

Google Firebase and Amazon Bedrock compete in cloud-based application development and management services. Firebase seems to have the upper hand in offering comprehensive out-of-the-box functionalities and ease of use, while Bedrock stands out in AI-focused applications with better security and model management.
Features: Google Firebase offers Realtime Database, Cloud Functions, and robust analytics tools, enabling smooth creation and management of mobile and web apps without backend infrastructure. The integration capabilities and ease of usability make it highly functional. Amazon Bedrock provides a platform centered around machine learning and AI, with foundational models and customization options, making it highly suitable for AI-centric applications. Bedrock is noted for its security focus and model management capabilities.
Room for Improvement: Google Firebase users have indicated the need for enhanced function management controls, improved logging capabilities, and better real-time features. Clearer pricing transparency and integration with other systems are also areas of needed improvement. Bedrock could benefit from improved documentation and more integration points beyond its ecosystem, as well as clearer pricing structures.
Ease of Deployment and Customer Service: Both Google Firebase and Amazon Bedrock utilize public cloud deployments. Firebase is enhanced by its extensive documentation and supportive community, often eliminating the need for direct customer service. Bedrock, while supported by documentation, can enhance its official support channels for better user experience.
Pricing and ROI: Google Firebase offers a free tier alongside a pay-as-you-go model, which is cost-effective for startups and scalable for larger projects, though pricing can become complex with increased usage. Amazon Bedrock, while competitive, has faced feedback on unexpected costs. Both require careful understanding of their pricing structures to maximize ROI.
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.
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.
If the community doesn't have the answers, then I contact the Google Firebase team directly.
I do not currently find any other competitors that match its offering.
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.
It can handle all tiers of the market effectively.
Google also has some other cloud services, but when we compare Google Firebase, it is not as scalable.
I would rate the scalability of Google Firebase as eight because I miss some features from MS SQL, particularly the SQL functions.
The stability of Amazon Bedrock is good as I have not faced any issues.
In our company, we have prototypes on Google Firebase and productions on AWS Cloud.
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.
Since Google Firebase is a NoSQL platform, areas for improvement include enhancing query functionalities to be more equivalent to SQL, like MS SQL.
Automation testing with mobile devices could be improved, especially in regression testing to ensure that new feature launches do not affect other features.
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.
They could have implemented credits based on usage so that users would be able to easily calculate the pricing and provide it to customers.
Google Firebase offers a reasonable pricing structure.
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.
What I appreciate most about Google Firebase is the speed, accuracy, and security it offers.
It allows for pre-launch testing without publishing it on Google Play right away.
A real-time database.
| Product | Market Share (%) |
|---|---|
| Google Firebase | 2.6% |
| Amazon Bedrock | 1.8% |
| Other | 95.6% |

| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 10 |
| Large Enterprise | 8 |
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.
Google Firebase is a stable, reliable, and scalable mobile platform that enables you to quickly develop apps, accelerate them, and monitor their performance.
With Google Firebase You Can
Google Firebase Features:
SDKs supported by Google Firebase:
Benefits of Google Firebase:
Reviews from Real Users
PeerSpot user Nilakshi S., technical team lead at AuthentiCode says, “The solution is very stable and very reliable. There aren't any bugs or glitches.”
"What I like most about Google Firebase is that it's one of the easier options to host a website or app quickly," says Craig F., application development manager at a financial services firm.
A senior developer at a consultancy says, “The documentation for Google Firebase is great. Also, what I like about it is the integration to Android, which is the reason I went with Firebase.”
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