President Trump Announces Joint Venture with OpenAI, SoftBank and Oracle to Build $500B Worth of AI-dedicated Data Centers

On his second day in office, President Trump approved funding for the Stargate Project, a joint venture with OpenAI, SoftBank and Oracle to initially build a $100 billion data center in Texas. Over the next four years, the project aims to expand into additional large-scale data centers, with a total of $500 billion in funding, making it the largest centralized data center investment in history. The funding includes significant financial backing from the U.S. government  with contributions from SoftBank, a firm known for its long-term investment strategies. OpenAI, SoftBank, Oracle and MGX will be the initial equity investors, while Arm, Microsoft, NVIDIA and OpenAI have been named as technology partners and will have some involvement in Stargate.
 
The $500 billion expenditure is unprecedented. Most Tier 1 hyperscaler data center projects are valued in the single-digit billions, making Stargate’s Phase 1 cost more than 50 times higher than its closest comparable. This venture will rely heavily on U.S. government funding, as SoftBank’s Vision Fund, which manages assets worth less than $200 billion, cannot shoulder the full burden. This positions Stargate as a “Manhattan Project” for the AI era, as it represents one of the largest technological undertakings in modern times. The project is poised to reshape global dynamics if it can navigate the significant hurdles that lay ahead. Regardless, OpenAI, equipped with the world’s largest AI cluster, will pursue its goal of artificial general intelligence (AGI) while enjoying unparalleled access to the compute infrastructure needed to push parameter counts higher.

What Does This Mean for Cloud Vendors and Model Developers?

OpenAI Gains a Powerful Competitive Advantage Over Other Model Developers

Being the largest single technology endeavor in recent history, the Stargate Project will have a notable influence on the budding AI market. In TBR’s opinion, no company stands to benefit more than OpenAI. The company’s access to dedicated compute resources will be unmatched in the model developer industry, enabling OpenAI to push parameter counts further and faster compared to peers and supporting the company’s objective of differentiating based on large language model (LLM) performance. OpenAI’s ultimate goal is to reach AGI, which the company defines as “highly autonomous systems that outperform humans at most economically valuable work.” While estimates about when OpenAI will reach this goal vary widely, the Stargate Project fulfills a critical requirement in the pursuit of AGI, making OpenAI a top contender to reach AGI before other firms.

Another Win for OCI

Oracle’s reentry into the already highly saturated IaaS market with Gen2 OCI (Oracle Cloud Infrastructure) has been widely successful. Though initially designed to drive stickiness with enterprises already rooted in the Oracle ecosystem, OCI appears to be gaining traction among digital natives and ISVs for AI use cases. In many ways, this is a testament to Oracle’s decision to cozy up to NVIDIA for GPUs early on in the emergence of generative AI (GenAI) by hosting the company’s DGX software in its data centers, helping NVIDIA position as a software company and avoid becoming another piece of commoditized hardware locked into the hyperscalers’ stacks. Now, riding a wave of over $70 billion in cloud backlog, OCI is Oracle’s fastest-growing business and will soon become its largest.
 
Aside from the GPUs, the other factor fueling OCI growth and granting Oracle its status as one of the largest data center operators in the world is the company’s ability to bring new data centers online extremely quickly. This is because Oracle has generally adopted a strategy of building more but smaller-scale (approximately 145-kW) data centers, with a focus on ensuring that, outside of scale, all Oracle data centers are identical.
 
This scale can vary significantly, though, and with the current AI wave, we have seen Oracle prioritize larger data centers, some spanning 800MWs for AI customers, including OpenAI. There was perhaps no greater testimonial for OCI as a cloud credible enough for AI applications than OpenAI’s mid-2024 decision to leverage OCI for AI training via Azure.
 
To be fair, the existing Azure-Oracle relationship influenced that decision, but OpenAI has made it clear that the company not only needs IaaS services to push the boundaries of its models but also needs them quickly, regardless of who provides them. The Stargate Project would only advance the OCI-OpenAI connection, bringing new workloads to OCI and sending a message to the market that OCI is also in the game with Amazon Web Services (AWS), Microsoft Azure and Google Cloud when it comes to AI workloads.

Oracle Sets Its Sights on Healthcare as the Company Pursues AI Opportunities for Cerner

Oracle CTO Larry Ellison’s remarks at the White House on Jan. 21 were brief but telling of where Stargate could go, with the Oracle co-founder highlighting AI’s role in modern electronic health record (EHR) management and healthcare transformation at large. Of course, Oracle entered the healthcare market over two years ago with its acquisition of Cerner and has since modernized Cerner Millenium on OCI in hopes of delivering a new cloud-based system that will challenge decades-old EHR systems. This includes an EHR system that can support disease-specific AI models that, importantly, are developed not by Oracle but by medical professionals with expertise in said diseases. The details and timeline around Stargate are still vague but stand to advance Oracle’s push in AI, including within the healthcare vertical, which perhaps has among the most to gain from AI’s potential.
 
When the Cerner deal closed, Ellison was very clear about plans to have a standardized database that unifies fragmented data so medical professionals can instantly access EHR data, regardless of what type of EHR system it is, within a single database. At the time, we wrote about the obvious roadblocks to overcome, including the security & compliance concerns and need to obtain legislative backing. Since Ellison’s initial remarks, we have not heard much of an update on this vision, but Stargate and what seems to be Oracle’s rising role in the new administration (stay tuned as we track any potential Oracle-TikTok developments) could help move this vision along.

While Microsoft Has Been a Close Ally of OpenAI, the Bond That Was Forged in the First Year of GenAI’s Time in the Spotlight Has Weakened

So, how did we get here? Rumors of the Stargate Project date back to March 2024, when OpenAI CEO Sam Altman outlined ambitions for a $100 billion data center in partnership with Microsoft. At the time, the partnership seemed logical, given the companies’ long-standing relationship and Microsoft’s significant equity stake in OpenAI. However, the dynamics have shifted. In October 2024 Altman publicly criticized Microsoft for its slow progress in building AI-dedicated infrastructure, an issue corroborated by reports of persistent infrastructure shortages from Microsoft management. OpenAI’s latest announcement reflects the outcome of this strained relationship, as Azure’s exclusivity agreement with OpenAI has been officially amended, granting OpenAI the right to seek alternative delivery agreements if Microsoft fails to meet its compute demands. Oracle is the first cloud provider OpenAI has turned to, leveraging Oracle’s substantial capacity for AI workloads and an increasingly strategic relationship with Microsoft.
 
OpenAI’s shift toward Oracle is a setback for Microsoft but does not entirely diminish the hyperscaler’s leadership in AI. OpenAI remains a close partner, and Microsoft is well positioned to grow its AI-related IaaS revenue as the company continues expanding its infrastructure. Furthermore, Microsoft’s SaaS portfolio serves as a key delivery mechanism for OpenAI’s models, and the company retains a significant equity stake in OpenAI.
 
These factors are likely to sustain the strategic partnership between the two entities for the foreseeable future. Although Microsoft is not a member of the Stargate joint venture, it is listed as a strategic technology partner, and TBR expects its platforms and software to play a role in the project.
 
Additionally, while Microsoft may have less influence over OpenAI’s hosting decisions, Oracle and Azure remain deeply interconnected. For instance, Oracle now uses Azure data centers to house its database hardware through the Oracle Database@Azure Service. This setup could theoretically integrate Azure OpenAI into AI development as customers bring enterprise data from Oracle into the Azure cloud.

Stargate’s First Phase Includes the Construction of a Massive $100B Data Center, the Largest GPU Cluster in the World

Why Build an AI Megacluster?

OpenAI’s primary motivation for increasing computational resources is to meet the exponential demands of training models with higher parameter counts. Scaling up these parameters allows models to process larger quantities of data, often referred to as context windows. As a context window expands, model outputs improve in quality and accuracy. The prevailing belief is that pushing parameter counts far enough will enable models to exhibit the capabilities defined in OpenAI’s vision of AGI. With full financial backing from the U.S. government, OpenAI’s pursuit of AGI appears more achievable. The result would likely be a versatile GenAI back-end architecture capable of transforming process automation in SaaS workflows. However, in the short term, OpenAI’s focus on parameter scaling keeps its AI strategy centered on general-purpose LLMs, rather than more specialized small language models (SLMs). This approach makes OpenAI’s models particularly suited for productivity tools and customer service applications, while specialized models may dominate in niche workflow tasks.

Possible Stargate Constraints

While unparalleled access to GPUs and compute infrastructure is a major advantage for OpenAI’s model training strategy, there are still several factors that need to be addressed alongside the data center construction. First, the Stargate Project initially intends to absorb Oracle’s privately funded, in-progress data center in Abilene, Texas, yet TBR believes this could heighten power supply challenges. While power transmission constraints are widespread, Texas’ power grid has had issues in the past, such as in the winter of 2021, due to the fact that Texas’ power grid, unlike the other 47 states in the continental U.S., does not connect to the two major national grids. This prevents access to backup power generated outside the state and poses a risk of a significant outage. To mitigate these power concerns, TBR believes developing alternative power sources, namely nuclear power, will be a priority early in the Stargate Project.
 
Second, having access to effective training data remains a persistent need within the model developer market. While OpenAI has been forthcoming in striking deals with internet platforms and media sources, some speculate that the corpus of data available to train LLMs and large multimodal models (LMMs) could soon be completely used up. The use of synthetic data has often been proposed to overcome this hurdle, yet this path brings separate issues like greater AI hallucinations and model drift. Altogether, while securing project financing is the first step, working around these constraints will challenge OpenAI as it pursues AGI, and the innovations created in the Stargate Project will need to reach beyond simply building the largest AI-dedicated data centers in the world.
 
Financing could also prove to be a risk. In response to Trump’s executive action, Elon Musk, a Trump insider and co-head of the new Department of Government Efficiency, publicly shared his belief that the U.S. government does not have enough money to fund the project. Of course, Musk has a bias as the founder of startup xAI, but nevertheless, the Stargate Project does have a staggering price tag. Still, with the Republican Party’s control of the legislative and executive branches, there will be fewer political barriers to financing the Stargate Project based on the assumption that AI supremacy is of greater strategic importance.

Conclusion

The Stargate Project marks a significant development in AI infrastructure, with OpenAI, SoftBank, Oracle and the U.S. government collaborating to create a network of AI-dedicated data centers. With a planned $500 billion investment, this initiative seeks to address the increasing computational demands of AI model development, positioning OpenAI to advance toward its goal of achieving AGI.
 
Oracle’s involvement, bolstered by partnerships with NVIDIA and its advancements in cloud infrastructure, highlights its growing role in the AI ecosystem and could advance Oracle’s Cerner ambitions. However, the project faces notable challenges, including substantial energy requirements and concerns over the availability of high-quality training data, which will require innovative solutions to address.
 
As one of the largest technology projects in recent history, Stargate reflects the evolving priorities in AI development and the broader strategic implications for technological leadership.

Analyst Summit Boston: PwC positions trust and cybersecurity as pillars for success in AI and business transformation

PwC has unquestionably built a brand around trust, as reflected in the two main themes woven throughout the Boston PwC Analyst Summit: risk and cybersecurity. In TBR’s view, PwC’s fundamental value proposition around trust and client intimacy reflects the firm’s strong governance, risk and compliance (GRC), cybersecurity and technology capabilities. TBR views risk and cybersecurity offerings as natural enablers for client discussions around business model reinvention and — when complemented by credible customer zero use cases across multiple domains, including AI — an extension of trust throughout a client’s ecosystem.

Despite its traditionally risk-averse culture, PwC was relatively quick to roll out an internal version of ChatGPT to all employees, and TBR believes the firm likely uncovered substantial best practices in how to manage change and limit downside risk associated with generative AI (GenAI).

Recognizing that cyber risk is no longer solely a technology issue but also a businesswide concern with financial and reputational consequences, PwC leaders repeatedly stressed that governance, not technology, must take the lead in ensuring robust cybersecurity strategies, supported by an ecosystem of partners. During a managed services use-case discussion, a client noted that PwC brought specific technology expertise and experience working with other business customers on this client’s specific problem. PwC’s relevant experience offered the client distinct benefits around risk mitigation, as part of their larger managed services engagement.

Additional highlights from PwC’s Boston event:

• An emphasis on data sustainability and GenAI is central to PwC’s long-term investment strategy, forming the foundation of every business line. PwC’s role as what TBR calls a “technology orchestrator” reflects the firm’s commitment to navigating the intersection of renewable energy, AI and other emerging technologies to help clients adapt and grow.
• Geopolitical tensions, particularly between the U.S. and China, remain a critical concern, with bipartisan consensus in the U.S. about addressing these issues underscoring the urgency. Despite challenges with R&D expense rules and state-level regulatory complexities, PwC has been advising clients to embrace sustainability and prepare for scenario planning.
• Mike Thiessen, PwC’s U.S. Chief Clients and Markets leader, noted that PwC’s approach to GenAI focuses on building bespoke workflows, integrating technical development with legal safeguards, and prioritizing user-driven curation. PwC recognizes that each GenAI project is unique, requiring tailored approaches and experience in addition to technical features. Under these conditions, PwC ensures AI implementation is secure and effective, aligning with the firm’s broader AI strategy.
• C. Lapierre, one of PwC’s U.S. Sustainability leaders, noted that mandatory reporting on climate action can serve as a catalyst for enterprises to get their data and sustainability strategy in order. Many companies now understand the scope and depth of the work needed to meet net-zero commitments, which were often made before the necessary parties had a full understanding of the difficulties and opportunities involved.

Artificial intelligence: Big bets, massive change, and all comes back to trust

On artificial intelligence (AI), PwC leaders during the Boston event noted that AI is reshaping everything, from brand and market positioning to operational strategy. PwC committed to a $1.5 billion investment in AI, an increase from its original announcement of $1 billion, underscoring the technology’s importance to the firm’s future.

The vendor is also focused on delivery excellence, specifically enhancing systems like SBLC to make them more intelligent and efficient. PwC noted that the U.S. firm spends 17 million hours annually bringing ISV partners into the production stage of engagements, clearly an area ripe for AI-driven optimization. Additionally, PwC leaders said that new partnerships around developing language models are revolutionizing SBLC, creating a new foundation while refactoring older offerings. According to PwC, this shift reflects the broader evolution of AI from an emerging technology to a general-purpose one that is now central to business strategies.

PwC leaders elaborated on potential business model implications of wider AI adoption, including the erosion of scale as a differentiator as AI-driven agentic workflows allow small companies to simulate large-scale operations. In addition, faster adoption rates help businesses more quickly realize the efficiency AI brings to back-office operations.

In TBR’s view, trust continues to be the linchpin for AI’s success; absent trust, AI’s potential will remain unrealized. As noted above, in previous TBR reports, and by PwC leaders repeatedly during the Boston event, PwC’s brand is built around trust.

Demonstrating the criticality of AI to PwC, firm leaders noted nine high-stakes AI investments, each worth $50 million, aimed at driving either top-line growth or cost reduction. One of the standout initiatives is Elly, an AI-powered system with a digital worker equivalent (DWE) of 2,500 — with each DWE representing 2,000 hours of work. This demonstrates the firm’s bullish outlook on AI’s return on investment.

Further, PwC leaders believe AI’s impact extends to enterprise resource planning (ERP) systems. While earlier designs emphasized efficiency, the focus has now shifted to functionality and achieving the best outcomes. In building these systems, PwC is ensuring that both design and implementation align with the firm’s strategic objectives. TBR agrees with PwC’s assessment of the potential for AI to massively improve ERP systems, provided enterprises fully trust their AI platform to handle mission-critical and proprietary data. Again, the emphasis is on trust.

Analyst Summit London: PwC leaders highlight megatrends and business model reinvention in effort to navigate transformation

In London, PwC leaders shared the following megatrends and commentary from the firm’s perspective:

• Climate change is negatively affecting social stability.
• Increasing demand for silicon chips and power, combined with a limited supply of chips and GenAI, is compounding clients’ risk factors.
• There is a global need to rethink power, global food supplies, demographics and migration, and industrial processes.
• Increase for on-demand mobility is quickening the pace of change in the automotive and oil & gas verticals.

To address the megatrends and capture opportunities, PwC is investing in three key areas both globally and in EMEA: sustainability, trust and business model reinvention (BMR). BMR requires helping clients operate in new ecosystems to find future areas of growth and reconsider how products and services will change. According to PwC EMEA leaders, AI, data and technology cut across all three, and every enterprise must be able to operate and be successful in these areas.

Going deeper on BMR, PwC EMEA leaders noted that, according to PwC’s most recent Global CEO Survey, approximately 45% of CEOs do not believe their business will be viable in the next 10 years without reinvention, up from 39% of respondents in 2023. Not surprisingly, clients are asking for PwC’s advice on strategic planning and how to invest today to be successful tomorrow. PwC has a methodology to assist clients with their transformations, starting by sitting with clients and discussing their needs to gain a deep understanding of their design and implementation abilities and industry knowledge and co-creating an approach that is industry-led and industry-focused.

According to PwC EMEA leaders, the PwC BMR framework helps clients identify business growth areas, such as the development of new ecosystems and the creation of new products and services to pursue value and underpenetrated areas of revenue. For example, through a client session, PwC walked through a BMR transformation in which PwC helped create a new company from scratch following a sale of the business. Through the new business, the client sought to overcome rising cost pressures as well as crop and agricultural challenges that were disrupting its ability to deliver its products. Additionally, changing its primary delivery method to include a different product allowed the new company to focus on driving value and creating new revenue streams.

In a separate client example, PwC created a new platform to transform how a university engaged with prospective students. Through the platform, the university sought to advance its technology, position for the future, strengthen trust, and improve online engagement and opportunities. PwC used its BMR framework in both of these client examples, guiding the evolution of existing business environments to identify needs and pursue next steps to future-proof operations.

On a technology-specific note, PwC EMEA leaders highlighted the firm’s Industry Edge approach, explaining that PwC’s first step is building a differentiated way to enable transformation outcomes tailored for each industry. PwC applies data and tech assets, gathers use cases to understand the best way to make decisions, and establishes preconfigured solutions that support business transformation, all while leveraging technology alliances.

The key, according to PwC EMEA leaders, is that the firm provides not only a consulting approach but also all of PwC’s capabilities, including technology, regulatory, risk and even tax services. Critically, in TBR’s view, PwC is not going to clients and selling Industry Edge; instead, the firm is adapting elements of Industry Edge that are applicable to specific clients.

PwC EMEA leaders noted that “every day PwC follows two principles”: 1) emphasizing client centricity, which PwC and TBR both recognize sounds obvious and is not differentiating but, according to PwC, is a key to success; and 2) a one-firm approach: a global network of firms that come together to seamlessly deliver services to clients. Pursuing these initiatives enables PwC to deliver reinvention and transformation services through an industry play, leading with the right approach to drive value, cocreation and evolution services.

TBR’s expectations for PwC in 2025

In TBR’s view, PwC’s twin analyst events at the end of 2024 showed a firm shifting into a new gear, perhaps reflecting leadership changes or the changing environment for professional services as the GenAI age begins to mature and PwC’s strategic investments and its own business model reinvention begin to take shape.

PwC’s early epiphany around artificial intelligence centered on understanding both the necessity of accessing client data and the implication that if a client’s data were a mess, AI would be useless. The firm steadily invested in the expertise and capabilities needed to assist clients with their AI journeys, accelerating that investment when GenAI hit the market. Notably, PwC continued its deeply ingrained practice of investing substantially in its own people, bringing AI and then GenAI solutions to the firm’s professionals and leaning into the customer zero approach.

Now PwC is fine-tuning its own business model and looking to accelerate technology adoption, redefine (or at least continually improve) global operations and grow its Managed Services business. In TBR’s view, it is not a reinvention … yet. Critically, as PwC transforms itself the firm remains grounded in its core value to clients: trust.

2025 Predictions is a series of special reports examining market trends and business changes TBR expects in the coming year for AI PCs, cloud market share, digital transformation, GenAI, ecosystems and alliances, and 6G.

Top Predictions for GenAI in 2025

1. 1. GenAI will continue to revolutionize mission-critical functions and day-to-day operations at federal civilian, defense and intelligence agencies
   2. Cloud vendors will splurge on AI investments even as customers grow apprehensive
   3. Infrastructure vendors’ focus will shift from serving cloud companies to making a massive push in enterprise AI
   4. The energy problem is likely to slow the pace of AI market development significantly
   5. GenAI upends pyramids, even as enterprises slow their AI roll

The state of AI and GenAI in 2024

In 2024 the AI and generative AI (GenAI) landscape faced four key challenges: rising costs, driven by growing investments in data and infrastructure; talent and training gaps; regulatory uncertainty; and macroeconomic pressures. These obstacles will persist into 2025, with additional challenges in the GenAI space becoming increasingly evident.
 

According to TBR research, the waning GenAI hype has exposed underlying issues, including expensive cloud commitments and fragmented data strategies, creating opportunities for companies that emphasize ROI, complementary technologies and cost management. Adding to the complexity, rising energy costs and heightened awareness of GenAI-related security risks are further shaping this uncertain yet opportunity-filled environment.
 
But after two years of GenAI disruption, a clear trend is emerging across the ecosystem: strategic partnering is becoming essential. Companies such as McKinsey & Co, Wipro, Dell Technologies, Amazon Web Services (AWS) and NVIDIA are adopting this approach, recognizing that no single organization can deliver comprehensive GenAI-enabled solutions alone. Instead, success increasingly depends on leveraging the technology and expertise of ecosystem partners.
 
To read the entire 2025 GenAI Predictions special report, request your free copy today!

AWS re:Invent 2024 Overview

Matt Garman kicked off his first re:Invent conference as AWS’ CEO, reinforcing a strategy that has been rooted in AWS’ DNA for over a decade. That is the notion of “building blocks,” in other words, the 220-plus native services AWS offers that cater to a specific workload and, when used together in a modular fashion, can address specific use cases. This approach of offering the broadest set of out-of-the-box, user-friendly tools to attract new applications, spin the IaaS meter and feed the lucrative flywheel effect AWS is known for, has naturally garnered a lot of interest with developer and startup communities. But Garman was quick to remind us how far AWS has come in catering to the large enterprise.

As an example, Garman welcomed JPMorgan Chase Global CIO Lori Beer to the stage to share the company’s aggressive cloud transformation, which consisted of growing from 100 applications on AWS in 2020 to over 1,000 today, powered by a range of services, from Graviton chips to SageMaker to AWS’ fastest-growing service, Aurora. If this success story is any indication and if we factor in the feedback from our own C-Suite discussions, this building-block approach appears to be resonating, solidifying AWS’ position as the leading IaaS & PaaS provider. But with every new application poised to have some AI or generative AI (GenAI) component, this budding technology is raising the stakes, and the hybrid-multicloud reality means customers have a lot of options when it comes to crafting new workloads.

Compute is foundational building block, with a heavy focus on AI training

Today, AWS offers over 850 Amazon Elastic Compute Cloud (EC2) instance types, and on average, 130 million new EC2 instances are launched daily. This pace of innovation and scale is largely due to AWS’ approach to the virtualization stack dating back to 2012 with the Nitro System, which other hyperscalers have since emulated in their own way, making compute the foundational building block and hallmark of AWS’ success. Though at the event AWS touted its commitment to NVIDIA, with support for Blackwell GPUs coming online next year, and general-purpose workloads via Graviton, a lot of the focus was on AI training.
 

Since it first launched its Trainium chip in 2020, AWS has served the needs of AI training workloads, but now AI-driven ISVs like Databricks and Adobe, seem to have an appetite for these chips, hoping to deliver cost and performance efficiencies to their wide swath of customers that also run on AWS. It is why AWS launched Trainium 2 and is making these EC2 instances, which encompass 16 Inferentia chips, generally available following year in private preview. AWS also reinforced its commitment to continuing to push the compute boundaries on AI training, announcing that Trainium 3, which will be available later next year, will reportedly offer double the compute power of Trainium 2.

Rise of the distributed database

Another core building block of the cloud stack is the database. Distributed databases are nothing new but have been picking up steam as customers in certain industries, including the public sector, want to have data stored within country borders but scale across different regions. At the event, AWS introduced Aurora DSQL, a distributed SQL database, that at its core isolates the transaction processing from the storage layer, so customers can scale across multiple regions with relatively low latency.
 

This development comes at an interesting time in the cloud database market. Database giant Oracle is shaking up the market, making its services available on all leading clouds, including AWS, with the Oracle Database@AWS service now in limited preview. But AWS is focused on choice. While the IaaS opportunity to land Oracle workloads was too good to pass up, particularly when Microsoft Azure and Google Public Cloud (GCP) are doing the same thing, AWS wants to continue pushing the performance boundaries of its own databases. In fact, it was Google Cloud that AWS targeted at the event, boasting that Aurora DSQL handles read-write operations four times faster than Google Spanner.
 

Watch On Demand: Monetizing GenAI: Cloud Vendors’ Investment Strategies and 2025 Outlook

Creating more unity between the data and AI was somewhat inevitable

Jumping on the platform bandwagon, AWS morphs SageMaker into SageMaker AI

AWS launched SageMaker seven years ago, and the machine learning development service quickly emerged as one of AWS’ most popular, innovative offerings, adding 140 new features in the last year alone. But when GenAI and Amazon Bedrock came on the scene, SageMaker found a new home in the GenAI portfolio, acting as the primary tool customers use to fine-tune foundation models they access through the Bedrock service. So, from a messaging perspective, it was not surprising to see AWS announce that SageMaker is becoming SageMaker AI. But what is notable is how SageMaker AI is being marketed, integrated and delivered.

First, AWS VP of Data and AI Swami Sivasubramanian introduced the SageMaker AI platform as a one-stop shop for data, analytics and AI, underpinned by SageMaker Unified Studio, which consolidates several disparate AWS data and analytics tools, from Redshift to Glue, into a single environment. Just as importantly, Unified Studio offers a native integration with Bedrock so customers can access Bedrock for GenAI app development within the same interface, as well as Q Developer for coding recommendations.
 

The second important piece is how data is accessed for SageMaker AI. The foundational layer of the SageMaker AI platform is SageMaker Lakehouse, which is accessible directly through Unified Studio, so customers can make a single copy of data regardless of whether it is sitting in data lakes they created on S3 or the Redshift data warehouse. This means customers do not have to migrate any existing data to use SageMaker Lakehouse, and they can query data stored in Apache Iceberg format as it exists today. From competitors and/or partners like Microsoft, Oracle and Databricks, we have seen big leaps forward in the data lake messaging, so the SageMaker Lakehouse announcement, combined with traditional S3 developments like S3 Tables for the automatic maintenance of Apache Iceberg tables, aligns with the market and is a big reaffirmation of the Apache Iceberg ecosystem.

In our view, SageMaker AI is a big development for a couple of reasons. First and foremost, it could go a long way in addressing one of the top concerns we often hear from customers pertaining to AWS, which is that they want consistent data without having to leverage multiple disparate services to carry out a task. SageMaker is still available as a stand-alone service for customers that have a specific requirement, but we suspect a lot of customers will find value in serving the full AI life cycle, from initial data wrangling up to model development as part of a unified experience. Since AWS launched the first EC2 instance in 2009, formalizing cloud computing as we know it today, we have watched the market gradually shift toward more complete, integrated solutions. From IBM to Microsoft, many of IT’s biggest players take a platform-first approach to ease common pain points like integration and cost in hopes of enabling true enterprise-grade digital transformation, and SageMaker AI signifies a step in this direction.
 

Secondly, SageMaker AI aligns AWS more closely with what competitors are doing to better integrate their services and selling data and AI as part of the same story. Considering the consolidation of services, data lake architecture and copilot (Amazon Q) integration, Microsoft Fabric is the most notable example, and while there are big technical differences between the two platforms, you can now draw parallels between both companies and how they are trying to better address the data layer in a broader AI pursuit. For context, TBR’s own estimates suggest Microsoft Azure (IaaS & PaaS) will significantly narrow, if not beat, AWS’ revenue lead by 2027, and a lot of customers we talk to today give Microsoft a leg up on data architecture. Nothing can displace Microsoft’s ties to legacy applications and the data within them, but SageMaker AI is clearly in step with the market, and if AWS can effectively engage partners on the data side, this solution could help AWS retain existing and compete for new workloads.

AWS’ values of breadth and accessibility extend to Bedrock

Because Bedrock and SageMaker go hand in hand, having a Bedrock IDE (integrated development environment) directly in SageMaker makes a lot of sense. This means within SageMaker AI, customers can access all the foundation models Bedrock supports and the various capabilities, like Agents and Knowledge Bases, that AWS has been rolling out to its audience of “tens of thousands” of Bedrock customers, which reportedly implies five times the growth in the last year alone. In true AWS fashion, offering the broadest set of foundation models is integral to the Bedrock strategy. This includes adding support for models from very early-stage AI startups like Luma and poolside, getting them tied to AWS infrastructure early on, and growing them into competitive ISVs over time.
 

Another key attribute of Bedrock has always been democratization and making access to the foundation models as seamless as possible through a single API hosting experience. In line with this strategy, AWS launched Bedrock Marketplace to make it easier for customers to find and subscribe to the 100-plus foundation models Bedrock supports, including those from Anthropic, IBM and Meta, as well as Amazon itself. AWS is the king of marketplaces, so having a dedicated hub for AI models that are from startups and are enterprise grade as part of a single experience is certainly notable and further fueling the shift in buyer behavior toward self-service.

Partners take note: Security, modernization and marketplace

Despite all the talk around AI and GenAI, security remains the No. 1 pain point when it comes to cloud adoption and was a big theme in the partner keynote. AWS’ VP of Global Specialists and Partners, Ruba Borno, reinforced the importance of AWS’ various specialization programs to demonstrate skills to clients in key areas including security. During the keynote, AWS announced new security specializations, including one around AWS’ Security Lake service. This is a pretty telling development for partners; Security Lake was a service essentially designed with partners in mind, allowing many services-led firms to build integrations and attach managed services. Now these partners can demonstrate their skills with Security Lake to customers, along with other areas in the realm of security, such as digital sovereignty, which aligns with AWS’ upcoming launch of the European Union (EU) Sovereign Cloud region.

Aside from security, AWS emphasized modernization and the need for partners to think beyond just traditional cloud migration opportunities. It is why AWS launched new incentives for modernization, including removing funding caps within MAP (Migration Acceleration Program), and rebranded the AWS Migration Competency as the AWS Migration and Modernization Competency. This is pretty telling of where AWS wants partners to focus and, in many cases, change the conversation with buyers, emphasizing the role of modernizing as part of the migration process. Considering how difficult it has become for services players to compete on migration services, as well as the fact that modernization could set the stage for more GenAI usage with tools like Q Developer, we believe this is aligned with where many global systems integrators are headed anyway.

Expanding the reach of AWS Marketplace

No partner discussion would be complete without AWS Marketplace, AWS’ pervasive hub where customers can buy and provision software using their existing cloud spend commitments. Year to date, AWS reports that essentially all of its top 1,000 customers buy on the AWS Marketplace, and usage spans several industries, including the public sector, which has reportedly transacted over $1 billion on AWS Marketplace in the past year. At re:Invent, AWS continued to take steps to expand the reach of AWS Marketplace, getting partners to better engage customers through this channel, with the availability of Buy with AWS. This option allows customers to access AWS Marketplace directly from a partner’s website.

Final thoughts

re:Invent showcased how AWS is pushing the envelope, in both breadth and capability, on the compute, database and AI building blocks customers use to solve specific use cases in the cloud. This approach, coupled with innovations like Bedrock Marketplace and a commitment to early-stage startups, speaks to how AWS will continue to lean into the core strengths that have made the cloud provider what it is today. But just as notably, offerings like SageMaker AI and an alliance with competitor Oracle show how AWS is embracing new tactics and elevating its role within the cloud ecosystem.