Google Cloud

Overview

Google Cloud is Google’s enterprise cloud platform, spanning infrastructure, developer tooling, security, identity, and AI services. In the context of AI product work, it matters not just as hosting infrastructure but as an operational layer for access control, collaboration, governance, and deployment around models and APIs such as Gemini. For AI Product Managers, Google Cloud often shows up where product experimentation meets enterprise requirements: project-based permissions, shared environments, auditability, and scalable integration into production systems.

In recent mentions, Google Cloud appears in two especially PM-relevant ways. First, it became the control plane for Gemini API key access through Google Cloud projects and the Google Cloud Console, which is important for teams managing shared AI resources across collaborators. Second, it is positioned as a strategic infrastructure partner in the buildout of agentic AI systems, including work with NVIDIA and ties to the A2A ecosystem later connected to the Linux Foundation. Together, these signals make Google Cloud relevant to PMs thinking about both near-term execution and longer-term platform strategy.

Key Developments

• 2026-02-07 — Logan Kilpatrick highlighted that Gemini API keys were tied to Google Cloud projects, allowing collaborators to be granted access through the Google Cloud Console. This points to a more enterprise-ready permissioning model for teams using Gemini.
• 2026-02-12 — A2A was referenced as having been announced by Google Cloud in April 2025 and later donated to the Linux Foundation in June 2025, with IBM’s ACP merging into A2A soon after. This suggests Google Cloud played an early role in shaping emerging agent-to-agent interoperability standards.
• 2026-03-17 — NVIDIA AI expanded its partnership with Google Cloud to co-engineer infrastructure for the next generation of agentic AI. This reinforces Google Cloud’s position as a foundational platform for more advanced autonomous and multi-agent systems.

Relevance to AI PMs

• Use project-scoped access to operationalize team collaboration. If your team ships with Gemini or adjacent Google AI services, Google Cloud projects provide a practical way to manage who can access keys, environments, and shared resources without relying on ad hoc credential sharing.
• Plan for enterprise governance earlier. Google Cloud matters when prototypes become products: IAM, project boundaries, console-based administration, and audit-friendly workflows help PMs move from solo experimentation to compliant cross-functional execution.
• Track platform bets around agentic systems. Google Cloud’s connection to A2A and its partnership with NVIDIA are useful signals for PMs evaluating future platform dependencies, interoperability standards, and infrastructure readiness for agent-based product experiences.

Related

• NVIDIA — Partnered with Google Cloud to build infrastructure for agentic AI, signaling the importance of cloud-plus-accelerator ecosystems.
• Agentic AI — Google Cloud is increasingly framed as a core platform layer for developing and scaling agentic systems.
• A2A (Agent2Agent Protocol) — Announced by Google Cloud and later donated to the Linux Foundation, linking Google Cloud to interoperability standards for agents.
• Linux Foundation — Received A2A after its initial launch, showing how Google Cloud-related initiatives may evolve into broader ecosystem standards.
• IBM — Its ACP agent communication protocol reportedly merged into A2A, strengthening the relevance of Google Cloud’s early role in the protocol’s ecosystem.
• Logan Kilpatrick — Notably referenced the change tying Gemini API keys to Google Cloud projects, a concrete example of Google Cloud’s operational role.
• Gemini API — A major touchpoint for PMs, with Google Cloud serving as the project and permissioning layer around API access.