Future AGI is an AI lifecycle platform designed to support enterprises throughout their AI journey. It combines rapid prototyping, rigorous evaluation, continuous observability, and deployment to help build, monitor, optimize, and secure generative AI applications.
N/A
Gemini Enterprise Agent Platform
Score 8.6 out of 10
N/A
The Gemini Enterprise Agent Platform is a fully-managed, unified environment designed for the development, orchestration, and governance of Autonomous AI Agents. The platform consolidates AI Studio, Agent Builder, and a diverse Model Garden to support the creation of complex, multi-agent systems grounded in enterprise data and business logic.
$0
Starting at
Pricing
Future AGI
Gemini Enterprise Agent Platform
Editions & Modules
No answers on this topic
Imagen model for image generation
$0.0001
Starting at
Text, chat, and code generation
$0.0001
per 1,000 characters
Text data upload, training, deployment, prediction
$0.05
per hour
Video data training and prediction
$0.462
per node hour
Image data training, deployment, and prediction
$1.375
per node hour
Offerings
Pricing Offerings
Future AGI
Gemini Enterprise Agent Platform
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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Pricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
More Pricing Information
Community Pulse
Future AGI
Gemini Enterprise Agent Platform
Considered Both Products
Future AGI
No answer on this topic
Gemini Enterprise Agent Platform
Verified User
Anonymous
Chose Gemini Enterprise Agent Platform
we evaluating Vertex AI. we also considered several ML platforms that provide same capabilities for building and training, and deploying ML models. The main alternatives we evaluated were anti gravity Azure Machine Learning, and to a lesser extent open-source ML-Ops such as …
Out the gate, Vertex just seemed to be more accurate on command with our prompts. We spent less time versus other platforms getting exactly what we wanted. Google's UI is way more robust, too, with how you can configure the exact settings you want when doing image generation. …
We tend to adapt and use the platform that suits the customers needs the best. We return to Vertex AI because it is the most in-depth option out there so we can configure it any which way they want. However, it is not quick to market and constantly changing or updating it's …
I have used OpenAI for their LLM and Vector Embedding service, they are really good at it. But Vertex AI has other better services like training pipeline , depolyment creation etc.
I have used AWS sagemaker is the past for AI/ML model development in my previous organization for everything. Sagemaker is good with respect to certain services but when we talk about Vertex AI in comparison, AutoML is the differentiator. AutoML is very strong and is able to …
Let's say that Azure OpenAI Service offers you exactly what you look for in simple-to-understand terms: your own private instance of OpenAI API backend.
Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all …
In my regular activity, Vertex AI is missing some of the True Positive Alerts due to the ML training and needs to train more data sets, after it has reduced the false positives. To find the Zero day Vulnerability it has low accuracy and sometimes it misses the true positives. Once we have trained with the large data set, it came up with good results.
Vertex AI comes with support for LOTs of LLMs out of the box
MLOps tools are available that help to standardize operational aspects
Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers
It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.
we evaluating Vertex AI. we also considered several ML platforms that provide same capabilities for building and training, and deploying ML models. The main alternatives we evaluated were anti gravity Azure Machine Learning, and to a lesser extent open-source ML-Ops such as Kubeflow, very flexible and highly combustible, full customisation on cloud. we used chatgpt and claud AI ML, model also we observed many changes Vertex AI will be differ from this. we used all the products but Vertex AI will be differ on the ML model training and deployment.