Future AGI vs. Vertex AI

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Future AGI
Score 0.0 out of 10
N/A
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.
$50
per month
Vertex AI
Score 8.6 out of 10
N/A
Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.
$0
Starting at
Pricing
Future AGIVertex AI
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 AGIVertex AI
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsPricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
More Pricing Information
Features
Future AGIVertex AI
AI Development
Comparison of AI Development features of Product A and Product B
Future AGI
-
Ratings
Vertex AI
8.6
2 Ratings
20% above category average
Machine learning frameworks00 Ratings8.62 Ratings
Data management00 Ratings9.12 Ratings
Data monitoring and version control00 Ratings8.22 Ratings
Automated model training00 Ratings9.12 Ratings
Managed scaling00 Ratings7.72 Ratings
Model deployment00 Ratings8.62 Ratings
Security and compliance00 Ratings8.62 Ratings
Best Alternatives
Future AGIVertex AI
Small Businesses
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
Medium-sized Companies
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
Enterprises
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
InterSystems IRIS
InterSystems IRIS
Score 7.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Future AGIVertex AI
Likelihood to Recommend
-
(0 ratings)
7.7
(13 ratings)
Performance
-
(0 ratings)
7.0
(10 ratings)
Configurability
-
(0 ratings)
7.2
(10 ratings)
User Testimonials
Future AGIVertex AI
Likelihood to Recommend
Future AGI
No answers on this topic
Google
we used Vertex AI on our automation process the model very useful and working as expected we have implemented in our monitoring phase this very helpful our analysis part. real time response is very effective and actively provide detailed overview about our products.this phase is well suited in our org. this model could not applicable for small level projects why because this model not needed for small level projects and without related resource of ML this model not useful. strictly on non cloud org not suitable means on pram not suitable
Read full review
Pros
Future AGI
No answers on this topic
Google
  • 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
Read full review
Cons
Future AGI
No answers on this topic
Google
  • Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
  • Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
  • Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users
Read full review
Performance
Future AGI
No answers on this topic
Google
Google is always top notch with their security and user interface performance. We use Google's entire suite in our business anyways, so using Vertex became second nature very quickly. I will say, though, that Google does need to come down on the price somewhat with their token allocation. Also, their UI is very robust, so it does require some time for training to really master it.
Read full review
Alternatives Considered
Future AGI
No answers on this topic
Google
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 feature-set. This makes it suitable for bigger customers that have the capital and time to spend on a bigger project that is well researched and not quick to market like some of the other options that feel like a light-version of this.
Read full review
Return on Investment
Future AGI
No answers on this topic
Google
  • It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
  • It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
  • It'll help you in terms of Security wherein we can use row level security access to authorized persons.
Read full review
ScreenShots

Vertex AI Screenshots

Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.Screenshot of gen AI for summarization, classification, and extraction - Text prompts can be created to handle any number of tasks with Vertex AI’s generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI’s PaLM API for text can be used to design prompts with flexibility in terms of their structure and format.Screenshot of Custom ML training overview and documentation - An overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions.Screenshot of ML model training and creation -  A guide that shows how Vertex AI’s AutoML is used to create and train custom machine learning models with minimal effort and machine learning expertise.Screenshot of deployment for batch or online predictions - When using a model to solve a real-world problem, the Vertex AI prediction service can be used for batch and online predictions.