Gemini Enterprise Agent Platform Reviews

We handle several cases for our customers, especially for contract management. We manage contract management use cases along with cases for finance and for Benthic company.

We meet with customers and understand their use cases. We have very good relationships with government and enterprise customers in South Africa, especially Middle East. We are creating and developing use cases for AI. As mentioned, we handle cases related to contract management, education, and fraud and money laundering for fintech companies. We also handle use cases related to customer support, CRM, and fintech companies. Most cases are related to contract management, fintech, customer service, customer satisfaction, fraud, money laundering, content and document management, and technical support, integrated with CRM and customer support systems.

Google Vertex AI is customizable, and we can use it for many use cases, especially for tickets to tickets and someone to tickets. It is valuable because we do not need to have infrastructure.

Integration with the customer's CRM, customer service, and ERP systems is important. Integration with Zoho is necessary because one of our customers uses Zoho. Some customers use Oracle database, so we integrate with Oracle database. Some customers use Oracle OCI, so we integrate with Oracle OCI as well.

Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interface needs to be more user-friendly. The Google team and development teams enhance the product every day, and there is a roadmap for enhancement.

We have been using the solution for two years.

The support is perfect and fantastic. The Google support team has been very helpful many times. The response is very good, and the technical support is very professional with excellent knowledge. The stability is better than other platforms.

Positive

We use Google pricing calculator for pricing considerations.

We are familiar with most concepts such as language and machine learning.

We use another platform called Quantum AI, which is an American company. We have a platform for AI integrated with services related to Azure AI. We also use a platform called One AI, which is a very good solution, especially because it is an on-premises solution. We use this solution for a government customer who needs an AI platform to manage traffic and people inside Haram or inside large mosques in crowded areas. We delivered the project based on the Quantum AI platform at the customer's data center.

There is a big difference between these solutions. Google Vertex AI is ready for implementation, and you do not need to prepare infrastructure, storage, or integration - it is very simple. With Quantum AI, you need to prepare the infrastructure, create virtual machines, grid hypervisor, and operating system. Integration is not straightforward and requires connectors and licenses. Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms.

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.

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, Google Vertex AI platform, we can achieve everything. We need not switch over to multiple tools, multiple platforms. Instead, everything can be accomplished through this one single platform for integration with existing workflows, integration with existing systems and tools and databases. Everything is being accomplished through Google Vertex AI platform.

We have gone through several pre-trained AI models and AI agents. Our product requirements and project requirements mandate us to develop agents and models from the ground up, from scratch.

It is an end-to-end comprehensive platform, Google Vertex AI. That means you can develop your AI agents and deploy your AI agents. You can monitor, manage, scale up your agents' infrastructure. The security offering from Google Vertex AI platform gives extra security for all the agents and data.

It is very simple to use.

Google Vertex AI platform stands at number one with its simplicity, functionality, and continued update of platform features.

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 other optimizations to enhance the performance of the AI models. We are not fully leveraging the automation being provided by Google Vertex AI for building AI models.

Google Vertex AI has to be enhanced to support agentic AI system development. AI agents have to be developed through Google Vertex AI. Additionally, RAG support, vector database support, knowledge graph support, integration with enterprise systems such as ERP, CRM, PLM, MES, and other enterprise systems need improvement. Automated workflow generation and automation could also be enhanced.

At this point, we are completely satisfied with the features and functionalities of Google Vertex AI platform, but as we move towards autonomous systems through agentic AI paradigm, Google Vertex AI platform needs to be improved to facilitate agentic AI system design, development, deployment, monitoring, observability, governance, and security.

For the last one year.

AWS Bedrock and SageMaker.

We are using Google along with AWS and Azure.

For one project, we are using Cloud SQL, but we are primarily moving towards vector databases and knowledge graphs. We are working with Azure databases, specifically Azure SQL Database, which is a managed service database service. We are also using Azure Cosmos DB.

We are primarily using Google Vertex AI platform. We are exploring Google Vertex Pipeline for creating data pipeline and model control flow pipeline. For MLOps, we are using MLflow, an open-source deployment product, while also evaluating Google Vertex AI platform's MLOps module.

We started with Google Cloud deployment and now we are moving towards a hybrid version, implementing both on-premise and Google Cloud solutions. We have directly partnered with Google Cloud.

I rate Google Vertex AI a nine out of ten. The platform is very good, and the price is reasonable at around two or three out of ten.

I personally use Google Vertex AI mostly for analysis in the product, particularly because performing manual tasks was very time-consuming, finding compliance controls. With the help of Google Vertex AI and Gemini, we leverage that feature and ask AI to do the analysis. We use it for some of the internal workflows and we also have some user-facing products in B2B where we have integrated chat LLM in the dashboard.

The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system.

We utilize pre-trained APIs in Google Vertex AI, specifically using Gemini, which is a Cloud API from Vertex Model Garden.

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.

It would be beneficial if Google Vertex AI included additional functions such as prompt-based SDKs to easily create workflows.

I have been working with Google Vertex AI for the past one year now.

I would rate the stability of Google Vertex AI as eight or nine.

For scalability, which refers to the ability to expand, I rate it as eight, considering everything depends on cost.

Google AI's team comes on call whenever we need MLOps support.

Positive

Currently, we are mostly on GCP now, so I am not with Amazon and have stopped working with Route 53.

The initial setup for Google Vertex AI is simple; you just use a service account and you're good to go.

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.

My main competitor comparison for Google Vertex AI would be Bedrock.

In terms of cost, Bedrock is a bit expensive, but feature-wise, they are more or less the same.

We are working with GCP and use Google Vertex AI, which is a solution I find quite useful.

We have checked Vertex Pipelines but have not used it for data integration. There may be some features, but since we cannot immediately migrate to that pipeline given our current data systems, we have not deployed our resources to explore it much.

For monitoring in Google Vertex AI, they provide some tokens and credits, such as 50% off, which adds to the feasibility from a cost perspective.

I am working intensively with the AutoML feature to reduce some manual work.

I am a partner with Google.

I would give Google Vertex AI a final mark of around eight, but I believe they can improve assistance with integrating Google Vertex AI into existing solutions and pipelines to help businesses achieve a more agentic approach.

Without the option for improvements I would like to see, my mark for the product is a five.

My overall rating for Google Vertex AI is 8 out of 10.

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.

The features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good. One is the large language model and the other is the vision model. It has helped enhance our applications significantly.

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 make Google Vertex AI closer to a ten, I think the platform should be simpler and the time taken to set up should be shorter.

One technical point that I think Google Vertex AI lacks is a multimodal model, a mechanism for using image and text simultaneously, which OpenAI or Azure has.

I have been dealing with Google Vertex AI for more than a year, approximately a year and a half.

It is very difficult to set performance metrics, but I do measure performance based on responses in Google Vertex AI. The performance metrics I find most informative are based on measuring the output or looking at the response to determine how well it has performed a particular task.

The services are cheaper compared to Claude or OpenAI, and I have also been using Microsoft Azure AI Foundry services for about two years.

Google Vertex AI MLOps support is not utilized because I typically develop machine learning models internally using open-source tools and platforms.

Model development time is short with Google Vertex AI, as I use only their API services, and I consider it the number one among the frontier models.

The best models of Google Vertex AI have been able to perform well, although the smaller models fall short. I have used Gemini and the document intelligence services for parsing document contents.

My research was around how well they support RAG use cases. Do they have all the components required for building the RAG use case? As in, you need a data storage part, vector storage where you can store your vector embeddings, and the model layer itself.

Vertex comes with inbuilt integration with GCP for data storage. It also supports ChromaDB, which is not provided by Azure.

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.

I have been using it for quite some time, approximately two months.

I found Vertex to be quite more stable than Azure.

It is quite scalable. It can easily scale up or down based on user demands.

I haven't personally tried customer service, however, based on reviews, they have good customer support that is available during downtime.

Positive

Before these two, we used H2O AI. It lacks most of the features, and H2O AI pieces are scattered rather than having a unified platform.

I find it quite user-friendly. A beginner can also find it easy to understand. The learning curve is exponential.

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

I would definitely recommend Vertex AI to someone. I compared both platforms over a wide range of factors, and in most areas, Vertex AI is ahead of Azure.

I'd rate the solution nine out of ten.

Mostly AutoML related use cases and now more and more agentic AI use cases.

The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints.

The lineage part is super helpful because it can be integrated with different tools. For example, from front-end facing applications, we can use Python Django framework, call the entire pipeline, get the output, and feed it to the front-end, which is very helpful.

The speed and performance are improving significantly because Gemini is advancing considerably. From that perspective, users can get the latest features available from the Gemini LLM itself, along with the capability to integrate it with back-end systems.

In one experimental case we were working on, we could connect it through Django, the back-end Python based framework. This facilitated and triggered the entire pipeline, which proved very helpful.

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. Sometimes if it is not properly managed, it can result in higher cloud costs. While the outcome is good, reducing the time for AutoML jobs to finish would be beneficial.

Compared to some other tools, improvements would be required, especially regarding the user interface from a DataIQ perspective. The functionality is present, but the user interface could be improved.

Almost four years.

It was straightforward.

The speech-to-text model and real-time speech models are being introduced, which is a good addition. The level that Google Vertex AI provides is not available anywhere else, particularly in AWS or Azure. This gives GCP an edge from an end-to-end capability perspective.

I have a technical background, and with the support from Gemini, users can easily ask all questions and get answers. It is fairly easy to use from that perspective. On a scale of 1-10, I rate Google Vertex AI a 9.

We are using Google Vertex AI for various purposes, especially various chatbots for customer engineers and customers. Additionally, we are working on projects involving AI and machine learning.

Features like the LLM model are valuable, and we are using Google Vertex AI for these purposes. Additionally, the integration of AutoML features streamlines our machine-learning workflows.

Both major systems, Azure and Google, are not yet stabilized, especially their customer support. It is essential to have a robust customer support system to get quick resolutions as they're frequently making changes. Working on multiple indexes is also a challenge with both systems.

I've used the solution for one year.

The stability of Google Vertex AI is rated an eight. As mentioned, the product is still growing, and there are some complexities.

Scalability is rated a seven due to some technical restrictions, such as the inability to work on multiple indexes.

Customer support should be improved to get quick and accurate answers.

Positive

We moved from Oracle to using artificial intelligence and machine learning solutions from Azure and Google Cloud Platform.

The initial setup was simple, with no apparent issues.

Google Vertex AI is relatively expensive, rating it six for pricing. The pricing model could be more flexible.

Vertex AI is a playground for data analysts. It is for machine learning engineers and data scientists. We create, test, customize, deploy, and monitor our models on Vertex AI. It is a fully managed product of machine learning.

About twenty people at our company use Vertex AI.

The most valuable feature of Vertex AI is the Feature Store. When it comes to model training and artificial intelligence, a whole team of people was needed to do the work. It could not be done by just one person. Data engineers, machine learning engineers, and data scientists have completely different tasks and, therefore, the communication gap is inevitable. With Feature Store, you can create your features and make them centralized in the store so everyone can see them. The feature is locked and you can create multiple versions of the features. It is similar to GitLab. It has a version control function which is very practical and important for anyone who works in machine learning.

I also like the monitoring feature. I give that feature a ten out of ten. It saves data scientists' lives. After deploying the model and that model runs on the cloud, you need to constantly check on it. With the monitoring feature, you can relax and wait for the notification in case something is wrong. You have time to examine what went wrong. In addition, you can define the error and pre-set the exact thing you want to monitor. Whether it's a task failure, an instant failure, too much uptime, or downtime, the monitoring feature is extremely helpful.

I've been using Google Vertex AI for four years.

The solution is stable.

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 does not require any maintenance at all.

I have not had any need to contact technical support. 

I have previously used Microsoft Azure but I prefer Vertex AI because it's less complicated to use.

The deployment is very simple. All you have to do is click a button, finish some data configuration, and let the machine do the work while you wait.
There are several options to choose from. You can deploy it on Google Cloud, Microsoft Cloud, AWS Cloud, or you can deploy it on your private cloud. There are two options when it comes to retrieving the data. You can either use HTTPS or API. The deployment is very customized.
If you want to deploy on a cloud other than Google Cloud, you need a cloud engineer with you because sometimes it works on Google Cloud, but it has different terms or different definitions on AWS or Microsoft. You need someone who has the knowledge of how these work and can guarantee everything will work smoothly.

I wouldn't say that the whole package is expensive, but I definitely wish it was a little cheaper.

It will improve your engineering abilities.

Considering that Vertex AI is adding new features such as Generative AI, I would rate it a nine out of ten. Google continuously improves the solution. Over the past three years, many new and useful features have been added. I have never had any need to contact technical support. 

I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information.

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. 

Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding. The process of deployment is facilitated as well. It's possible to build LLM models through an API and deploy it through an app.

It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions. They could offer some simplified system to train the samples other than LLM models.

I have been using Vertex AI for the last five months.  

We have experienced no stability concerns, and we are entirely satisfied with the current state.

Vertex AI provides both scalability and flexibility, while allowing all of the needed adaptation of machine learning projects.

In my experience, there have been some delays related to customer service. They offer a good solution eventually, but it takes a long time to receive the assistance after you request it. Other platforms, such as Azure OpenAI have a live chat option which makes it easier to communicate any possible issues. I would rate their customer service a seven out of ten. 

Neutral

I previously used Azure OpenAI and AWS AI, but I was not satisfied with the setup process. Azure OpenAI requests that you submit exclusively credit cards, while Vertex AI accepts debit cards as well. AWS AI was a good solution, but not not the best. I used it briefly before switching to Vertex AI. 

The initial configuration was easily executed. Vertex AI is user-friendly, all you need is to have a Google account and Cloud to set up everything. With all of the new technology and developments, many other AI platforms require new knowledge to go through the setup process, but Vertex AI is very convenient in that matter. 

The price structure is very clear. You are aware of what you are paying for and what services you are getting. It is quite confidential, which is another benefit. Google Vertex AI offers a range of pre-trained models that helps accelerate the development of your machine-learning applications, so if you are skilled, you can build your own model in a very short period of time.

Before opting for Vertex AI, I would recommend a short course on this solution, to know how to use it to its full potential. It doesn't have to be an extensive course, maybe an hour or two. Google provides very good but simple, beginner-friendly programs, that ensure you get all of the necessary information.

We use it to run AI chatbots, generative AI, and some data science and machine learning instances.

One of the use cases is that we built a chatbot to improve technician efficiency by providing all the user manuals for a specific line of work. The technicians could then interact with the chatbot and get generative AI responses on fixing errors that pop up.

It should improve the model efficiency, which is just training the model over and over again. Building an application on Agent Builder and grounding it with our internal documents and internal data does improve its accuracy when used in generative AI. 

Some of the tools should have more advanced settings available. Some are very locked into certain features and settings, and there is no customization. A more advanced button to fine-tune some of those settings would be good.

Having it more available throughout all the other applications on GCP would be good. 

I have been using Google Vertex AI for a year.

I rate the solution’s stability a nine out of ten. 

It is very good at scaling from small to large datasets.

The development team includes five to ten people using this solution.

Support is good. I have a direct connection with some Google folks. I get support there, but it's pretty good and straightforward.

Positive

The initial setup took a couple of days.

I rate the initial setup an eight out of ten, where one is difficult, and ten is easy.

It costs 1000/year, so it's very inexpensive.

I rate the product’s pricing a five out of ten, where one is cheap, and ten is expensive.

Vertex is a platform for models and AI tools, but in this realm of data management, running all my queries and everything inside of instances with those GPUs available helps the flow of data.

I advise you to start with the partner training. It would help to always start with the training that Google provides. It provides some good labs. It's very intuitive, so you should have an easy time transitioning into it.

Overall, I rate the solution an eight out of ten.