Google Cloud AI Infrastructure Reviews & Product Details

Dramatic Cost Savings on AI Inference and Training

TPUs deliver 4x better performance-per-dollar for inference compared to Nvidia GPUs, with companies like Midjourney slashing costs by 65% after switching EngageBay. Salesforce and Cohere report 3x gains EngageBay in throughput. Three-year TCO analysis for 1,000-device deployments shows $8.8M TPU savings versus H100, driven by energy efficiency and per-workload economics.

2. Superior Energy Efficiency and Performance

Google's TPUv7 (Ironwood) is 100% better in performance per watt than their TPUv6e (Trillium). TPUv7 Ironwood has a peak computational performance rate of 4,614 TFLOP/s FinancesOnline. A TPU v5e pod delivers up to 100 quadrillion int8 operations per second, or 100 petaOps of compute power

Flexibility Across Hardware Options

With Google Cloud, you can choose from GPUs, TPUs, or CPUs to support a variety of use cases including high performance training, low cost inference, and large-scale data processing. This flexibility means you're not locked into a single vendor or architecture. Review collected by and hosted on G2.com.

Sometimes you have to constantly review the relevant documentation, and the parameters that can be configured for the development of a particular model tend to involve concepts that must be read carefully so as NOT to make mistakes when generating said models. Review collected by and hosted on G2.com.

Google Cloud gives powerful tools and machines (like TPUs) to build and run AI faster. It is easy to scale up or down and works well with Google’s other products. It keeps data safe and offers good performance worldwide. Good for mission critical & enterprise workloads. Users generally find Google’s docs, guides, forums, etc., to be thorough, which helps especially for smaller or less urgent issues. Review collected by and hosted on G2.com.

Google Cloud support can be slow, especially on lower plans. Its pricing is complex, and costs can rise quickly. Some tools and regions are hard to use or not available everywhere. Basic or lower-tier support tends to offer generic advice rather than offering tailored or deep technical solutions. Review collected by and hosted on G2.com.

I use it as part of my work building websites for real estate companies in the US, and what I like most is how stable and fast it is. It helps me process images, manage data, and integrate AI features into property listings without slowing down the sites. It also connects well with other Google Cloud tools, so I don’t waste time switching between platforms. Review collected by and hosted on G2.com.

Sometimes the setup feels a little technical, and the pricing details could be easier to understand. But once everything is running, it works smoothly and supports my daily tasks without issues. Review collected by and hosted on G2.com.

What I like most is how easy it is to scale compute resources for training and deploying AI models. In my team, we use Google Cloud AI Infrastructure to run machine learning experiments, train deep learning models, and manage data processing pipelines. The integration with Vertex AI, BigQuery, and Cloud Storage makes the workflow seamless, allowing us to move from data preparation to model deployment in one environment.

The platform delivers consistent performance — even under heavy workloads, uptime and response times remain excellent. The flexibility to choose between GPUs and TPUs for different workloads helps optimize both cost and performance. It’s also well-documented, making automation and orchestration through Cloud Functions or Kubernetes straightforward for experienced users. Review collected by and hosted on G2.com.

The pricing model can be complex, especially for long-running GPU or TPU training jobs. It takes time to understand cost optimization options and configure resource quotas properly. The initial setup for custom environments and IAM permissions requires some cloud expertise. However, once configured, everything runs smoothly and reliably. Also, while support is generally responsive, more real-time assistance during production incidents would be helpful. Review collected by and hosted on G2.com.

Google Cloud AI Infrastructure offers unmatched scalability and performance for training and deploying large AI and ML models. The TPUs and GPUs are incredibly powerful and optimized for deep learning workloads, reducing training time significantly. I also appreciate the integration with Vertex AI, which simplifies model lifecycle management. The network reliability and global reach of Google Cloud make it ideal for enterprises running mission-critical AI applications. Review collected by and hosted on G2.com.

The biggest challenge is the complex setup and pricing structure it can be difficult for new users to estimate costs, especially when scaling workloads dynamically. Some advanced configurations require deep cloud expertise, and the documentation can be a bit dense for non-engineering teams. Additionally, billing across different services (Compute, Storage, AI APIs) could be more unified and transparent. Review collected by and hosted on G2.com.

I really like how easy it is to scale up resources when training big models. The performance is solid, and integration with tools like Vertex AI and TensorFlow makes the whole process smoother. It saves a lot of time because you don’t have to worry much about managing servers or setup. The GPUs and TPUs run fast, and overall, it feels stable and well-optimized for AI projects. Review collected by and hosted on G2.com.

The pricing can get a little tricky to understand, especially when you’re running multiple experiments. Sometimes, figuring out the right configuration or cost estimate takes extra time. Also, the documentation is pretty detailed but could be easier to follow for beginners. Apart from that, it’s a great platform once you get used to it. Review collected by and hosted on G2.com.

Google Cloud's AI infrastructure is built to facilitate every stage of the machine learning lifecycle, covering everything from initial development through to deployment. Review collected by and hosted on G2.com.

The cost can become quite high for large-scale or particularly complex projects. Additionally, there is a steep learning curve, as using the platform effectively demands considerable expertise in both machine learning and Google Cloud services. Another concern is vendor lock-in, which means that moving your project to a different cloud provider can be a difficult process. Review collected by and hosted on G2.com.

Google Cloud AI Infrastructure excels with its high-performance TPUs and flexible GPU options, enabling fast, scalable training for advanced AI models. Vertex AI’s unified tooling simplifies the entire ML cycle experimentation reducing operational overhead and accelerating development. Its reliability, speed, and seamless integration make it a standout choice for AI teams. Review collected by and hosted on G2.com.

Google Cloud AI Infrastructure can be expensive, with complex pricing that’s hard to estimate. TPU workflows have a learning curve, and some tools feel less mature than competitors, requiring additional setup and expertise. Review collected by and hosted on G2.com.

Google Cloud AI is enterprise ready Infrastructure. The integration with all AI services like for Voice automation, image generation, profanity check everything is very seamless. Review collected by and hosted on G2.com.

No features dislike for Google Cloud AI Infrastructure. Only pricing is on a higher side Review collected by and hosted on G2.com.

I like that it provides strong performance for training and running AI models. The setup is smooth, and it connects well with other Google Cloud tools. It’s fast, scalable, and works great for handling large datasets. The dashboard is also clean and easy to understand once you get used to it. Review collected by and hosted on G2.com.

Some services can get expensive if you’re not careful with usage. It also takes a little time to learn all the options and tools available. A few things could be explained better in the documentation. Review collected by and hosted on G2.com.