Best AWS Marketplace Software - Page 44

How Many AWS Marketplace Software Products Does G2 Track?

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Last updated: November 01, 2026

How Does G2 Rank AWS Marketplace Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 30,300+ Authentic Reviews
  • 2,450+ 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.

Adverse Drug Reactions - Identifier

The Adverse Drug Reactions - Identifier is an advanced AI-driven solution designed to automate the detection, extraction, validation, and reporting of adverse drug reactions (ADRs from scientific literature. By integrating multiple specialized agents, this framework streamlines pharmacovigilance processes, ensuring timely and accurate regulatory submissions while enhancing patient safety. Key Features and Functionality: - Automated Literature Screening: Continuously scans scientific journals, regulatory bulletins, and clinical trial publications to identify relevant safety information. - Intelligent Data Extraction: Utilizes natural language processing (NLP and machine learning to accurately extract adverse event data, including drug-event relationships and patient demographics. - Validation and Knowledge Integration: Cross-references extracted data with existing safety databases and regulatory guidelines to validate findings and reduce false positives. - Regulatory Report Generation: Automatically converts validated adverse event information into standardized regulatory submission formats, such as Individual Case Safety Reports (ICSR. - Orchestration and Workflow Management: Coordinates the activities of all specialized agents, managing the workflow from literature search to report submission. Primary Value and User Benefits: This solution addresses the challenges of managing the increasing volume and complexity of drug safety data. By automating labor-intensive processes, it enhances efficiency and scalability in pharmacovigilance workflows. Users benefit from improved signal detection capabilities, ensuring early identification of potential safety issues. Additionally, the system ensures compliance with global regulatory standards, facilitating accurate and timely submissions to health authorities. Overall, it supports better patient outcomes through proactive risk management and streamlined reporting processes.

Who Is the Company Behind Adverse Drug Reactions - Identifier?

  • Seller: Virtusa
  • Year Founded: 1996
  • HQ Location: Southborough, Massachusetts, United States
  • LinkedIn® Page: www.linkedin.com
    18,250 employees on LinkedIn®
  • Ownership: NASDAQ: VRTU

AFIB Disease State Predictor

The AFIB Disease State Predictor is a sophisticated machine learning model designed to identify and predict the presence of atrial fibrillation (AFIB, the most common type of treated heart arrhythmia. By analyzing patient data, this tool aids healthcare professionals in early detection and intervention, potentially reducing the risk of complications associated with AFIB. Key Features and Functionality: - Advanced Machine Learning Algorithms: Utilizes state-of-the-art machine learning techniques to analyze complex patient data for accurate AFIB prediction. - Comprehensive Data Analysis: Processes a wide range of patient information, including age, heart rate, blood pressure, and other relevant health metrics. - Real-Time Predictions: Provides timely assessments, enabling prompt clinical decisions and interventions. - Integration with AWS Services: Seamlessly integrates with AWS infrastructure, ensuring scalability, reliability, and security. Primary Value and Problem Solved: The AFIB Disease State Predictor addresses the critical need for early and accurate detection of atrial fibrillation. By leveraging machine learning, it enhances diagnostic precision, supports proactive patient management, and ultimately contributes to improved cardiovascular health outcomes.

Who Is the Company Behind AFIB Disease State Predictor?

Agave Analytics

Agave® Analytics Suite is a cloud-based Energy as a Service solution designed to simplify and enhance energy management for commercial enterprises. By monetizing energy usage patterns, it helps identify optimal times for reducing energy consumption, leading to significant cost savings. Key Features and Functionality: - Real-Time Interval Data: Provides up-to-the-minute insights into energy consumption, enabling prompt and informed decisions. - Sustainability Reporting: Generates comprehensive reports to track and improve sustainability initiatives. - Enhanced Energy Management: Offers tools to monitor and control energy usage effectively, optimizing operational efficiency. - Rate & Billing Auditing: Ensures accuracy in utility bills by auditing rates and charges, preventing overpayments. - Energy Star® Benchmarking: Assists in comparing energy performance against industry standards to identify areas for improvement. - Demand Charge Reduction: Helps lower demand charges by analyzing and managing peak energy usage periods. - Microgrid Efficiency Analysis: Evaluates microgrid performance to enhance efficiency and reliability. - ROI Assessment on Energy Projects: Provides insights into the return on investment for energy-related projects, facilitating informed decision-making. Primary Value and User Solutions: Agave® Analytics Suite empowers organizations to make real-time, data-driven decisions regarding their energy consumption. By bridging the gap between operations and finance, it enhances accountability and asset management. The suite's comprehensive tools enable users to reduce energy costs, improve operational resilience, and achieve sustainability goals. Its integration of real-time data analytics with on-site control systems offers a holistic approach to enterprise-wide energy management, catering to various industries such as agriculture, food processing, manufacturing, hospitality, and healthcare.

Who Is the Company Behind Agave Analytics?

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Agentic AI for Information Retrieval

VividCloud's 'Agentic AI for Information Retrieval' is an advanced ReAct AI platform designed to enhance information access within organizations. By integrating conversational interfaces with real-time data retrieval, it enables users to interact naturally and obtain contextually accurate responses, making it ideal for knowledge-driven environments. Fully deployed on AWS, the solution leverages key AWS services to ensure scalability, security, and automation. Key Features and Functionality: - Agent-Based Data Collectors: Utilizes ReAct Agents that autonomously interact with SQL databases, combining reasoning and action to manage database operations effectively. - Real-Time Knowledge Retrieval: Employs Retrieval-Augmented Generation (RAG) to fetch relevant information from knowledge bases during conversations. - Conversational Interactions: Allows users to pose natural language queries and receive precise answers enriched with corporate knowledge. - Advanced Metadata Filtering: Tags and filters data based on metadata, ensuring retrieval of the most pertinent information. - History-Aware Retriever: Considers conversational history to provide context-aware responses, enhancing user experience. - Customizable Information Retrieval: Offers flexibility in configuring document ingestion, retrieval parameters, and role-based access controls to meet specific organizational needs. - Customizable UI: Provides a chat client or preferred user interface that can be tailored to reflect the organization's brand. - Cost Control: Incorporates token, user, and role-based limitations to manage API usage and prevent excessive costs. - Fully Automated Deployment: Uses Infrastructure as Code (IaC) for easy replication, deployment, and management, reducing manual configuration errors. - User Authentication and Access Control: Integrates with AWS Cognito and corporate Single Sign-On (SSO) systems for secure and seamless user authentication. - Data Protection: Ensures secure storage of sensitive data, such as conversation histories and document embeddings, with encryption at rest and in transit. Primary Value and Problem Solved: The 'Agentic AI for Information Retrieval' solution streamlines access to vast datasets, including databases and document repositories, by providing a conversational AI interface. This enhances user experience and accelerates decision-making processes. By integrating advanced AI capabilities with real-time data retrieval, it addresses the challenge of efficiently accessing and utilizing large volumes of information, thereby improving productivity and operational efficiency within organizations.

Who Is the Company Behind Agentic AI for Information Retrieval?

AIDA Healthcare

AIDA is a great solution for managing complex referral workflows with post-acute providers and highly technical parameters. AIDA enables referral workflow centralization, file-less (API-driven) referral packet distribution, industry-leading payer contract verification, automated regulatory compliance, deep customization for internal policy compliance, and comprehensive network traffic analytics. These tools are incredibly powerful for hospitals, ACOs, and Continuing Care Networks that need to understand where their referral traffic is going and need to put controls in place to ensure that patient choice is upheld, and preferred providers are appropriately utilized.

Who Is the Company Behind AIDA Healthcare?

AI Powered Secured CX-as-a-Self Service (CXaSS) - Subscription

Seqoria offers industry first, cloud-controller Secured Customer Experience-as-a-Self-Service (CXaSS) within Data, IP Communications, Cloud connected Collaboration and Contact Center.Seqoria offers industry first, cloud-controller based Secured Customer Experience-as-a-Self-Service (CXaSS) model throughout customer journey lifecycle while ensuring Always-on Security and Continuous Compliance. This unique approach helps Data, IP Communications, Cloud connected Collaboration and Contact Center.

Who Is the Company Behind AI Powered Secured CX-as-a-Self Service (CXaSS) - Subscription?

AI Powered Secured CX-as-a-Self Service (CXaSS) - Transactional

Seqoria offers industry first, cloud-controller based Secured Customer Experience-as-a-Self-Service (CXaSS) model throughout customer journey lifecycle while ensuring Always-on Security and Continuous Compliance. This unique approach helps Data, IP Communications, Cloud connected Collaboration and Contact Center.

Who Is the Company Behind AI Powered Secured CX-as-a-Self Service (CXaSS) - Transactional?

AirgapAI Chat

AirgapAI Chat is a fully local and secure AI chat platform designed to operate entirely on-device, ensuring that all data processing and storage remain within the user's environment without any external connections. This approach addresses critical security and compliance concerns by eliminating the risks associated with cloud-based AI solutions. AirgapAI Chat is engineered for straightforward deployment, installing as a single executable file, and comes pre-loaded with over 2,800 quick-start workflows tailored for various departments and industries. These workflows encompass tasks such as drafting legal briefs, generating intelligence reports, creating marketing campaigns, and analyzing supply chain data. The platform offers customizable personas and retrieval-augmented generation (RAG) capabilities with source citations, enhancing the relevance and accuracy of AI-generated content. AirgapAI Chat supports multiple AI models and is fully capable of offline operation, providing flexibility and reliability in diverse environments. With a one-time purchase model and no recurring subscription fees, it offers a cost-effective solution for organizations seeking to leverage AI technology without compromising data security or incurring ongoing costs. Key Features and Functionality: - Fully Local Operation: All AI processing occurs on the user's device, ensuring data remains secure and private. - Extensive Workflow Library: Over 2,800 pre-configured workflows cater to a wide range of tasks across different industries and departments. - Customizable Personas and RAG: Users can tailor AI interactions with customizable personas and benefit from retrieval-augmented generation with source citations for enhanced content accuracy. - Multi-Model Support: The platform is compatible with various AI models, offering flexibility to meet specific user needs. - Offline Capability: AirgapAI Chat operates without the need for an internet connection, making it suitable for environments with strict security protocols. - One-Time Purchase: A single payment grants perpetual access to the platform, eliminating the need for ongoing subscription fees. Primary Value and User Solutions: AirgapAI Chat primarily addresses the need for secure, efficient, and cost-effective AI solutions within organizations. By operating entirely on-device, it ensures that sensitive data remains within the user's control, mitigating risks associated with data breaches and compliance violations inherent in cloud-based AI services. The extensive library of pre-built workflows enables users to quickly implement AI-driven processes, enhancing productivity across various functions without the need for extensive customization. The platform's customizable personas and RAG capabilities allow for tailored AI interactions, improving the relevance and accuracy of outputs. Its offline functionality ensures uninterrupted operation in environments with limited or no internet access, making it ideal for sectors with stringent security requirements. The one-time purchase model offers a cost-effective alternative to subscription-based services, providing organizations with a predictable and manageable investment in AI technology.

Who Is the Company Behind AirgapAI Chat?

Airline Tweets Sentiment Analyzer

This solution classifies tweets mentioning airline travel into positive, neutral and negative sentiments. It uses text analysis, natural language processing, machine learning techniques to predict sentiment classes for tweets. It automates the manual effort to analyze airline travel related tweets and helps generate faster actionable insights around airline services.

Who Is the Company Behind Airline Tweets Sentiment Analyzer?

  • Seller: Mphasis
  • Year Founded: 2007
  • HQ Location: Reston, VA
  • LinkedIn® Page: www.linkedin.com
    13 employees on LinkedIn®

AISE PyTorch 0.4 Python 3.6 CPU Notebook

The AISE PyTorch 0.4 Python 3.6 CPU Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and data science applications. It combines PyTorch 0.4, an open-source machine learning library, with Python 3.6 and Jupyter Notebook, a browser-based interactive platform for programming and data analysis. Optimized for CPU performance, this environment facilitates efficient development and execution of machine learning models without the need for GPU resources. Key Features and Functionality: - Integrated Software Stack: Includes PyTorch 0.4, Python 3.6, and Jupyter Notebook, providing a cohesive environment for machine learning tasks. - CPU Optimization: Tailored for high-performance execution on CPU architectures, ensuring efficient training and inference without GPU dependency. - Development Tools: Equipped with essential development tools such as a C compiler and build utilities, supporting comprehensive program development. - Stability and Support: Offers a stable, production-ready environment with long-term support and regular updates to maintain reliability. Primary Value and User Solutions: This notebook environment addresses the need for a ready-to-use, CPU-optimized platform for machine learning practitioners and data scientists. By eliminating the complexities of manual setup and configuration, it enables users to focus on developing and deploying machine learning models efficiently. Its integration of key tools and libraries streamlines workflows, making it particularly beneficial for those without access to GPU resources or requiring a stable CPU-based solution.

Who Is the Company Behind AISE PyTorch 0.4 Python 3.6 CPU Notebook?

  • Seller: Jetware
  • Year Founded: 2017
  • HQ Location: Roma, IT
  • LinkedIn® Page: www.linkedin.com
    2 employees on LinkedIn®

AISE PyTorch 0.4 Python 3.6 CUDA 9.1 Notebook

The AISE PyTorch 0.4 Python 3.6 CUDA 9.1 Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and data science applications. It combines PyTorch 0.4, an open-source machine learning library, with Python 3.6 and Jupyter Notebook, providing a seamless platform for developing and deploying deep learning models. Optimized for NVIDIA GPUs, this environment leverages CUDA 9.1 to accelerate computations, making it ideal for both training and inference tasks. The stack also includes development tools such as a C compiler and make, facilitating a comprehensive development experience. Key Features and Functionality: - Integrated Environment: Combines PyTorch 0.4, Python 3.6, and Jupyter Notebook for a cohesive development experience. - GPU Optimization: Utilizes CUDA 9.1 to harness NVIDIA GPU capabilities, enhancing performance for intensive computations. - Development Tools: Includes essential tools like a C compiler and make for building and compiling code. - Versatile Deployment: Can be installed on various platforms, including Linux servers, virtual machines, Docker containers, and cloud instances. Primary Value and User Solutions: This product addresses the complexities of setting up a machine learning environment by offering a ready-to-use, optimized stack. Users can focus on developing and deploying models without the overhead of configuring software dependencies. The GPU optimization ensures faster execution of deep learning tasks, making it suitable for researchers, data scientists, and developers aiming to accelerate their machine learning workflows.

Who Is the Company Behind AISE PyTorch 0.4 Python 3.6 CUDA 9.1 Notebook?

  • Seller: Jetware
  • Year Founded: 2017
  • HQ Location: Roma, IT
  • LinkedIn® Page: www.linkedin.com
    2 employees on LinkedIn®

AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Notebook

The AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Notebook is a pre-configured, fully integrated runtime environment designed for deep learning applications. It combines TensorFlow 1.10, Python 2.7, and NVIDIA CUDA 9.2, providing a seamless platform for developing and deploying machine learning models. This environment is optimized for high-performance execution, enabling efficient training and inference processes. Key Features and Functionality: - TensorFlow 1.10 Integration: Leverages the capabilities of TensorFlow 1.10 for building and training machine learning models. - Python 2.7 Support: Ensures compatibility with legacy Python codebases and libraries. - CUDA 9.2 Optimization: Utilizes NVIDIA's CUDA 9.2 to accelerate computations on compatible GPUs, enhancing performance for complex models. - Pre-Configured Environment: Includes essential tools and libraries, reducing setup time and potential configuration issues. - Jupyter Notebook Integration: Provides an interactive interface for coding, visualization, and debugging, streamlining the development workflow. Primary Value and User Solutions: This notebook environment addresses the challenges of setting up and configuring deep learning frameworks by offering a ready-to-use platform. Users can focus on developing and refining their models without the overhead of environment setup. The integration of CUDA 9.2 ensures that computations are efficiently offloaded to GPUs, significantly reducing training times. This solution is particularly beneficial for data scientists and machine learning practitioners seeking a reliable and performance-optimized environment for their projects.

Who Is the Company Behind AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Notebook?

  • Seller: Jetware
  • Year Founded: 2017
  • HQ Location: Roma, IT
  • LinkedIn® Page: www.linkedin.com
    2 employees on LinkedIn®

AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Production

TensorFlow, an open source software library for machine learning, and Python, a high-level programming language for general-purpose programming

Who Is the Company Behind AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Production?

  • Seller: Jetware
  • Year Founded: 2017
  • HQ Location: Roma, IT
  • LinkedIn® Page: www.linkedin.com
    2 employees on LinkedIn®

AISE TensorFlow 1.10 Python 3.6 CUDA 9.2 Notebook

The AISE TensorFlow 1.10 Python 3.6 CUDA 9.2 Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and deep learning applications. It combines TensorFlow 1.10, Python 3.6, and CUDA 9.2, providing a robust platform for developing and deploying complex models. This environment is optimized for high-performance execution, ensuring efficient training and inference processes. Key Features and Functionality: - TensorFlow 1.10 Integration: Offers the capabilities of TensorFlow 1.10 for building and training machine learning models. - Python 3.6 Support: Utilizes Python 3.6, ensuring compatibility with a wide range of libraries and tools. - CUDA 9.2 Compatibility: Leverages CUDA 9.2 to accelerate computations on NVIDIA GPUs, enhancing performance for deep learning tasks. - Jupyter Notebook Interface: Includes Jupyter Notebook, providing an interactive environment for code development and visualization. - Pre-installed Libraries: Comes with essential libraries such as Keras and cuDNN, facilitating seamless development and deployment of neural networks. Primary Value and Problem Solved: This notebook environment addresses the challenges of setting up and configuring a deep learning workspace. By offering a ready-to-use platform with integrated tools and libraries, it allows data scientists and developers to focus on model development and experimentation without the overhead of environment setup. The inclusion of CUDA 9.2 ensures that users can fully exploit GPU acceleration, leading to faster training times and more efficient workflows.

Who Is the Company Behind AISE TensorFlow 1.10 Python 3.6 CUDA 9.2 Notebook?

  • Seller: Jetware
  • Year Founded: 2017
  • HQ Location: Roma, IT
  • LinkedIn® Page: www.linkedin.com
    2 employees on LinkedIn®
Neeraja Prakash
NP
Researched and written by Neeraja Prakash
Updated June 16, 2025