# Best AWS Marketplace Software - Page 55

## How Many AWS Marketplace Software Products Does G2 Track?

**Total Products under this Category:** 2,544

### Category Stats (Jul 2026)

- **Average Rating:** 4.4/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** ELK Kubernetes Container Solution Stack (+1.95%) - Among all products in this category, ELK Kubernetes Container Solution Stack recorded the largest rating increase compared to last month

_Last updated: July 31, 2026_

## How Does G2 Rank AWS Marketplace Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 29,700+ Authentic Reviews
- 2,544+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

**Sponsored**

### Fortinet Managed Rules for AWS WAF

Fortinet’s WAF rulesets are additional security signatures that can be used to enhance the protections included in the base AWS WAF product. They are updated on a regular basis to include the latest threat intelligence from the award-winning FortiGuard Labs. The Complete OWASP Top 10 Ruleset provides a comprehensive package for web application protection offered by Fortinet to help address the OWASP Top 10 web application threats. Includes protection for various Injection attacks such as SQL and command Injection , Cross Site Scripting, General and Known Exploits, Malicious Bots and Common Vulnerabilities and Exposures (CVE).

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=2598&secure%5Bchosen_at%5D=2026-07-31T14%3A31%3A39Z&secure%5Bdisplayable_resource_id%5D=2598&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2598&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=145276&secure%5Bresource_id%5D=2598&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Faws-marketplace%3Fpage%3D55&secure%5Btoken%5D=8ec93a5f1877a4d0d2699d2aab9afae3032cda9f9c79c09f5cdca311e58fad4e&secure%5Burl%5D=https%3A%2F%2Fwaf-security.com%2F%3Futm_source%3Dg2%26utm_medium%3Dcpc%26utm_id%3Ddirect-publisher&secure%5Burl_type%5D=custom_url)

### [BCB Blockchain Observer Node](https://www.g2.com/products/bcb-blockchain-observer-node/reviews)

The BCB Blockchain Observer Node is a pivotal component of the BCB Blockchain ecosystem, designed to enhance the development of smart city solutions. Built on the Tendermint framework, it ensures immediate authentication and prevents forking, thereby bolstering the efficiency and security of business operations. Developed in Golang, the node employs a PBFT-DPOS consensus algorithm for swift information dissemination and block consensus. It supports Ethereum's EVM and utilizes Docker containers for smart contract execution, facilitating seamless development using Solidity and Golang. Key Features and Functionality: - Immediate Authentication: Ensures rapid verification processes, enhancing transaction speed and reliability. - Fork Prevention: Maintains blockchain integrity by preventing chain splits. - PBFT-DPOS Consensus Algorithm: Combines Practical Byzantine Fault Tolerance with Delegated Proof of Stake for efficient consensus. - EVM Compatibility: Supports Ethereum Virtual Machine, allowing for the execution of Ethereum-based smart contracts. - Docker Integration: Utilizes Docker containers for flexible and isolated smart contract operations. Primary Value and User Solutions: The BCB Blockchain Observer Node addresses the need for a robust and efficient blockchain infrastructure tailored for smart city applications. By providing immediate authentication and preventing forking, it ensures the reliability and security essential for urban development projects. Its compatibility with Ethereum's EVM and support for Docker containers offer developers a versatile platform for creating and deploying smart contracts, streamlining the development process and fostering innovation in smart city solutions.

#### Who Is the Company Behind BCB Blockchain Observer Node?

- **Seller:** [BCB Innovation](https://www.g2.com/sellers/bcb-innovation)
- **HQ Location:** N/A
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

### [BeeGFS](https://www.g2.com/products/beegfs/reviews)

BeeGFS is a high-performance parallel file system developed by ThinkParQ, designed to meet the demanding storage requirements of environments such as High-Performance Computing , Artificial Intelligence , Machine Learning , Media & Entertainment, Life Sciences, and Oil & Gas. Emphasizing scalability, flexibility, and robustness, BeeGFS enables seamless expansion of storage capacity and performance by distributing file contents and metadata across multiple servers. Its user-friendly design ensures straightforward installation and management, making it accessible for a wide range of applications. Key Features and Functionality: - Distributed Architecture: BeeGFS distributes both file contents and metadata across multiple servers, eliminating bottlenecks and enhancing performance. - Scalability: The system allows for linear scalability, enabling users to increase storage capacity and performance seamlessly by adding more servers and disks. - High Throughput: BeeGFS delivers exceptional client throughput, achieving up to 8 GB/s with a single process on a 100 GBit network, and can fully saturate network bandwidth with multiple streams. - Flexibility: It supports various Linux distributions and kernels, and can run on top of existing local file systems like XFS or ZFS, providing adaptability to different infrastructures. - High Availability: The Enterprise Edition offers features such as metadata and file content mirroring, quota enforcement, and storage pools, ensuring continuous operation and data integrity. Primary Value and User Solutions: BeeGFS addresses the critical need for high-performance, scalable, and reliable storage solutions in data-intensive environments. By distributing data and metadata, it eliminates single points of failure and performance bottlenecks, ensuring efficient data access and management. Its ease of deployment and integration with existing infrastructures reduce administrative overhead, allowing organizations to focus on their core computational tasks. The system's flexibility and robustness make it an ideal choice for industries requiring rapid data processing and analysis, ultimately enhancing productivity and enabling new data-driven methodologies.

**Average Rating:** 3.5/5.0

**Total Reviews:** 1

#### How Do G2 Users Rate BeeGFS?

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.5/10)
- **Ease of Admin:** 6.7/10 (Category avg: 8.6/10)
- **Ease of Use:** 10.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind BeeGFS?

- **Seller:** [ThinkParQ](https://www.g2.com/sellers/thinkparq)
- **Year Founded:** 2014
- **HQ Location:** Kaiserslautern, DE
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=237b5fbcb269f7aa2898fe9a61d8c2a4a35252321b646172f5fb42f0aafc1853&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10100426%2F&secure%5Burl_type%5D=linkedin_company_website)  
17 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 100% Small

#### What Are G2 Users Discussing About BeeGFS?

- [What is BeeGFS used for?](https://www.g2.com/discussions/what-is-beegfs-used-for)

### [BeeGFS - Free (Community Support)](https://www.g2.com/products/beegfs-free-community-support/reviews)

BeeGFS is a high-performance, hardware-independent parallel file system designed to meet the demands of performance-driven environments such as High-Performance Computing , Artificial Intelligence , Machine Learning , Media & Entertainment, Life Sciences, and Oil & Gas. Developed by ThinkParQ, BeeGFS offers scalable, high-throughput access to file storage systems, ensuring optimal performance and flexibility. Key Features and Functionality: - Distributed File Contents and Metadata: BeeGFS avoids architectural bottlenecks by distributing file contents across multiple storage servers and metadata across multiple metadata servers, enhancing scalability and performance. - High Scalability: The system allows for the addition of servers and disks without downtime, enabling linear scalability to meet growing data demands. - Flexibility: BeeGFS supports various Linux distributions and kernels, and runs on any Linux machine, providing flexibility in deployment. - Ease of Use: With simple installation and management, BeeGFS is designed for ease of use, featuring a graphical administration and monitoring system for efficient system management. Primary Value and User Solutions: BeeGFS addresses the challenges of managing large-scale, data-intensive workloads by providing a scalable and high-performance storage solution. Its distributed architecture ensures that both file contents and metadata are efficiently managed, preventing bottlenecks and enabling seamless expansion as data needs grow. This makes BeeGFS particularly valuable for organizations requiring robust and flexible storage systems to support complex computational tasks and large datasets.

#### Who Is the Company Behind BeeGFS - Free (Community Support)?

- **Seller:** [ThinkParQ](https://www.g2.com/sellers/thinkparq)
- **Year Founded:** 2014
- **HQ Location:** Kaiserslautern, DE
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=237b5fbcb269f7aa2898fe9a61d8c2a4a35252321b646172f5fb42f0aafc1853&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10100426%2F&secure%5Burl_type%5D=linkedin_company_website)  
17 employees on LinkedIn®

### [Behavior Scorecard](https://www.g2.com/products/behavior-scorecard/reviews)

Behavior Scorecard monitors risk after onboarding.

#### Who Is the Company Behind Behavior Scorecard?

- **Seller:** [ElectrifAi](https://www.g2.com/sellers/electrifai-6f99f1b0-33e0-489e-acf4-a875e256745d)
- **Year Founded:** 2004
- **HQ Location:** Jersey City, US
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=3bf532f9fd4f390ca03b10f0e632cc3637c89850b560cea72055c9e9bbce35b0&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Felectrifai&secure%5Burl_type%5D=linkedin_company_website)  
65 employees on LinkedIn®

### [Benchy](https://www.g2.com/products/benchy/reviews)

Benchy is a versatile benchmarking tool designed to evaluate and optimize the performance of AI and machine learning models. It provides real-time dashboards, seamless dataset management, and supports various databases, including RedisDB and YCSB. Benchy enables developers and researchers to assess efficiency, accuracy, and scalability, facilitating informed decisions to enhance AI applications. Key Features and Functionality: - Real-Time Dashboards: Offers immediate insights into model performance metrics. - Dataset Management: Simplifies the organization and handling of datasets. - Database Support: Compatible with multiple databases, such as RedisDB and YCSB. - Performance Evaluation: Assesses efficiency, accuracy, and scalability of AI models. Primary Value and User Solutions: Benchy addresses the need for comprehensive performance evaluation in AI development. By providing real-time analytics and robust dataset management, it empowers users to identify bottlenecks, optimize model performance, and ensure scalability, ultimately leading to more efficient and reliable AI applications.

#### Who Is the Company Behind Benchy?

- **Seller:** [sensor.live - IoT Platform SaaS](https://www.g2.com/sellers/sensor-live-iot-platform-saas)
- **HQ Location:** N/A
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

### [BERT Base Cased PyTorch Hub Extractive Question Answering](https://www.g2.com/products/bert-base-cased-pytorch-hub-extractive-question-answering/reviews)

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface\_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

#### Who Is the Company Behind BERT Base Cased PyTorch Hub Extractive Question Answering?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Base MEDLINE/PubMed](https://www.g2.com/products/bert-base-medline-pubmed/reviews)

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on MEDLINE/PubMed returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

#### Who Is the Company Behind BERT Base MEDLINE/PubMed?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Base Multilingual Uncased](https://www.g2.com/products/bert-base-multilingual-uncased/reviews)

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

#### Who Is the Company Behind BERT Base Multilingual Uncased?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Base Uncased PyTorch Hub Extractive Question Answering](https://www.g2.com/products/bert-base-uncased-pytorch-hub-extractive-question-answering/reviews)

This is a Extractive Question Answering model from PyTorch Hub

#### Who Is the Company Behind BERT Base Uncased PyTorch Hub Extractive Question Answering?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Base Uncased PyTorch Hub Sentence Pair Classification](https://www.g2.com/products/bert-base-uncased-pytorch-hub-sentence-pair-classification/reviews)

This is a Sentence Pair Classification model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface\_pytorch-transformers/ ). It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and then fine-tuning the entire model on [QNLI](https://rajpurkar.github.io/SQuAD-explorer/ ) dataset.

#### Who Is the Company Behind BERT Base Uncased PyTorch Hub Sentence Pair Classification?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Base Wikipedia and BooksCorpus](https://www.g2.com/products/bert-base-wikipedia-and-bookscorpus/reviews)

It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on WikiPedia and BookCorpus returns an embedding of the input pair of sentences. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

#### Who Is the Company Behind BERT Base Wikipedia and BooksCorpus?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Large Cased](https://www.g2.com/products/bert-large-cased/reviews)

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

#### Who Is the Company Behind BERT Large Cased?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Large Cased PyTorch Hub Sentence Pair Classification](https://www.g2.com/products/bert-large-cased-pytorch-hub-sentence-pair-classification/reviews)

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

#### Who Is the Company Behind BERT Large Cased PyTorch Hub Sentence Pair Classification?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Large Cased Whole Word Masking SQuAD](https://www.g2.com/products/bert-large-cased-whole-word-masking-squad/reviews)

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

#### Who Is the Company Behind BERT Large Cased Whole Word Masking SQuAD?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

### [BERT Large Uncased](https://www.g2.com/products/bert-large-uncased/reviews)

It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and then fine-tuning the entire model on [QNLI](https://rajpurkar.github.io/SQuAD-explorer/ ) dataset. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

#### Who Is the Company Behind BERT Large Uncased?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

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Spotlight Categories

[Sales Training and Onboarding Software](https://www.g2.com/categories/sales-training-and-onboarding)

[Employee Recognition Software](https://www.g2.com/categories/employee-recognition)

[Session Replay Software](https://www.g2.com/categories/session-replay)

[Managed File Transfer (MFT) Software](https://www.g2.com/categories/managed-file-transfer-mft)

[Generative AI Infrastructure Software](https://www.g2.com/categories/generative-ai-infrastructure)

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- [Google Workspace ERP](/categories/google-workspace-erp)
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[Browse AWS Marketplace Themes](/categories/aws-marketplace/themes)

 ![Neeraja Prakash](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Neeraja Prakash")
NP

Researched and written by [Neeraja Prakash](https://research.g2.com/insights/author/neeraja-prakash)

Updated June 16, 2025

The AWS Marketplace category includes a diverse range of software solutions designed to enhance and optimize operations within the Amazon Web Services (AWS) ecosystem. These products offer pre-configured Amazon Machine Images (AMIs), container solutions, and specialized services that cater to various business needs, from secure web hosting and data labeling to machine learning model deployment and transportation cost prediction. By leveraging AWS infrastructure, these solutions provide scalability, security, and seamless integration with existing AWS services, enabling businesses to efficiently deploy, manage, and scale their applications and services in the cloud.

To qualify for inclusion in the AWS Marketplace category, a product must:

- Have features and use cases that do not fit into existing marketplace apps categories
- Be designed to integrate with or enhance the functionality of AWS services, providing value through pre-configured environments, specialized tools, or managed services

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