chrisheecho

Thanks! - I like it too :)

This sounds accurate. I see myself as a Pain Manager more than a Product manager. Product just solves the pain that users have ;)

Sometimes we get it right the first time we launch it, I think most of the time we get it right over a period of time.

Trying to do a little bit better everyday and ship as fast as possible!

You don't have to specify role when you call through Python (https://cloud.google.com/vertex-ai/generative-ai/docs/start/...)

(which I think is what you are using but maybe i'm wrong).

Feel free to DM me on @chrischo_pm on X. Stuff that you are describing shouldn't happen

Can you tell me the exact instance when this happened please? I will take this feedback back to my colleagues. But in order to change how we behave I need a baseline and data

ooh i like!

I think you are talking about generativeai vs. vertexai vs. genai sdk.

And you are watching us evolve overtime to do better.

Couple clarifications 1. Going forward we only recommend using genai SDK 2. Subtle API differences - this is a bit harder to articulate but we are working to improve this. Please dm at @chrischo_pm if you would like to discuss further :)

We have Python/Go in GA.

Java/JS is in preview (not ready for production) and will be GA soon!

one step ahead of you ;)

We have moved our quota system to Dynamic Shared Quota (https://cloud.google.com/vertex-ai/generative-ai/docs/quotas) for 2.0+ models. There are no quotas in DSQ. If you need a guaranteed throughput there is an option to purchase Provisioned Throughput (https://cloud.google.com/vertex-ai/generative-ai/docs/provis...).

lemming, this is super helpful, thank you. We provide the genai SDK (https://github.com/googleapis/python-genai) to reduce the learning curve in 4 languages (GA: Python, Go Preview: Node.JS, Java). The SDK works for all Gemini APIs provided by Google AI Studio (https://ai.google.dev/) and Vertex AI.

Ramoz, good to hear that native Structured Outputs are working! But if the docs are 'confusing and partially incomplete,' that’s not a good DevEx. Good docs are non-negotiable. We are in the process of revamping the whole documentation site. Stay tuned, you will see something better than what we have today.

That’s correct! You can send images through uploading either the Files API from Gemini API or Google Cloud Storage (GCS) bucket reference. What we DON’T have a sample on is sending images through bytes. Here is a screenshot of the code sample from the “Get Code” function in the Vertex AI studio. https://drive.google.com/file/d/1rQRyS4ztJmVgL2ZW35NXY0TW-S0... Let me create a feature request to get these samples in our docs because I could not find a sample too. Fixing it

simonw, 'Google's service auth SO hard to figure out' – absolutely hear you. We're taking this feedback on auth complexity seriously. We have a new Vertex express mode in Preview (https://cloud.google.com/vertex-ai/generative-ai/docs/start/... , not ready for primetime yet!) that you can sign up for a free tier and get API Key right away. We are improving the experience, again if you would like to give feedback, please DM me on @chrischo_pm on X.

We built the OpenAI Compatible API (https://cloud.google.com/vertex-ai/generative-ai/docs/multim...) layer to help customers that are already using OAI library to test out Gemini easily with basic inference but not as a replacement library for the genai sdk (https://github.com/googleapis/python-genai). We recommend using th genai SDK for working with Gemini.

I couldn’t have said it better. My billing friends are working to address some of these concerns along with the Vertex team. We are planning to address this issue. Please stay tuned, we will come back to this thread to announce when we can In fact, if you can DM me (@chrischo_pm on X) with, I would love to learn more if you are interested.

simonw, good points. The Vertex vs. non-Vertex Gemini API (via AI Studio at aistudio.google.com) could use more clarity.

For folks just wanting to get started quickly with Gemini models without the broader platform capabilities of Google Cloud, AI Studio and its associated APIs are recommended as you noted.

However, if you anticipate your use case to grow and scale 10-1000x in production, Vertex would be a worthwhile investment.

Hey there, I’m Chris Cho (x: chrischo_pm, Vertex PM focusing on DevEx) and Ivan Nardini (x: ivnardini, DevRel). We heard you and let us answer your questions directly as possible.

First of all, thank you for your sentiment for our latest 2.5 Gemini model. We are so glad that you find the models useful! We really appreciate this thread and everyone for the feedback on Gemini/Vertex

We read through all your comments. And YES, – clearly, we've got some friction in the DevEx. This stuff is super valuable, helps me to prioritize. Our goal is to listen, gather your insights, offer clarity, and point to potential solutions or workarounds.

I’m going to respond to some of the comments given here directly on the thread