Google Vertex AI vs Replicate comparison

Google and Replicate are both solutions in the AI Development Platforms category. Google is ranked #2 with an average rating of 8.3, while Replicate is ranked #10 with an average rating of 8.0. Google holds a 18.8% mindshare in AIDP, compared to Replicate’s 10.1% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of Replicate users who would recommend it.

Google Vertex AI

Read 10 Google Vertex AI reviews

8,597 Views
7,806 Comparison Views

100% willing to recommend

Replicate

Read 1 Replicate review

2,136 Views
2,010 Comparison Views

100% willing to recommend

Google Vertex AI

Replicate

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Executive Summary

We performed a comparison between Google Vertex AI and Replicate based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Google, Amazon Web Services (AWS) and others in AI Development Platforms.

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Buyer's Guide

AI Development Platforms

January 2025

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Helped 831,265 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Google Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

Replicate

Ranking in AI Development Platforms

10th

Average Rating

8.0

Reviews Sentiment

5.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 18.8%, up from 17.9% compared to the previous year. The mindshare of Replicate is 10.1%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Serge Dahdouh

CEO at Les Roches International School of Hotel Management

A user-friendly platform that automatizes machine learning techniques with minimal effort

We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.

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reviewer2386686

Junior Software Engineer at a comms service provider with 11-50 employees

Easy to use and good for disaster recovery planning

I use the tool for real-time data synchronization. Replicate is a beneficial tool for disaster recovery planning. The use cases attached to Replicate are very direct. I have used other products in the past, but they are not as efficient as Replicate. I feel Replicate is easier to use than other tools. Replicate has impacted our company's data integration processes by twenty to thirty percent. Overall, the product is easy to use. The product was also easy to configure. I recommend the product to others who plan to use it for real-time data integration. The product has been integrated into our company's existing infrastructure. I haven't done the integrations but I know that it was performed by someone else. I rate the tool an eight and a half out of ten.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."

"Vertex comes with inbuilt integration with GCP for data storage."

"The integration of AutoML features streamlines our machine-learning workflows."

"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

More Google Vertex AI pros

"Replicate is a beneficial tool for disaster recovery planning."

Cons

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"I'm not sure if I have suggestions for improvement."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"The tool's documentation is not good. It is hard."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

More Google Vertex AI cons

"I feel that the marketing activities of the product are an area of concern...Replicate is a very beneficial tool that should be marketed well enough in a good way."

Pricing and Cost Advice

"The price structure is very clear"

"The solution's pricing is moderate."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

13%

Manufacturing Company

Retailer

Computer Software Company

15%

University

11%

Educational Organization

11%

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Google Vertex AI?

We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...

See all answers

What is your experience regarding pricing and costs for Google Vertex AI?

They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.

See all answers

What needs improvement with Google Vertex AI?

I'm not sure if I have suggestions for improvement. Based on my comparison between the two, Vertex has various additional functionalities that Azure doesn't provide.

See all answers

What do you like most about Replicate?

Replicate is a beneficial tool for disaster recovery planning.

See all answers

What needs improvement with Replicate?

I feel that the marketing activities of the product are an area of concern that needs to be taken care of from an improvement perspective. Replication was a tool that my company had never heard of,...

See all answers

What is your primary use case for Replicate?

Basically, I came across Replicate while searching for an open-source LLM model. Regarding my use case, from a prompt I want to generate an output response token of 25k tokens driver, but currently...

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Comparisons

Azure OpenAI vs Google Vertex AI

Compared 40% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 20% of the time

Hugging Face vs Google Vertex AI

Compared 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 8% of the time

Lambda Stack vs Google Vertex AI

Compared 2% of the time

More Google Vertex AI Competitors

Hugging Face vs Replicate

Compared 61% of the time

Together Inference vs Replicate

Compared 21% of the time

Amazon SageMaker vs Replicate

Compared 5% of the time

Fireworks AI vs Replicate

Compared 4% of the time

Azure OpenAI vs Replicate

Compared 3% of the time

More Replicate Competitors

Product Reports

Buyer's Guide

Google Vertex AI

January 2025

Download Google Vertex AI product report

Buyer's Guide

AI Development Platforms

January 2025

Download Replicate product report

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Google

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Replicate

Overview

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Replicate is a cutting-edge solution designed to streamline AI model deployment, making it easier for developers to implement advanced machine learning techniques efficiently.

Replicate simplifies the process of deploying AI models by eliminating the intricate setup usually required. It provides an intuitive platform that supports developers with powerful APIs, fostering a seamless integration into existing workflows. The ability to track and version models enhances reliability and collaboration, making it a preferred choice for tech-savvy audiences seeking advanced solutions in AI deployment.

What are the key features of Replicate?

Efficient Versioning: Enables developers to manage different versions of AI models easily, ensuring all stakeholders work on the correct iteration.
Robust APIs: Provides straightforward APIs that help in the swift integration of AI capabilities into applications and projects.
Model Tracking: Offers comprehensive tracking features, allowing users to monitor performance and make informed adjustments.
User-friendly Interface: While advanced, Replicate still offers an interface designed for ease of use, supporting efficient model management.

What benefits can Replicate users expect based on reviews?

Increased Efficiency: Users highlight the significant time savings in deploying models compared to traditional methods.
Improved Collaboration: Version control and tracking facilitate teamwork, enhancing productivity and accuracy.
Scalability: Replicate supports growing project demands, allowing seamless scale-up as requirements grow.
Reduced Complexity: Simplifies the deployment process, reducing the learning curve and technical hurdles.

In industries like healthcare and finance, Replicate's efficient deployment and tracking of AI models assist in creating smarter solutions, from predictive analytics to operational automation. By addressing specific industry challenges with tailor-made AI solutions, it supports businesses in meeting innovation goals effectively.

Buyer's Guide

AI Development Platforms

January 2025

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