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

Use cases

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

Security MCP Agents

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

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

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

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

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

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

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

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