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Cohere vs Gemini Enterprise Agent Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 23, 2026

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
8th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
AI Writing Tools (3rd), Large Language Models (LLMs) (3rd), AI Proofreading Tools (4th)
Gemini Enterprise Agent Pla...
Ranking in AI Development Platforms
1st
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI Agent Builders (5th)
 

Mindshare comparison

As of June 2026, in the AI Development Platforms category, the mindshare of Cohere is 2.0%, up from 0.7% compared to the previous year. The mindshare of Gemini Enterprise Agent Platform is 8.0%, down from 12.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Gemini Enterprise Agent Platform8.0%
Cohere2.0%
Other90.0%
AI Development Platforms
 

Featured Reviews

amankrs21 - PeerSpot reviewer
Generative AI Engineer at a tech consulting company with 10,001+ employees
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 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."
"Speed has helped me in my day-to-day work, and I really notice the difference because it responds very quickly to LLM requests."
"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."
"Cohere has helped my organization innovate and stay ahead in our industry as Cohere was better than Titan, and it helped us to secure the client's confidence and we moved from proof of concept to production."
"The very first thing that I really like about it is the support team, because they're really available on Discord and they answer all of your questions."
"I assess the value of Cohere's API support in my business operations as easy to integrate."
"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."
"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."
"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."
"The integration of AutoML features streamlines our machine-learning workflows."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The support is perfect and fantastic."
"Vertex comes with inbuilt integration with GCP for data storage."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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."
 

Cons

"When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved."
"Cohere can be improved by having more integrations beyond its current offerings with Amazon."
"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."
"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."
"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."
"I have not observed any measurable benefits or return on investment with Cohere."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"I'm not sure if I have suggestions for improvement."
"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."
"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."
"Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful."
"The tool's documentation is not good. It is hard."
"I think the technical documentation is not readily available in the tool."
 

Pricing and Cost Advice

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

By visitors reading reviews
Financial Services Firm
13%
Manufacturing Company
9%
Comms Service Provider
8%
Construction Company
7%
Manufacturing Company
10%
Financial Services Firm
10%
Computer Software Company
8%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise1
Large Enterprise8
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Cohere?
My experience with pricing, setup cost, and licensing was that it was all managed by AWS, and we had AWS credits, so I did not have to dive into that.
What needs improvement with Cohere?
Cohere can be improved by having more integrations beyond its current offerings with Amazon. Integrations with Databricks, Azure, and Google Cloud would be beneficial.
What is your primary use case for Cohere?
My main use case for Cohere is that it's a good embedding model. I have used it with Titan, but Cohere came out better. A specific example of how I've used Cohere for embeddings is when I was worki...
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?
Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful. To ...
What is your primary use case for Google Vertex AI?
Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.
 

Also Known As

No data available
Vertex, Google Vertex AI
 

Overview

Find out what your peers are saying about Cohere vs. Gemini Enterprise Agent Platform and other solutions. Updated: April 2026.
899,258 professionals have used our research since 2012.