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Cohere vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive Summary

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

Cohere
Ranking in AI Development Platforms
12th
Average Rating
7.6
Reviews Sentiment
6.7
Number of Reviews
8
Ranking in other categories
AI Writing Tools (3rd), Large Language Models (LLMs) (5th), AI Proofreading Tools (5th)
Google Vertex AI
Ranking in AI Development Platforms
3rd
Average Rating
8.2
Reviews Sentiment
6.4
Number of Reviews
14
Ranking in other categories
AI-Agent Builders (6th)
 

Mindshare comparison

As of January 2026, in the AI Development Platforms category, the mindshare of Cohere is 1.3%, up from 0.3% compared to the previous year. The mindshare of Google Vertex AI is 8.1%, down from 17.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI8.1%
Cohere1.3%
Other90.6%
AI Development Platforms
 

Featured Reviews

AS
Engineer at Roche
Have improved project workflows using faster response times and reduced data embedding costs
One thing that Cohere can improve is related to some distances when I am trying similarity search. Let's suppose I have provided textual data that has been embedded. I have to use some extra process from numpy after embedding the model. In the case of OpenAI embedding models, I do not have to use that extra process, and they provide lower distances compared to my results from Cohere. I was getting distances of approximately 0.005 sometimes, but in the case of Cohere, I was getting distances around 0.5 or sometimes more than that. I think that can be improved. It was possibly because of some configuration or the way I was using it, but I am not exactly sure about that.
Hamada Farag - PeerSpot reviewer
Technology Consultant at Beta Information Technology
Customization and integration empower diverse AI applications
We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Quotes from Members

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

Pros

"The very first thing that I really like about it is the support team. They're really available on Discord, and they answer all of your questions."
"Cohere positively impacted my organization by improving the performance of my RAG system."
"The best feature Cohere offers is the Reranking model."
"Cohere helped us with all three aspects: money is saved, time is saved, and we needed fewer resources to meet our end goals."
"Cohere's Embed English v3.0 is a cloud-hosted model that took less time to embed the textual data and was more than 50 to 60% faster than other models, even somewhat faster than text-embedding-3 from OpenAI, helping to reduce development and embedding times."
"Speed has helped me in my day-to-day work, and I really notice the difference because it responds very quickly to LLM requests."
"When it creates a new test, it creates it almost 70 to 80% correctly without errors; the time savings are significant—what previously took one or two days can now be completed in two to three hours maximum."
"A key advantage of integrating Cohere’s reranking model is that it aligns with client requests to include a reranking module — a widely recognized method for improving RAG quality. Additionally, the API demonstrates strong performance in terms of response speed."
"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."
"Vertex comes with inbuilt integration with GCP for data storage."
"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."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The integration of AutoML features streamlines our machine-learning workflows."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"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."
"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."
 

Cons

"I believe Cohere can be improved technically by providing more feedback, logs, and metrics for embedding requests, as it currently appears to be a black box without any understanding of quality."
"The documentation and support could be improved, as there is limited documentation available on the web."
"It's challenging for us to make a conclusion about quality enhancement by using reranking models, as solid evaluation methodology for reranking is still immature."
"One thing that Cohere can improve is related to some distances when I am trying similarity search."
"When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved."
"Cohere could improve in areas where the command model is not as creative as some larger LLMs available in the market, which is expected but noticeable in open-ended generative tasks."
"Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"The tool's documentation is not good. It is hard."
"I'm not sure if I have suggestions for improvement."
"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."
"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."
"I think the technical documentation is not readily available in the tool."
"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."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
 

Pricing and Cost Advice

Information not available
"The solution's pricing is moderate."
"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."
"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 price structure is very clear"
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Top Industries

By visitors reading reviews
Manufacturing Company
11%
Educational Organization
8%
Financial Services Firm
8%
University
7%
Computer Software Company
13%
Financial Services Firm
9%
Manufacturing Company
9%
Educational Organization
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise1
Large Enterprise6
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise3
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Cohere?
Compared to models available in the market, Cohere's pricing, setup cost, and licensing are better.
What needs improvement with Cohere?
Cohere could improve in areas where the command model is not as creative as some larger LLMs available in the market, which is expected but noticeable in open-ended generative tasks. Reporting and ...
What is your primary use case for Cohere?
We adopted Cohere primarily for their command model to support enterprise-grade text generation and NLP workflows. There was a use case for one of our customers where they required automated text g...
What is your experience regarding pricing and costs for Google Vertex AI?
I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.
What needs improvement with Google Vertex AI?
We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...
What is your primary use case for Google Vertex AI?
We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...
 

Overview

Find out what your peers are saying about Cohere vs. Google Vertex AI and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.