Google Vertex AI
Google Vertex AI is a fully managed machine learning and AI development platform offered by Google Cloud. It integrates a complete MLOps workflow covering data preparation, model training, model evaluation, and deployment. The platform supports custom model training and access to Google's pretrained large language models such as the Gemini series, while providing enterprise-grade security and compliance, service-level agreements (SLAs), and deep integration with the Google Cloud ecosystem.
Core Facts
Timeline (last 90 days)
Cline itself does not train models, but acts as a client forwarding requests to various providers (such as OpenRouter, GitHub Copilot, Google Vertex AI, DigitalOcean, etc.).
The default models of providers including 302.AI, Cortecs, CrossModel, DevPass, DigitalOcean, Eden AI, GitHub Copilot, Google Vertex AI, LLM Gateway, Meta, Moark, NanoGPT, NEAR AI Cloud, Neuralwatt, Novita AI, Ofox, OpenCode Go, Requesty, and Vivgrid have changed, with most switching to Claude Haiku 5.5 or Muse Wonder 1.0.
The focus of this round of Google Vertex AI updates is introducing more large language models (LLMs) and diffusion models, giving developers greater freedom of combination and thereby covering richer multimodal scenarios.
The Claude apps gateway is an intermediary proxy layer provided by Anthropic for enterprise customers, responsible for routing Claude Code requests to cloud upstreams such as AWS Bedrock, Google Vertex AI, and Azure AI Foundry, while uniformly handling authentication, rate limiting, failover, and auditing.
The default models for multiple providers were switched to Claude Haiku 5.5, including Google Vertex AI (changed from Claude Sonnet 5.5 to Claude Haiku 5.5), Cortecs, DevPass, Eden AI, GitHub Copilot, LLM Gateway, NanoGPT, OpenCode Go, Requesty, and Vivgrid.
Supporting 500+ models typically means the tool achieves broad model access through compatibility with the OpenAI API standard, Anthropic API, Google Vertex AI, and local inference frameworks such as Ollama.
The mainstream cloud platforms AWS SageMaker, Google Vertex AI, and Azure ML all provide managed pipelines from training to deployment.
在Hugging Face、OpenAI API、Google Vertex AI等平台成熟后，调用高性能预训练模型的门槛已降至只需几行Python代码
All Facts (9)
Anthropic provides model access through its own API platform and cloud service channels such as Amazon Bedrock and Google Cloud Vertex AI
80%Unverified在Hugging Face、OpenAI API、Google Vertex AI等平台成熟后，调用高性能预训练模型的门槛已降至只需几行Python代码
85%UnverifiedThe default models for multiple providers were switched to Claude Haiku 5.5, including Google Vertex AI (changed from Claude Sonnet 5.5 to Claude Haiku 5.5), Cortecs, DevPass, Eden AI, GitHub Copilot, LLM Gateway, NanoGPT, OpenCode Go, Requesty, and Vivgrid.
70%UnverifiedThe mainstream cloud platforms AWS SageMaker, Google Vertex AI, and Azure ML all provide managed pipelines from training to deployment.
60%UnverifiedThe default models of providers including 302.AI, Cortecs, CrossModel, DevPass, DigitalOcean, Eden AI, GitHub Copilot, Google Vertex AI, LLM Gateway, Meta, Moark, NanoGPT, NEAR AI Cloud, Neuralwatt, Novita AI, Ofox, OpenCode Go, Requesty, and Vivgrid have changed, with most switching to Claude Haiku 5.5 or Muse Wonder 1.0.
50%UnverifiedCline itself does not train models, but acts as a client forwarding requests to various providers (such as OpenRouter, GitHub Copilot, Google Vertex AI, DigitalOcean, etc.).
50%UnverifiedThe Claude apps gateway is an intermediary proxy layer provided by Anthropic for enterprise customers, responsible for routing Claude Code requests to cloud upstreams such as AWS Bedrock, Google Vertex AI, and Azure AI Foundry, while uniformly handling authentication, rate limiting, failover, and auditing.
50%UnverifiedSupporting 500+ models typically means the tool achieves broad model access through compatibility with the OpenAI API standard, Anthropic API, Google Vertex AI, and local inference frameworks such as Ollama.
50%UnverifiedThe focus of this round of Google Vertex AI updates is introducing more large language models (LLMs) and diffusion models, giving developers greater freedom of combination and thereby covering richer multimodal scenarios.
50%