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In 2024, Google Cloud hired Saurabh Tiwary, a former Microsoft corporate vice president associated with Copilot, as general manager and vice president of Cloud AI. It also brought in Raj Pai, a former AWS vice president with product leadership experience at Amazon EC2, as vice president of product for Cloud AI, reporting to Tiwary. The appointments signaled an effort to strengthen Google Cloud’s AI product organization—not a change in leadership over all of Google’s AI work.
Who Google Cloud hired
Saurabh Tiwary: Microsoft Copilot experience, and a previous Google role
CRN reported that Tiwary joined Google Cloud as general manager and vice president of Cloud AI after 11 years at Microsoft. According to the report’s account of his LinkedIn profile, he worked across engineering, product-management and scientific teams involved in Microsoft Copilot, with responsibilities touching Microsoft 365, Windows Copilot, Bing and other business units. That background is relevant to enterprise AI products, but it should not be read as evidence that he alone led all of Copilot.
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Tiwary had also worked in Google’s search organization from 2010 to 2013, according to CRN. His move was therefore a return to Google as well as a transition from Microsoft. CRN’s 2024 report attributed the appointment and career details to public profile information and announcements.
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Pai joined as Google Cloud vice president of product for Cloud AI and reported to Tiwary, CRN reported. He had spent about 10 years at AWS, where he led product management for Amazon EC2 and general management for related services. CRN also reported that he served as an EC2 product-management director from 2014 to 2019.
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Pai’s experience was not limited to AWS: he had spent 15 years at Microsoft, including work as a program manager for Office 365 Exchange Enterprise Cloud. That combination gives him experience across two major cloud competitors and enterprise software, though it does not establish what specific products or decisions he would own at Google.
What “leading AI” meant in this report
The reported roles were for Google Cloud’s Cloud AI business unit: Tiwary as its general manager and vice president, and Pai in product leadership. The hiring story does not say they were put in charge of Google DeepMind, all Alphabet AI research, or every Gemini product for consumers. Nor does it announce an acquisition, a new foundation model or the replacement of existing Google AI leadership.
The product context has since evolved. Google now presents Gemini Enterprise Agent Platform, formerly Vertex AI, as a platform for building, deploying, governing and optimizing enterprise AI agents and models. Its described capabilities include model access, tuning and evaluation, deployment, notebooks, pipelines and vector search, alongside connections to Google Cloud services such as BigQuery. That is the current product framing; it should not be projected backward as the terminology used in the 2024 hiring report.
Why the hires could matter to Google Cloud
The two backgrounds point to complementary areas of experience. Tiwary’s reported Copilot work is relevant to packaging AI capabilities into products people can use across business workflows. Pai’s EC2 and cloud-product experience is relevant to infrastructure, developer services and managing a broad cloud portfolio. Both had worked inside organizations competing with Google Cloud, and Pai had experience at both Microsoft and AWS.
Those connections make the appointments a plausible move to strengthen product execution and enterprise positioning. They do not confirm Google’s internal hiring motives, guarantee better products, or show that either executive transferred confidential information. The careful conclusion is that Google Cloud recruited experienced operators from its rivals as it sought to compete more effectively in enterprise AI.
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Google, AWS and Microsoft: different routes into enterprise AI
In the hiring story’s market context, Synergy Research Group estimates cited by CRN put global cloud infrastructure-services share in the first quarter of 2024 at 31% for AWS, 25% for Microsoft and 11% for Google Cloud, or 67% combined. These are historical Q1 2024 estimates, not current market-share figures. Market share also depends on the research firm and how the market is defined.
| Provider | Current platform positioning | What buyers should distinguish |
|---|---|---|
| Google Cloud | Gemini Enterprise Agent Platform emphasizes agents, model options and evaluation, deployment, and integration with Google Cloud data and infrastructure. | Consider its fit with your existing Google Cloud estate, data systems, governance needs and expected usage. Platform features and billing vary by service. |
| AWS | Amazon Bedrock offers access to multiple foundation models and capabilities for agents, customization, evaluation and cost management. | It may suit organizations already invested in AWS, but model inference and other features can have separate usage-based charges. Check the live pricing details for the relevant model, region and features. |
| Microsoft | Microsoft Foundry is positioned as an Azure AI development and deployment environment with consumption-based pricing and separate billing models for services and features. | Foundry is a platform for building and deploying AI; Microsoft 365 Copilot is a workplace product integrated into Microsoft applications. They are not interchangeable purchases. |
These platforms overlap, but they are not identical. A company choosing an agent-development platform is solving a different problem from a company buying an AI assistant for employees. Model access, data integration, security controls, regional availability, support, and usage costs can all affect the decision. Pricing comparisons are especially difficult because charges may depend on models, tokens, compute, storage, networking, region and discounts.
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Executive appointments are an intent signal, not a procurement reason by themselves. For customers assessing Google Cloud AI, the meaningful evidence is what the company delivers and supports:
- Product delivery: Are agent-building, evaluation, deployment and governance tools improving in ways that address real workloads?
- Model choice: Which models are available for the task, and what are the trade-offs in performance, control and cost?
- Data and security: Can the platform meet the organization’s requirements for access controls, privacy, residency and compliance?
- Integration: How well does it fit the existing data estate, applications and cloud operations, and what migration work would it require?
- Economics and adoption: Can teams estimate and govern usage costs, and is there evidence of sustained customer use rather than announcements alone?
These are also useful criteria when comparing Bedrock or Foundry. The right choice depends on the workload and the organization’s existing systems—not simply which provider has hired a prominent executive.
What the appointments do not prove
The 2024 hires do not show that Google Cloud surpassed AWS or Azure, that its AI products were technically superior, or that the appointments increased revenue or market share. They do not establish that Tiwary personally ran every Microsoft Copilot initiative or that Pai was responsible for all of EC2. The report also provides no basis to infer that either executive brought confidential competitor information. The impact has to be judged by product delivery, customer adoption, operating economics and the quality of support—not job titles alone.
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