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Amazon Bedrock vs Google Compute Engine comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 16, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.8
Amazon Bedrock offers cost-efficient usage-based charges but unexpected fees may impact perceived returns despite reduced manual intervention.
Sentiment score
6.3
Compute Engine offers initial cost savings and performance boosts, but financial benefits and precise savings remain challenging to gauge.
 

Customer Service

Sentiment score
7.9
Amazon Bedrock's customer service is highly rated for efficiency and responsiveness, with strong support and documentation quality.
Sentiment score
6.3
Google Compute Engine support receives mixed reviews; some praise responsiveness while others note inadequate assistance and delayed responses.
So, you always have to bridge the gap by presenting scenarios, getting recommendations, and testing or validating those assumptions.
 

Scalability Issues

Sentiment score
7.1
Amazon Bedrock excels in scalability using AWS, integrating well yet faces limitations like rate and token limits at hyperscale.
Sentiment score
8.0
Google Compute Engine is scalable and versatile, suitable for varying workloads, with strong network and security features.
Amazon Bedrock is quite highly scalable, but there are some limitations they impose on the accounts, which could be an area for improvement.
It scales well with AWS Lambda, AWS Transcribe, and Polly.
It is scalable on a truly global basis.
 

Stability Issues

Sentiment score
8.6
Amazon Bedrock is reliable and stable, handling workloads seamlessly, maintaining performance, and achieving high user satisfaction ratings.
Sentiment score
8.3
Google Compute Engine is highly reliable with a 99.99% SLA, frequently surpassing performance expectations and stability compared to competitors.
 

Room For Improvement

Users seek improvements in documentation, integration, pricing, UI, AI capabilities, and introduction of Amazon native models.
Google Compute Engine users seek UI enhancements, expanded options, improved security, synchronization, and better support and marketing focus.
In AgenTek AI business, the only foundation models we can rely on for scaling now are the Cloud 3.5 models like Haiku and SONNET, designed for low latency and complex AI business use cases.
If AWS provided methods, like five or six prompts that yield specific results, it would ease development.
The user interface of Amazon Bedrock on the management console needs improvements.
 

Setup Cost

Amazon Bedrock offers flexible, consumption-based pricing, cost-effective for experimentation, though enterprise models can be expensive.
Google Compute Engine offers competitive, flexible pricing, often cheaper than Azure and AWS, with savings possible through resource optimization.
Our cost is incredibly low, operating for a few hundred dollars a month in production.
One customer paid around $100 to $200 per month, which was significant given their overall infrastructure costs.
The pricing and licensing of Amazon Bedrock are quite flexible.
 

Valuable Features

Amazon Bedrock offers secure, cost-effective AI model customization and seamless integration, enhancing performance and trust in deployments.
Google Compute Engine offers customizable VMs, scalability, cost-effectiveness, security features, and diverse compute and storage options.
It has improved operational costs and efficiency significantly, saving money and enhancing the quality of operations.
Amazon Bedrock offers an environment where we only pay for the model we use, and AWS handles the scaling.
The ability to make changes in the foundational model is valuable since different customers have specific needs, allowing customization.
In GCP, there's a custom configuration feature unlike AWS and Azure.
 

Categories and Ranking

Amazon Bedrock
Ranking in Infrastructure as a Service Clouds (IaaS)
13th
Average Rating
8.2
Reviews Sentiment
7.3
Number of Reviews
11
Ranking in other categories
AI Infrastructure (3rd)
Google Compute Engine
Ranking in Infrastructure as a Service Clouds (IaaS)
10th
Average Rating
8.8
Reviews Sentiment
7.0
Number of Reviews
16
Ranking in other categories
No ranking in other categories
 

Featured Reviews

Charles Powell - PeerSpot reviewer
Very good security features for strengthened data protection
I am not going to speak to their roadmap. Amazon operates Bedrock as an ecosystem supporting third-party models. I am speculating here, but I am sure those third-party models will always be present. However, one must consider that Amazon native models could proliferate Bedrock in the future. We would welcome Amazon native models to Bedrock, since, if they are natively built by Amazon, they are tuned to SageMaker and other Amazon service layers. They have done this somewhat for generative AI, however, in AgenTek AI business, the only foundation models we can rely on for scaling now are the Cloud 3.5 models like Haiku and SONNET, designed for low latency and complex AI business use cases.
Arundeep Veerabhadraiah - PeerSpot reviewer
A highly scalable and seamless platform which is easily automated
One of GCE's best features is the managed instance groups. We typically use managed instance groups for high availability. You can set certain parameters for managed instance groups where if the load of the computer or server increases beyond 80%, for example, the solution will automatically spawn another instance, and the load will be automatically divided between two systems. If the load is 80% of one of the VMs or GCEs, once the load is divided, it comes down to 40%, so the availability of your systems goes up. However, that all depends on the parameters or configurations we put on the instance group. You also have regular health checks on these managed instance groups, which are configurable. If these health checks determine something wrong with the VM, they will automatically kick off or spawn a new GCE instance. This way, the outage time is less. Previously, on-premises, unless somebody reported the issue to the helpdesk saying that a particular service was unavailable, then a support team would need to troubleshoot what went wrong, which takes a long time. At least 30 minutes to one hour. But by using these managed instance groups, we can reduce the outage time, and second, we can configure them with minimal resources, bringing down our cost. And if the load increases, the managed instance groups automatically respond to new things. Subsequently, our costs decrease. We have a wide range of VMs. There are general-purpose VMs that can be used for hosting general-purpose applications. If some of our applications are memory intensive, then we have a lot of VMs in the M1 series. We can use a range of memory-optimized VMs for these things. We have C-series VMs for compute-intensive applications. If we use some mathematical formulas and require a very high throughput from that, there are GPU-optimized VMs used for machine learning or 3D visualizations in rendering software. GPU-enabled VMs are pretty powerful and responsive. Again, the best part is that we can spin them up when we need them, and once we're done with our work, we can shut them down, allowing tremendous cost savings for any customer. Previously, if we wanted a very high-configuration VM, we had to own the entire hardware and have it on our on-prem data center. And once we'd done with a particular activity, the system would just be lying there on our premises. That is not the case now. We use and decommission it, so we're only billed for the time we're using the product. One of the best things is the preemptible VMs or Spot VMs. These are the cheapest VMs in Google Cloud, but it has a string attached to it where Google can shut down these VMs whenever Google teams split. You only get about 90 seconds notice before they shut down this particular VM. There are scenarios where customers can use these preemptible VMs, for example, when running a batch job. Batch jobs are run once or twice daily, depending on the customer's requirement. Once we are done running these batches, we can decommission the VM. Even if, in the middle of this batch job, Google shuts down these VMs, we can pick up the processing from wherever the VM left off. These are some of the beautiful things we have on Google Cloud concerning the Compute Engine.
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Top Industries

By visitors reading reviews
Manufacturing Company
18%
Financial Services Firm
14%
Computer Software Company
13%
Transportation Company
9%
Manufacturing Company
23%
Computer Software Company
16%
University
9%
Financial Services Firm
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What is your experience regarding pricing and costs for Amazon Bedrock?
The pricing and licensing of Amazon Bedrock are quite flexible.
What needs improvement with Amazon Bedrock?
Currently, I do not have any thoughts about what areas of Amazon Bedrock need improvement.
What is your primary use case for Amazon Bedrock?
I am using Amazon Bedrock ( /products/amazon-bedrock-reviews ) mainly to do some analysis of customer support calls.
What do you like most about Google Compute Engine?
Everything is simple and useful. The initial setup is not challenging.
What is your experience regarding pricing and costs for Google Compute Engine?
Google resources are cheaper compared to AWS and Microsoft Azure. Among the three, Google is the cheapest option.
What needs improvement with Google Compute Engine?
Google has a lack of focus on their products. They have many products in various areas of the market, but they do not productize or appeal to the market effectively. They should concentrate on prod...
 

Overview

 

Sample Customers

Information Not Available
Allthecooks, BetterCloud, Bluecore, Cosentry, Evite, Ezakus, HTC, Infectious Media, iStreamPlanet, Mendelics, SageMathCloud, Sedex, Treeptik, Wibigoo, Wix, zulily, Zync
Find out what your peers are saying about Amazon Bedrock vs. Google Compute Engine and other solutions. Updated: April 2025.
850,028 professionals have used our research since 2012.