List of the Top 3 Application Performance Monitoring (APM) Software for AWS Auto Scaling in 2025

Reviews and comparisons of the top Application Performance Monitoring (APM) software with an AWS Auto Scaling integration


Below is a list of Application Performance Monitoring (APM) software that integrates with AWS Auto Scaling. Use the filters above to refine your search for Application Performance Monitoring (APM) software that is compatible with AWS Auto Scaling. The list below displays Application Performance Monitoring (APM) software products that have a native integration with AWS Auto Scaling.
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    New Relic Reviews & Ratings

    New Relic

    New Relic

    Empowering engineers with real-time insights for innovation.
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    Company Website
    New Relic offers a top-tier Application Performance Monitoring solution that is relied upon by countless engineers around the globe. Designed for extensive operational environments, our platform offers immediate insights and trend analysis to enhance software performance, promoting reliability and outstanding user experiences. Equip your enterprise engineering team with New Relic's comprehensive platform to foster innovation and streamline processes in the ever-evolving software industry.
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    Amazon CloudWatch Reviews & Ratings

    Amazon CloudWatch

    Amazon

    Monitor, optimize, and enhance performance with integrated observability.
    Amazon CloudWatch acts as an all-encompassing platform for monitoring and observability, specifically designed for professionals like DevOps engineers, developers, site reliability engineers (SREs), and IT managers. This service provides users with essential data and actionable insights needed to manage applications, tackle performance discrepancies, improve resource utilization, and maintain a unified view of operational health. By collecting monitoring and operational data through logs, metrics, and events, CloudWatch delivers an integrated perspective on both AWS resources and applications, alongside services hosted on AWS and on-premises systems. It enables users to detect anomalies in their environments, set up alarms, visualize logs and metrics in tandem, automate responses, resolve issues, and gain insights that boost application performance. Furthermore, CloudWatch alarms consistently track metric values against set thresholds or those created by machine learning algorithms to effectively spot anomalies. With its extensive capabilities, CloudWatch is a crucial resource for ensuring optimal application performance and operational efficiency in ever-evolving environments, ultimately helping teams work more effectively and respond swiftly to issues as they arise.
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    WebLOAD Reviews & Ratings

    WebLOAD

    RadView Software

    "Elevate your performance testing with AI-driven scalability."
    RadView WebLOAD stands out as a premier performance and load testing solution powered by AI, designed for web, mobile, and packaged applications, supporting an extensive array of over 150 protocols and technologies, including popular front-end frameworks, APIs, message queues, and databases, thus facilitating comprehensive load testing for various enterprise technology stacks. Available as a SaaS option or for self-hosting in the cloud or on-premise, RadView WebLOAD.AI is remarkably scalable, capable of simulating hundreds of thousands of concurrent users from diverse locations and cloud environments. The platform excels at generating reliable tests with ease, and its robust AI-driven analytics empower performance teams to swiftly identify and address performance challenges. Additionally, with seamless integration into many widely-used Testing, CI/CD, and APM tools, along with a versatile API that allows for straightforward incorporation into any delivery pipeline, RadView WebLOAD is designed to be highly adaptable to varying development, testing, or operational frameworks. This adaptability is further enhanced by its flexible deployment options, making it a go-to choice for organizations seeking to optimize their performance testing processes.
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