

VMware Cloud Foundation and Amazon Bedrock compete in virtualization and AI model management categories. VMware Cloud Foundation holds an edge in virtualization suite features, while Amazon Bedrock leads with its security and AI customization flexibility.
Features: VMware Cloud Foundation provides a comprehensive virtualization suite offering features like disaster recovery, clustering load, and life cycle management. It allows for easy deployment across multiple platforms, significantly reducing setup time. Amazon Bedrock offers security and flexibility in AI model customization, including the ability to fine-tune and integrate pre-trained models. It excels in integrating with data sources, making AI output traceable and actionable.
Room for Improvement: VMware Cloud Foundation could improve integration capabilities, pricing models, and upgrade processes, as well as enhance life cycle and multi-site deployment management. Amazon Bedrock needs better documentation, more integration options, and clearer cost transparency while enhancing Gen AI applications and comprehensive AI features.
Ease of Deployment and Customer Service: VMware Cloud Foundation supports diverse environments like on-premises and private cloud settings, with feedback on technical support quality mixed, noting speed and consistency issues. Amazon Bedrock provides straightforward deployment, optimized for public cloud, with generally positive support experiences but calls for better guidance in complex scenarios.
Pricing and ROI: VMware Cloud Foundation is considered expensive with high licensing costs compared to competitors, though its feature value is acknowledged by users, seeing gradual ROI through operational cost reduction. Amazon Bedrock offers reasonable pricing for model experimentation, albeit with some unexpected charges. Its ROI is not frequently assessed, highlighting cost-effectiveness in model hosting.
Amazon Bedrock enabled the use of huge models and the democratization of their use at comparatively low cost, if we host these models in the company.
VMware Cloud Foundation allows cost and time savings by quickly deploying infrastructure requests and integrating automatic ticketing and backup services.
In terms of ROI, although VMware Cloud Foundation is expensive, it saves time in most cases, which indirectly saves costs for users.
We are experiencing the fastest time ever to get things done with AI integrating into our work, regardless of where we are.
So, you always have to bridge the gap by presenting scenarios, getting recommendations, and testing or validating those assumptions.
My experience with the technical support has been very good because they resolved my billing issue within a day.
There are times when support is unclear, and even VMware support personnel may lack familiarity with certain parts, causing difficulties.
Based on my nine years of experience with VMware, I would evaluate their technical support as effective.
The technical support from VMware Cloud Foundation deserves a nine out of ten rating.
It is scalable on a truly global basis.
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.
When more hosts are added, performance goes slow.
VMware Cloud Foundation supports scalability and company growth.
VMware Cloud Foundation is quite scalable, receiving a rating of eight and a half to nine out of ten.
The stability of Amazon Bedrock is good as I have not faced any issues.
The stability of VMware Cloud Foundation is very high.
If one component fails to operate in a timely manner, the entire infrastructure can go down because everything is interconnected.
I am currently satisfied with the stability of VMware Cloud Foundation, though having started only two to three months ago, I am still in the monitoring phase.
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.
For companies in general, the main pain point or main issue related to Amazon Bedrock is security because they are not confident that all information is hidden by this kind of architecture.
If AWS provided methods, like five or six prompts that yield specific results, it would ease development.
VMware Broadcom needs to include auto resource allocation at the VM levels.
The maintenance cost has increased significantly, especially after Broadcom acquired VMware, with a shift from socket-based licensing to core-based licensing.
Each user needs deep knowledge of every aspect of virtualization.
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.
The cost has become very high, especially after Broadcom's acquisition, altering the licensing model to a more expensive core-based system.
When comparing prices of both clouds, AWS is cheaper because they offer a free section for practice.
The price is quite higher than some other vendors.
It has improved operational costs and efficiency significantly, saving money and enhancing the quality of operations.
The valuable features that have helped in leveraging generative AI for operational efficiency improvements include customization capabilities, various types of models suitable for specific use cases, and the integration of knowledge bases.
The ability to make changes in the foundational model is valuable since different customers have specific needs, allowing customization.
The solution now offers auto-deployment of VMs.
All features of VMware Cloud Foundation are valuable to us, as it covers every industry standard protocol and requirement protocol.
VMware Cloud Foundation allows for extensive customization, aligning with our customer requirements.
| Product | Market Share (%) |
|---|---|
| VMware Cloud Foundation | 1.6% |
| Amazon Bedrock | 1.8% |
| Other | 96.6% |


| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 3 |
| Large Enterprise | 27 |
Amazon Bedrock offers comprehensive model customization and integration with AWS services, making AI development more flexible for users. It streamlines content generation and model fine-tuning with a focus on security and cost efficiency.
Amazon Bedrock is engineered to provide a seamless AI integration experience with a strong emphasis on security and user-friendliness. It simplifies AI development by offering foundational models and managed scaling, enhancing both trust and operational efficiency. With its versatile model customization and ease of integration, Bedrock reduces the need for extensive infrastructure management. It supports businesses in deploying pre-trained models, performing generative AI tasks, and improving analytics through AI technologies such as chatbots, sentiment analysis, and data formatting.
What are the key features of Amazon Bedrock?Amazon Bedrock is applied across industries for implementing AI-driven solutions like enhancing customer service with chatbots and improving data analysis with sentiment analysis tools. Businesses create knowledge bases, automate business processes, and utilize pre-trained models for tasks such as invoice processing and customer call analysis. Its integration with large language models assists in text and image generation, offering diverse AI capabilities adaptable to industry needs.
VMware Cloud Foundation makes it easy to deploy and run a hybrid cloud. VMware Cloud Foundation provides integrated cloud infrastructure (compute, storage, networking, and security) and cloud management services to run enterprise applications in both private and public environments.
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