300.000 billion for data centers, and contracts with manufacturers and suppliers such as Nvidia, AMD, Broadcom or Google, in addition to a $22.400 billion agreement with CoreWeave, one of the benchmarks of the so-called neo-clouds oriented to AI developers.
Until this announcement, AWS had been the odd one out among the major US cloud providers supporting OpenAI; now it becomes a top-tier partner. This move is significant because Amazon maintains very close ties with Anthropic , a direct competitor of OpenAI. Amazon has invested billions in that company and is building an $11.000 billion campus in Indiana for its workloads, with large-scale Trainium2 deployments already operational.
OpenAI, for its part, continues to prioritize Nvidia accelerators for its most critical workloads, instead of AWS's Trainium processors, a trend the market had already been indicating. On the commercial front, the collaboration with Amazon is also evident in the services ecosystem: Amazon Bedrock integrates multi-house models, and, according to the companies, AWS customers can access OpenAI models in that environment for enterprise use cases.
The industry consensus is that diversifying providers is not a whim, but a measure of resilience. When computing demand is enormous and sustained, relying on a single cloud provider poses an operational and financial risk that should be mitigated. AWS's entry into the equation offers OpenAI redundancy, better negotiation on price and delivery times, and guaranteed continuity in the face of potential chip supply disruptions.
Financial impact, market and implications for the industry
The agreement with AWS is part of an unprecedented effort by OpenAI to secure computing power and computing resources at scale. The company has acknowledged commitments worth $1,4 trillion in infrastructure to build and power its AI models. This figure, far exceeding typical industry standards, has raised concerns about a potential investment bubble surrounding the AI economy.
To put it in perspective, Altman has estimated that fulfilling these commitments will require approximately 30 gigawatts of electrical power , more than 2% of the total installed capacity in the United States by the end of 2023, according to official data. The process of building data centers, securing chip supplies, and closing long-term energy contracts has become the real race behind the AI race.
On the corporate front, OpenAI is completing its transformation from a non-profit structure to one capable of generating returns for investors . This organizational change paves the way for a potential IPO, a move that various media outlets have estimated at valuations of up to one trillion dollars, always subject to market conditions.
The most immediate financial metrics also offer insight. OpenAI is expected to generate around $13.000 billion in revenue this year, although the company doesn't anticipate reaching profitability until 2029. This long timeframe, combined with the level of committed capital expenditure, explains why some investors are adopting a cautious stance despite the general enthusiasm for generative AI.
The stock market reacted optimistically to the announcement: Amazon shares rose by around 5% in early trading, peaking at times at up to 6%, and Nvidia shares climbed nearly 2,7%, in line with its role as the dominant provider of AI accelerators. For AWS, the deal reinforces its ability to build and operate massive data center networks in the new era of artificial intelligence.
In this context, major technology companies have intensified their capital spending. Last year alone, Amazon, Google, Meta, and Microsoft invested over $360.000 billion in capex, fueling the debate about the sustainability of the investment cycle. The circular relationships forged between cloud providers, chip manufacturers, and AI developers—with cross-commitments and long-term acquisitions—are both a driver of growth and a source of systemic risk if demand fails to keep pace.
For AWS, the agreement with OpenAI also has competitive implications: it consolidates its catalog as a leading infrastructure for large-scale AI workloads and, in turn, strengthens its offering for enterprise customers who consume models through managed services. For OpenAI, the gain is twofold: capacity and resilience, with the flexibility to scale without depending on a single provider.
Key points for CTOs and architecture teams
The first lesson is that there is no AI strategy without a compute strategy . If industry leaders are securing clusters with next-generation accelerators and low-latency networks, organizations should prioritize the efficiency of their workloads (from training to inference) and treat metrics like cost per token and p95 latency as if they were business indicators.
- End-to-end optimization: from preprocessing to training and to serving of inferencesorchestrating resources to reduce waiting times and improve utilization.
- Fault-prepared architectures: design with redundancy, multi-AZ/multi-region and Selective multi-cloud when the risk justifies it.
- Energy and capacity plan: size growth with realistic scenarios, anticipate peaks and adjust commitments flexible purchasing.
- Governance and security: protecting models, data, and pipelines with identity controls, encryption, and continuous audit in hybrid environments.
In operational terms, the OpenAI and AWS move demonstrates that the AI bottleneck is no longer talent or software, but sustained access to high-performance hardware , power, and well-orchestrated networks. Whoever secures these three elements will be able to accelerate product cycles and differentiate themselves.
The companies' public statements all point in the same direction: there is an unprecedented demand for computing power, and massive, reliable computing is needed to scale cutting-edge models. At the same time, Microsoft maintains a leading role as a major OpenAI partner on Azure, Oracle is expanding its data center footprint, Google Cloud is already among those driving ChatGPT, and CoreWeave is serving AI developer workloads with a deal worth tens of billions.
It remains to be seen how the workload will be distributed across platforms and how the actual cost of training and inference will evolve as new generations of chips are introduced and software stacks are optimized. But the trend is clear: vendor diversification and long-term computing reserves are becoming strategic levers for sustaining the pace of innovation.
This alliance between AWS and OpenAI not only secures capacity for the coming years; it also confirms that the next phase of AI will be decided at the foundation level: chips, data centers, and energy , coordinated with architectures that maximize every millisecond. In this arena, securing agreements of this magnitude is as much a matter of technology as it is of corporate strategy.
