Pecan vs. Google Vertex AI | Pecan AI
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Google Vertex AI Alternative

Why Choose Pecan Over Google Vertex AI?

Sure, you’ve heard of this “Google,” and it might be tempting to dive right into their AI tools. And maybe Vertex AI would be a good match for you.

But Pecan is easy to use, fast, scalable, and flexible — without the hassles of trying to use Vertex.

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Reason #1

Use Pecan without being a data science expert

Data analysts can use SQL to create machine-learning models

Connect Pecan to your data tools and processes easily — no data engineering needed

Reason #2

Pecan’s smart automation speeds up machine learning success

Cleans up messy data automatically so it's ready for ML; Vertex requires manual preparation

Creates robust models using cutting-edge automated feature engineering and AutoML

Reason #3

Don’t get lost in confusing documentation — get constant support

Pecan provides you an AI Assistant, generates SQL for you, and offers smart tips throughout

Pecan's AI specialists will help you define a strategy and achieve success

Reason #4

Use the tools you prefer and know exactly what you'll pay

Keep your data where you like it — Pecan connects easily to many services

Get predictable bills with Pecan’s transparent, upfront pricing

Common Challenges with Google Vertex AI

  • “The biggest downside in my opinion is its pricing structure. Initially, when you start it seems OK, and one can quickly launch new models using it. But as you move forward, you find the pricing structure a bit too steep and complex to understand.”
  • “A big concern to me is vendor lock-in … Once you start using Vertex AI, you try to use GCP services only as it's an effort to use third-party tools and services. … You get stuck in the GCP ecosystem and it's really difficult to switch.”
  • “[It can be] overwhelming for beginners and those with limited experience in machine learning. A more user-friendly interface and improved documentation could enhance accessibility.”
  • “The tool isn't as intuitive as we had hoped and had come to expect with Google.”
  • “So many answers to questions I faced are not found in articles and [even fewer] videos on YouTube.”
  • “Using Vertex AI can be expensive … The costs can add up quickly when utilizing multiple services or running large-scale machine learning operations.”
  • “One potential drawback of Vertex AI is its limited availability compared to other cloud providers. Vertex AI was only available within the Google Cloud ecosystem.”
“The biggest downside in my opinion is its pricing structure. Initially, when you start it seems OK, and one can quickly launch new models using it. But as you move forward, you find the pricing structure a bit too steep and complex to understand.”
“A big concern to me is vendor lock-in … Once you start using Vertex AI, you try to use GCP services only as it's an effort to use third-party tools and services. … You get stuck in the GCP ecosystem and it's really difficult to switch.”
“[It can be] overwhelming for beginners and those with limited experience in machine learning. A more user-friendly interface and improved documentation could enhance accessibility.”
“The tool isn't as intuitive as we had hoped and had come to expect with Google.”
“So many answers to questions I faced are not found in articles and [even fewer] videos on YouTube.”
“Using Vertex AI can be expensive … The costs can add up quickly when utilizing multiple services or running large-scale machine learning operations.”
“One potential drawback of Vertex AI is its limited availability compared to other cloud providers. Vertex AI was only available within the Google Cloud ecosystem.”

Accelerate your team’s journey to AI success.

Most data analysts using Pecan successfully build their first machine learning model in under a day.  Ready to bring powerful machine learning and its business benefits to your organization?