I’m building n8n workflows for enterprise customers who have strict data privacy and compliance requirements.
I’ve noticed a significant difference in how Google’s models are used in n8n:
Google Gemini: Uses a standard API key, and has a simple “Standard Node”. It’s very fast and easy to use for basic prompt/response tasks.
Google Vertex AI: Uses GCP Service Accounts (which our enterprise clients actually want for compliance). However, it seems it must be used within the “Advanced AI” ecosystem (connected to an AI Agent or Basic LLM Chain).
My questions are:
Am I correct that there is no simple “Standard Node” for Vertex AI, and it forces you into the LangChain/Agent ecosystem?
If I just want the simple, linear behavior of the standard Gemini node, but with the enterprise security of Vertex AI, is using a “Basic LLM Chain” the recommended approach? Are there downsides to doing it this way?
@rgrzesk
At least from what I’m seeing, Vertex AI in n8n doesn’t currently have a standalone “standard” node like Gemini does. If I needed the same simple prompt/response flow with Service Account auth, I’d treat Basic LLM Chain + Vertex Chat Model as the closest equivalent. The tradeoff is mainly the extra LangChain/sub-node layer, not agent overhead unless I intentionally add agent features.
Hi @rgrzesk you are correct, the google vertex AI node is a cluster node, which can only be used by AI Agent, Text Classifier, Information Extractors and like these which require an LLM, on the other hand the Gemini has a series of very useful nodes.
Using llm chain with the vertex AI is good for basic stuff where you just want some text to be used as an input and linear output, but i would recommend using the AI agent as it also supports extra tools which can be Memory or MCP tools and stuff like that, so why to get limited to LLM Chain when you can increase the performance and give more context to the AI agent to refine and expect a better output.
A follow-up question regarding data security: When building for enterprise clients who strictly forbid their data from being used to train foundation models, are both Google nodes treated the same?
Or does using the Vertex AI node (via the LangChain/Agent ecosystem) offer stronger contractual data privacy and network boundaries compared to using the standard Google Gemini node?
What is the recommended path when-in-doubt recommendation for strict enterprise environments?
@rgrzesk although google does not use enterprise plan API data, but i would recommend the Vertex with the most frontier plan to ensure the data would not get collected.
@rgrzesk skip the langchain layer entirely, just hit the Vertex AI REST API with an HTTP Request node using your service account token — you get the same simple linear flow as the standard Gemini node but with GCP auth and data residency controls
plug your Google Service Account creds into the HTTP Request node, swap in your GCP project ID and region in the Set node, done — no langchain overhead, full Vertex data residency guarantees.