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Gemini Enterprise Agent Platform vs. HPE Private Cloud AI

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Gemini Enterprise Agent Platform

    Score8.9 out of 10
    N/AThe 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

    HPE Private Cloud AI

    N/AN/AHPE Private Cloud AI is a turnkey, full-stack infrastructure platform co-developed with NVIDIA to support the development and deployment of Generative AI, Retrieval-Augmented Generation (RAG), and high-performance inference workloads. The solution integrates compute, storage, and networking with a pre-configured software stack to provide a private, cloud-managed environment for AI lifecycle management.N/A
    Pricing
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    Editions & Modules
    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
    No answers on this topic
    Offerings
    Pricing Offerings
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeOptionalNo setup fee
    Additional DetailsPricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
    More Pricing Information
    Community Pulse
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    Considered Both Products
    Google
    No answer on this topic
    Hewlett Packard Enterprise (HPE)
    No answer on this topic
    Key User Insights
    Would buy again
    93%
    Would buy again
    14 Answers
    No answers on this topic
    Delivers good value for the price
    92%
    Delivers good value for the price
    12 Answers
    No answers on this topic
    Happy with the feature set
    87%
    Happy with the feature set
    13 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    78%
    Lived up to sales and marketing promises
    7 Answers
    No answers on this topic
    Implementation went as expected
    86%
    Implementation went as expected
    12 Answers
    No answers on this topic
    Features
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    AI Development
    Comparison of AI Development features of Gemini Enterprise Agent Platform and HPE Private Cloud AI
    Feature
    Gemini Enterprise Agent Platform
    8.6
    2 Ratings
    14% above category average
    HPE Private Cloud AI
    -
    Ratings
    Machine learning frameworks8.62 Ratings00 Ratings
    Data management9.12 Ratings00 Ratings
    Data monitoring and version control8.22 Ratings00 Ratings
    Automated model training9.12 Ratings00 Ratings
    Managed scaling7.72 Ratings00 Ratings
    Model deployment8.62 Ratings00 Ratings
    Security and compliance8.62 Ratings00 Ratings
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    All AlternativesView all alternativesView all alternatives
    User Ratings
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    Likelihood to Recommend
    7.6
    (15 ratings)
    -
    (0 ratings)
    Performance
    7.5
    (12 ratings)
    -
    (0 ratings)
    Configurability
    7.7
    (12 ratings)
    -
    (0 ratings)
    User Testimonials
    Gemini Enterprise Agent PlatformHPE Private Cloud AI
    Likelihood to Recommend
    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
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    Pros
    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
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    Cons
    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
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    Performance
    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.
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    Alternatives Considered
    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.
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    Return on Investment
    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.
    Incentivized
    Read full review
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    ScreenShots

    Gemini Enterprise Agent Platform 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.