According to Richard Seroter, Google Cloud Run simplifies hosting for developers by allowing them to deploy code or containers without the need for a specific 'Cloud Run' app. Sara, an expert in the product, reflects on five critical lessons she would share with her past self to optimize the use of Cloud Run.

According to Richard Seroter, Google Cloud Run now supports NVIDIA RTX 6000 PRO Blackwell GPUs for AI workloads, enabling teams to serve 70B-plus parameter models without managing underlying infrastructure (source: Richard Seroter on X and Google Cloud blog). He highlights that drivers are pre-installed, no capacity reservations are required, and serverless instances offer 20 to 44 vCPUs with 80 to 176 GiB memory to streamline large language model inference and other high‑throughput tasks (source: Richard Seroter on X and Google Cloud Run documentation). This update allows on-demand scaling of LLM inference on Cloud Run while removing GPU infrastructure administration overhead for developers (source: Richard Seroter on X and Google Cloud blog).

According to @rseroter, Google Cloud Run services and functions now support .NET 10; source: @rseroter; Google Cloud blog. According to @rseroter, Google Cloud also released a new .NET library that uses the Microsoft Extensions AI abstraction to integrate Gemini (Vertex AI) into .NET applications; source: @rseroter; Google Cloud blog. According to @rseroter, the update enables standardized Gemini integration within the Microsoft Extensions AI ecosystem for multi-model .NET development; source: @rseroter; Google Cloud blog.