Google Cloud Is Now an All-You-Can-Eat AI Buffet. Should You Bite?
Google Cloud now offers OpenAI and Anthropic models alongside its own, creating a unified enterprise platform that changes how I think about building with AI.
The short version
Google Cloud is now the only major cloud provider to offer top models from Google, Anthropic, and OpenAI in one place. This move simplifies things for developers like me who want to test different models without juggling multiple APIs and billing accounts. It feels less like a competition and more like a supermarket, and I think that’s a very smart move.
What did Google Cloud actually announce?
Google Cloud dropped a pretty significant piece of news. They are adding OpenAI’s models, including the popular GPT series, to their Vertex AI Model Garden. This is not a small update. It means that on a single platform, you can now access Google’s own powerful Gemini models, Anthropic’s highly-regarded Claude family, and now, models from OpenAI.
The platform was already home to over 200 models, but adding OpenAI to the mix is a major statement. The official announcement from Google Cloud framed this as giving customers flexibility and choice. The integration is happening through Vertex AI, Google’s managed machine learning platform. This means you can call any of these models using a unified API, manage them with Google Cloud’s security and governance tools, and theoretically, build applications that use the best model for a specific task, regardless of who made it. It’s all happening under one roof. Your Google Cloud roof.
Why is this a big deal for developers?
This is a huge deal for me. And for anyone else who actually builds things with these AI models. For the past two years, my workflow for any new AI-powered feature has been a fragmented mess. It involved signing up for multiple services, managing a spreadsheet of API keys with varying levels of security, and trying to compare apples, oranges, and bananas when it came to performance and cost. It was a pain.
Imagine you’re building a new customer support chatbot. You might want to use Claude 3 Opus for its nuanced, long-form conversational ability. But for summarizing the support ticket into a structured JSON object for your database, maybe Gemini 1.5 Pro’s native function calling is faster and more reliable. And perhaps for translating a user query from Spanish, a specific fine-tuned GPT model is the best in class. Before this announcement, building that one feature required three separate integrations. Three API clients. Three billing accounts. Three sets of security reviews. It was a nightmare to manage and an even bigger nightmare to explain to the finance department. A very real headache.
Google Cloud’s decision to bring these rivals together on Vertex AI solves this problem. The promise is a single project, a single API endpoint (with different model specifiers, of course), a single set of IAM permissions, and one consolidated bill. That’s not just a convenience. That is a fundamental shift in the developer experience. It transforms the task from complex systems integration into a simple model selection problem. It lets us focus on what we’re trying to build, not on the plumbing required to get the models to talk to each other.
Is Google admitting its models aren’t enough?
I’ve seen some hot takes online suggesting this is Google waving a white flag in the AI model race. I don’t see it that way at all. Not for a second. This move is not about the quality of Google’s Gemini models. This is about the strategy of the Google Cloud business.
Google Cloud’s primary objective isn’t necessarily to have the number one LLM. Its objective is to be the number one cloud platform where AI development happens. They sell compute, storage, networking, and high-level services. By hosting OpenAI and Anthropic models, Google is making an incredibly pragmatic and strategic choice. They are saying to the thousands of companies already building on OpenAI: “Don’t rip out your code. Just move your workload here. We’ll make it easier for you.”
Think of it like a grocery store. The store wants to sell its own store-brand products, sure. But it would be a terrible business if it didn’t also stock Coca-Cola, Heinz, and Tide. Google is realizing it’s in the cloud supermarket business, not just the Gemini-brand soda business. If a customer runs a GPT-4o workload on Google Cloud, Google still wins. They sell the compute via a virtual machine. They sell the data storage in Cloud Storage. They sell the networking traffic. They probably take a small percentage of the API call fee for the convenience. The model is the reason you’re in the store, but Google sells you the entire shopping cart.
How does this actually change my workflow?
Let’s get specific. My Tuesday morning inbox triage is a good example. I use a script to pull important emails, summarize them, and draft replies. I’ve always wanted to experiment with different models to see which one gets my tone right, but the friction was too high.
Before this change, I’d need to set up an AWS account for Bedrock to get easy access to Claude, keep my OpenAI account for GPT-4, and use a Google Cloud project for Gemini. That’s three different environments. Three different SDKs to install and learn. Three places to monitor for billing overages.
Now, I can imagine a much simpler process. I can create a single Google Cloud Function. Inside that function, I can write code that calls the Vertex AI API. I can have a simple configuration flag that lets me switch between gemini-1.5-pro, claude-3-sonnet, and gpt-4o with a single line change. I can run an A/B test. I can route different emails to different models based on their content. And all my data, the email content itself, never has to leave the Google Cloud ecosystem. This is a massive improvement for data security and governance.
For a larger team, the benefits multiply. You can use Google Cloud’s IAM (Identity and Access Management) to control exactly who has permission to use which models and how much they can spend. You can use the built-in logging and monitoring to track performance. This is the kind of boring, enterprise-grade stuff that doesn’t make for a flashy demo but is absolutely critical for running AI in a real business. Google is turning the wild west of LLM APIs into a well-managed, governable corporate resource.
What are the potential downsides?
This isn’t a complete paradise, of course. There are always tradeoffs. The most obvious one is cost. Convenience is never free. Google will be taking a cut for providing this unified platform, and you can expect that using OpenAI or Anthropic models via Vertex AI will be more expensive than going to them directly. For a small-scale project, that markup might not be worth it. For an enterprise that values the security and operational consolidation, it almost certainly will be.
Another potential issue is the abstraction layer. When I make an API call to GPT-4o through Vertex AI, what happens when I get a weird error? Is it a Google Cloud problem or an OpenAI problem? Who do I file a support ticket with? This “single pane of glass” can sometimes become a “single point of blame” where vendors point fingers at each other. Resolving these cross-platform bugs could become a new kind of frustration for developers.
Finally, there’s the lock-in question, but it’s a new flavor of it. You are no longer locked into a single model, which is great. You are, however, becoming more deeply locked into the Google Cloud platform. By routing everything through Vertex AI, you’re building your applications on top of a Google-specific service. Moving to Azure or AWS later would mean rewriting all of that integration logic. Google is trading you model freedom for platform loyalty. For many, that will be a trade worth making. For now.
What should you do about it?
If you’re an individual developer or a small startup, this is a great time to experiment. The barrier to comparing top-tier models just got much lower. If you already have a Google Cloud account, spin up a Vertex AI project and see how the unified API feels. See if the convenience is worth the potential extra cost for your use case.
If you’re part of a larger organization that is already on Google Cloud, this is a moment to talk to your cloud architecture team. The ability to bring OpenAI and Anthropic workloads under the corporate security and billing umbrella is a major selling point. This could simplify your AI strategy immensely, reducing risk and operational overhead. It could be the catalyst for projects that were previously blocked by security concerns about using multiple external AI services.
And if you’re on another cloud like AWS or Azure? Well, the calculus just got more interesting. Google Cloud has made a compelling case for itself as the most versatile and neutral ground for AI development. It might be time to re-evaluate your multi-cloud strategy or at least start a small pilot project on GCP to see what you’re missing.
FAQ
Do I need a Google Cloud account to use OpenAI models now? No, you can still go directly to OpenAI to use their models. This announcement means you now have the option to access them through the Google Cloud platform, which integrates them with Google’s other cloud services.
Is it cheaper to use OpenAI models on Google Cloud? It is unlikely to be cheaper. You’re paying a premium for the convenience, security, and integration of the Vertex AI platform. The final pricing will determine the exact markup, but direct access from the model provider is typically the cheapest route.
Which specific OpenAI models are available? Google stated they are starting with select models, including GPT-4o, and will expand the offerings over time. The integration is currently in private preview, so availability will be limited at first.
Does this mean I can move my data easily between models? Yes, this is one of the main benefits. Because all models are accessed from within the Google Cloud environment, your data can stay in one place (e.g., in a Google Cloud Storage bucket) and you can point different models at it without incurring data transfer costs or security risks associated with moving data between clouds.