Vertex AI vs. Stack AI

Different tools are created for different audiences. While both Vertex AI and Stack AI offer access to AI technology, they do so with a diverse approach.

Vertex AI is a cloud infrastructure platform that offers AI and machine learning, part of the Google Cloud Platform. It serves IT professionals, data scientists and developers, people comfortable with infrastructure management. For those who lack technical skills or prefer a faster approach, the complex menus and configuration flows will be a time sink, not an empowering tool.

On the other hand, Stack AI is a software platform that combines leading LLMs, data integrations, and a visual building experience to help teams automate their work with AI—no coding skills required. Anyone in your company can learn, create and easily share their projects. You can quickly test workflow optimization ideas and measure how much time each saves. Learn more about which is the best for you.

How does Vertex AI compare to Stack AI?

1. Platform approach

1.1 Vertex AI: AI infrastructure and chat-driven agents

A view of Vertex AI’s dashboard view within Google Cloud Platform.

Vertex AI is part of Google Cloud Platform. It includes Model Garden, a selection of popular LLMs, including those from Google, Anthropic and Meta. You can spin up an API to integrate them into your apps and internal tools. On top of that, it provides AI agents that use a combination of natural language processing (using DialogFlow) and generative AI to answer questions and run actions from a chat interface.

While it’s a flexible tool for developers, it becomes quite rigid and slow if you’re looking to solve productivity problems, improve access to information and make better decisions by processing more data with AI.

1.2 Stack AI: upgrading workflows with AI beyond chatbots, user interfaces included

Stack AI’s dashboard displaying tools built on the platform.

Stack AI is in a better position to solve productivity, data processing and information access problems. First, it takes care of all the AI infrastructure by integrating with all leading LLMs (including OpenAI, Google and Anthropic). Second, it offers a variety of data inputs, integrations and knowledge bases so you can ground responses in your data. Finally, it has customizable user interfaces that you can distribute to your team quickly.

These interfaces include:

• Chat assistant, for a chatbot-like experience
• Form, for creating downloadable reports
• Batch, to run AI-powered processing on lists with a single click
• Slack, WhatsApp and SMS for integrating with these external channels
• Chrome extension to place your tool inside your browser, ready to use as you browse
• API, to use the project programmatically or integrate it with your internal tools

2. User experience

2.1 Vertex AI: designed for developers

Vertex AI’s conversational agents interface, displaying all connections between each part of an address collection chat flow.

Vertex AI is tailored to IT teams, with advanced controls that require networking, programming and coding skills. From opening an account to having everything ready, you have to set up projects and configure billing in complex, jargon-ridden interfaces. This experience is the same when approaching AI and machine learning features.

2.2 Stack AI: designed for teams of any technical skill level

A zoomed-in view of a Stack AI project for scoring leads, showing connected nodes representing input, Google Search, URL scraping and an Anthropic Claude LLM.

Stack AI is much easier to use. Creating an account takes less than 3 minutes. When you finish signing up, you can start building a project right away, either from scratch or by customizing a template. Drop inputs, outputs, LLMs and knowledge bases onto the canvas, and connect them to control the data flow. The experience is similar to Miro, a visual way of building AI-driven automation.

When you finish building the project’s architecture—the brain of the tool—move to the export tab where you can configure how the interface should look like. It’s easy to share with your team: simply copy the link and paste it into an email or send it via an internal communication channel.

3. Retrieval-augmented generation (RAG) capabilities

Retrieval-augmented generation frameworks help AI models respond accurately to user requests, greatly reducing the ocurrence of hallucinations.

You can configure them by uploading your data (either as documents, integrations or data sets) and connecting them to your LLMs. Instead of relying only on training data to reply to your questions, the model will run a data retrieval phase from your sources before computing the output. This is also known as grounding.

3.1 Vertex AI: powerful RAG, high setup and maintenance complexity

Vertex AI includes retrieval-augmented generation (RAG) and Google Search integration for grounding AI responses in relevant data. Google Cloud also includes other products that help store, manipulate and push data into LLMs. But, as we’ve touched before, the setup time, skill requirement and complexity make it harder to successfully implement this functionality—especially as it requires writing code and maintaining infrastructure.

3.2 Stack AI: powerful and flexible RAG, easy setup and maintenance

In Stack AI, you can begin by connecting your data sources in the Connections tab. The integration process is similar to other SaaS apps you use, either by following a connection flow or gathering access keys. You can integrate platforms like Microsoft Sharepoint, Salesforce or Amazon S3, among other cloud storage providers like Google Drive, OneDrive and Dropbox—with many more available.

When connecting platforms to Stack AI, you can log in with the external provider and manage permissions, a simpler integration process overall.

Once connected, you can reuse them across projects and create knowledge bases with a selection of files and folders within each—complete with role-based access control for data security. The process of grounding an AI response with Stack AI is simple:

1. When building a project, drop the knowledge base node related to the data source you want to use—for example, Microsoft Sharepoint.
2. Connect it to the user input and the LLM.
3. Include the variable that links the data source’s data to the user prompt.

With those steps complete, any message the user sends to the LLM will draw data from the knowledge base, grounding the response in the process.

4. AI models

4.1 Vertex AI: Model Garden has variety, but no OpenAI

Vertex AI’s Model Garden, where you can set up instances of popular models and activate APIs to send requests from your systems.

Vertex AI’s Model Garden lets you set up APIs for Google, Anthropic and Meta models, along with many open source projects. Since it’s a Google platform, it’s unlikely that you’ll see an OpenAI model in the lineup. And, if you decide to use Vertex AI for its AI agents feature, you’re locked to using Google models only.

4.2 Stack AI: integrates with all leading AI LLM providers and optimization platforms

Stack AI’s canvas displaying the Anthropic, Google Gemini and OpenAI LLM nodes, with many others available in the left-side menu.

Stack AI integrates with all leading providers in the market, including:

• OpenAI, Anthropic, Google and Meta
• Promising developers such as Mistral and XAI
• Customized and open source projects in Hugging Face and Replicate
• Inference optimization and training platforms such as Together AI, Groq and Cerebras

This means you’ll never be locked out of a state-of-the-art model, having the freedom to choose the best LLM for each task.

5. Pricing

5.1 Vertex AI: fixed and pay-as-you-go

In Vertex AI specifically, using the AI agents API starts at a fixed cost of $12 per month, with 1,000 queries and most of the features included. As for Model Garden, each model has pay-as-you-go pricing, meaning you pay how many times you send a request and get a response. The billing tools are also hard to use, as they’re meant for optimizing costs, offering too many inintuitive controls for people used to a more conventional SaaS approach.

5.2 Stack AI: predictable fixed pricing

Stack AI’s pricing is more predictable, offering a free plan and two paid tiers that unlock more project runs, seats and knowledge bases. While there’s a limitation in runs, there’s no limitation in AI tokens, so you can process as much data as you need—especially in the Enterprise plan, where these costs are included in the final monthly price.

Vertex AI vs Stack AI: which one is the best?

Vertex AI is part of Google Cloud Platform, offering infrastructure to integrate AI with existing apps or with those you code from scratch. It’s highly technical, better suited for IT teams with complex needs. While it offers robust connection features, its power comes at the cost of speed and without premade user interfaces to publish tools right away. Model Garden includes a good selection of AI models, but OpenAI is not within the lineup—and the pricing can be unpredictable with pay-as-you-go costs.

Stack AI is a better match to solve front-line problems where information access, answering questions or coordinating multiple systems with AI is key. The configuration and building processes are simple and intuitive, while being powerful and flexible to adapt to a range of use cases from retrieving information from Salesforce or automating tender document review. It integrates with all leading AI providers, while offering predictable pricing so that more value doesn’t necessarily equal higher costs.

Create a free account and start automating your work today with Stack AI today.