Overview
Product Information on Google Cloud Platform
Updated 13th October 2025
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Overall experience with Google Cloud Platform
IT ASSOCIATE
<50M USD, IT ServicesFAVORABLE
“"Scalable, Developer-Friendly Cloud Platforms with strong Data & AI Capabilities"”
We have been using Google cloud platform to run production-grade applications and backend services at scale. Overall, the platform has been reliable, scalable, and developer-friendly. Deployment speed, managed services, and integration with modern tooling worked very well for our use case.
However, cost predictability and billing transparency can sometimes be challenging, especially during rapid scaling. Support response quality varies depending on the plant tier. That said, for high-growth environments and modern architectures, GCP has delivered strong performance and flexibility.
Technical Content Writer
50M - 250M USD, IT ServicesCRITICAL
“Models and AI Capabilities Fall Short of Expected Results and Performance”
Not Seeing Appropriate Results for the Models and AI Capabilities as we had expected from it.
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Strategic Cloud Platform Services
About Company
Company Description
Googlers is a company that creates products intended to create opportunities for an extensive audience, regardless of their location across the globe. The company values diverse perspectives, imaginations and non-conformity to predefined norms and impossibilities. The goal is to build products while incorporating uniqueness of each individual involved in this process, aiming to make their products accessible and useful to all.
Company Details
Company type
Public
Year Founded
1998
Head office location
Mountain View, CA, United States
Annual Revenue
30B+ USD
Website
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VP, IT Infrastructure
I am curious if anyone has recommendations for an objective third party to provide an external evaluation of GCP environments from a Fin Ops/Cost Optimization perspective. Many suppliers in the space selling reporting toolsets or serving as VARs but am interested in finding an independent third party.Director of IT
See Full DiscussionI have conducted my own investigation and analysis using native cloud platform tooling, applying the Technology Business Management (TBM) taxonomy, and LeanIX, to help understand cloud costs. Apptio is another product I was looking at as well to help improve cloud costs. Apptio is easily integrated with LeanIX. https://www.apptio.com/products/ibm-apptio/ https://www.leanix.net/en/enterprise-architecture/integrations#apptio
Cloudability is AppTio’s FinOps certified platform to enable cloud cost optimization efforts:
https://www.apptio.com/products/cloudability/
My opinion is that I don’t trust independent third-party’s (consultants) to take an unbiased approach nor even have an effective diligent approach. I have had average to poor experiences with third-party’s that management in my previous organization brought in to conduct an investigation. Given good tooling and a decent budget I think you are better off creating your own cloud centre of excellence that follows best practices for workload cost management and optimization.
Lastly, I read the recent post from Curt Hodge on that question, he is recommending PointFive. I had a look at PointFive’s website information and I would agree with Curt’s recommendation to take a closer look at the PointFive solution https://www.pointfive.co/ however at first glance they seem to be AWS focused and there is no mention of GCP integration.
VP of Product Management
We have a Vertex AI environment and we plan to apportion the consumption costs use case wise that are on onboarded on to a project. Currently Google Cloud provides option to extract data for billing at Project level only and we can get details below project level.
Hence trying to check if there is a option/configurations to be made to assign costs at use case wise that are onboarded with in a project.CIO
See Full Discussioncheck the below links:
https://cloud.google.com/billing/docs/how-to/export-data-bigquery-tables/standard-usage#tags
https://cloud.google.com/vertex-ai/docs/pipelines/understand-pipeline-cost-labels#view_pipeline_resources_cost
Google Cloud Platform Reviews and Ratings
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- IT ASSOCIATE<50M USDIT ServicesReview Source
"Scalable, Developer-Friendly Cloud Platforms with strong Data & AI Capabilities"
We have been using Google cloud platform to run production-grade applications and backend services at scale. Overall, the platform has been reliable, scalable, and developer-friendly. Deployment speed, managed services, and integration with modern tooling worked very well for our use case. However, cost predictability and billing transparency can sometimes be challenging, especially during rapid scaling. Support response quality varies depending on the plant tier. That said, for high-growth environments and modern architectures, GCP has delivered strong performance and flexibility. - Software Developer<50M USDServices (non-Government)Review Source
Strong Data Capabilities and Seamless Integrations With Complex Cost Management Risks
As a Google cloud platform user, I'm telling you this is a powerful & enterprise level cloud platform ecosystem with a strong data & AI capabilities. It comes with cost management challenges and also a learning curve. - Software Developer50M-1B USDSoftwareReview Source
GKE Offers Robust AI Workload Support, But UI And Documentation Need Improvement
GCP is a strong foundation for AI infrastructure. The compute layer scales well for GPU-intensive workloads, GKE handles the orchestration for model serving and training pipelines, and Google's own fiber network keeps inter-region latency low when moving large datasets or syncing distributed training jobs. Vertex AI integrates natively with this stack. The UI and documentation still need work, especially for the newer AI services - Head Of It Architecture50M-1B USDMediaReview Source
Vertex AI and Big Query Integration Streamline Machine Learning on Large Datasets
GCP offers a suite of specialized services and proprietary TPU infrastructure optimized for for large-scale deep learning and training workloads. Integrated tools like Vertex AI, Vertex AI Pipelines, Kubeflow and Big Query provide access to generative models like Gemini and allow for building, training, deploying, monitoring and managing MLOps. - Engineer50M-1B USDTelecommunicationReview Source
Learning AI Made Easier, But Community and Pricing Uncertainty Remain Issues
I have been using GCP throughout my college years and after it too, as it makes it easier for using high level AI models without any hassle of physical hardware, it helped me learn a lot through online code books and the community.
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