TLDR: Amazon Web Services (AWS) has announced a new integration between its generative AI assistant, Amazon Q Developer, and Jellyfish, an engineering management solution. This collaboration aims to provide engineering leaders with comprehensive, data-driven insights into how AI tools are influencing developer productivity, reducing “toil,” and optimizing software development workflows. The integration allows organizations to track AI adoption, measure its impact on development velocity, and calculate the return on investment from AI expenditures.
Amazon Web Services (AWS) has introduced a significant advancement in measuring the effectiveness of artificial intelligence in software development through a new integration between Amazon Q Developer and Jellyfish. This partnership addresses a long-standing challenge for modern software development teams: quantifying the impact of generative AI tools on productivity and business outcomes.
Modern development environments are characterized by increasing pressure to deliver high-quality code rapidly amidst growing system complexity. Developers frequently dedicate substantial time to “toil”—manual, repetitive, and undifferentiated tasks that offer limited enduring value. Generative AI tools like Amazon Q Developer are designed to automate or delegate such toil, thereby freeing developers to focus on more innovative and productive work. However, demonstrating this impact has historically been difficult.
Jellyfish, an established engineering management solution, plays a pivotal role in this integration by consolidating metrics from various development tools. When combined with Amazon Q Developer, Jellyfish enables organizations to analyze AI usage data alongside traditional engineering metrics. This holistic view helps engineering leaders understand how Amazon Q Developer influences development velocity, track AI adoption and usage patterns across their teams, and ultimately calculate the return on investment (ROI) from their AI spending.
The technical implementation involves Amazon Q Developer automatically capturing detailed usage data and prompt logs within the AWS environment. This data is then securely transferred to a designated Amazon Simple Storage Service (Amazon S3) bucket. Jellyfish subsequently accesses this information through pre-defined IAM roles, processing it in conjunction with data from other development tools to deliver comprehensive insights via its analytics system.
Key metrics surfaced by Jellyfish’s AI Impact Dashboard include engineering adoption rates of AI tools, the direct impact on development velocity, and the overall efficiency gains. This data-driven approach empowers organizations to make informed decisions regarding their AI investments, optimize developer workflows, and enhance overall engineering team efficiency.
Billy Robbins, Head of Partnerships at Jellyfish, highlighted the demand for this integration, stating, “At Jellyfish, our customers have been asking us for an Amazon Q Developer integration so they can understand the complete picture of how generative AI has transformed, improved and accelerated their software development workflows.” This sentiment underscores the industry’s need for clearer metrics on AI’s contribution to development.
Also Read:
- Qlik Earns AWS Generative AI Competency, Bolstering Enterprise AI Leadership and AWS Partnership
- AWS to Unveil Advanced Generative AI and Media Solutions at IBC 2025
This integration marks a significant evolution in developer productivity measurement. While the initial Amazon Q Developer Dashboard, launched in October 2023, offered basic visibility into subscription usage, code generation statistics, and security scans, and subsequent updates in May 2025 provided additional user-level insights, the Jellyfish integration offers a deeper, more connected understanding of how these metrics translate into tangible productivity and business outcomes. By providing powerful, actionable insights, this collaboration aims to help organizations optimize the impact of AI in their software development lifecycles.
