AI/ML orchestration on Cloud Run documentation

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Cloud Run is a fully managed platform that lets you run your containerized applications, including AI/ML workloads, directly on Google's scalable infrastructure. It handles the infrastructure for you, so you can focus on writing your code instead of spending time on operating, configuring, and scaling your Cloud Run resources. Cloud Run's capabilities provide the following:

  • Hardware accelerators: access and manage GPUs for inference at scale.
  • Frameworks support: integrate with the model serving frameworks you already know and trust such as Hugging Face, TGI, and vLLM.
  • Managed platform: get all the benefits of a managed platform to automate, scale, and enhance the security of your entire AI/ML lifecycle while maintaining flexibility.

Explore our tutorials and best practices to see how Cloud Run can optimize your AI/ML workloads.

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Documentation resources

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Explore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services.
Use case
Use cases

Optimize cold-start latency for containerized LLM inference on using serverless configuration settings and architecture design pattern tuning.

Cold starts Latency Optimization LLMs

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Use cases

Configure and enforce Model Context Protocol (MCP) authorization rules to secure remote tool connectivity for AI agents deployed on .

Security MCP Agents

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Use cases

Deploy full-stack applications to directly from Google AI Studio's Build Mode with integrated Firebase and backup support.

AI Studio Firebase vibe coding

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Use cases

Use NVIDIA L4 GPUs on for real-time AI inference, including fast cold-start and scale-to-zero benefits for Large Language Models (LLMs).

GPUs LLMs

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Use cases

Learn how to use for production-ready AI applications. This guide describes use cases such as traffic splitting for A/B testing prompts, RAG (Retrieval-Augmented Generation) patterns, and connectivity to vector stores.

AI applications traffic splitting for A/B testing RAG patterns vector stores connectivity to vector stores

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Use cases

One-click deployment from Google AI Studio to and the MCP (Model Context Protocol) server to enable AI agents in IDEs or agent SDKs and deploy apps.

MCP servers deployments

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Use cases

Integrate NVIDIA L4 GPUs with for cost-efficient LLM serving. This guide emphasizes scale-to-zero and provides deployment steps for models like 2 with Ollama.

LLMs GPU Ollama Cost Optimization

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Use cases

Decouple large model files from the container image using . Decoupling improves build times, simplifies updates, and creates a more scalable serving architecture.

Model Packaging Best Practices Large Models

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Use cases

Use the Cog framework that is optimized for ML serving to simplify packaging and deployment of containers to .

Cog Model Packaging Deployment Tutorial

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Use cases

Use for lightweight ML inference and build a cost-effective monitoring stack by using native services like and .

Monitoring MLOps Cost Efficiency Inference

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