Sold by: Jellyfish
Deployed on AWS
Jellyfish is the leading intelligence platform for AI-Integrated engineering, helping more than 700 companies including DraftKings, Keller Williams and Blue Yonder, leverage AI to transform how they build software. By combining the deepest engineering dataset with context-rich intelligence, Jellyfish helps R&D organizations understand what is driving impact, adopt proven industry best practices, and make smarter decisions across AI adoption, planning, delivery, and engineering performance. Learn more at jellyfish.co
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AWS Private Offers available. Contact us at aws@jellyfish.co to get started.
Jellyfish is the Software Engineering Intelligence Platform that answers the question of whether AI investments are delivering the expected business impact, connecting AI tool usage to real engineering outcomes so R&D leaders can see what's actually moving the needle and make confident decisions about where to invest next. More than 700 companies, including DraftKings, Keller Williams, and Blue Yonder, use Jellyfish to align engineering work with business priorities and ship higher-impact software predictably.
Jellyfish passively ingests signals from across your SDLC and AI tooling, source control, issue trackers, CI/CD, cloud infrastructure, and AI coding assistants, and turns them into a coherent intelligence layer for engineering leadership. You get purpose-built AI Impact dashboards that track adoption and utilization of tools like AWS Kiro and Cursor, measure AI-assisted pull requests, and connect before/after productivity changes to real outcomes: cycle time, deployment frequency, defect rates, and delivery predictability.
This works across software engineering, DevOps, SRE, and infrastructure teams moving beyond ticket velocity to show where engineering effort is going, how work is flowing, and which AI investments are paying off. Engineering leaders get the data to prove ROI to finance and the board. Platform and DevEx teams get the evidence to rationalize tool spend, benchmark impact across cohorts, and make the case for what to double down on, or cut.
Beyond AI impact, Jellyfish gives R&D leaders a unified view of investment allocation, delivery health, and developer experience across every team and product area so that engineering and business leadership share a single, data-backed picture of performance and risk.
Built entirely on AWS and delivered as a multi-tenant SaaS, Jellyfish leverages AWS AI services including Amazon Bedrock (for chat and agentic experiences) and Amazon SageMaker (for intelligent work classification), alongside core AWS infrastructure services, to provide secure, scalable analytics for modern engineering organizations. By tying usage and spend on AWS Kiro, Bedrock-powered assistants, and other AI tools to engineering productivity, quality, and business outcomes, Jellyfish gives AWS customers the data they need to prove ROI and deliver stronger business outcomes from their AI and cloud investments.
Highlights
- Measure how AI affects productivity, speed, and quality to invest confidently in what works
- Combine system data and developer feedback to spot bottlenecks and improve focus, flow, and team performance
- Build a consistent metrics strategy grounded in DORA and SPACE to benchmark trends and report outcomes
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Dimension | Description | Cost/12 months |
|---|---|---|
Jellyfish AI Engineering Intelligence Platform | Access to full Jellyfish Platform + 15 Engineering User Seats. Contact us for custom configuration and pricing at aws@jellyfish.co | $30,800.00 |
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Vendor support
Jellyfish provides support via the Jellyfish Help Center (https://help.jellyfish.co ), where designated administrators can submit requests. Our support team assists with configuration, integrations, data quality, troubleshooting, and product best practices throughout your subscription. For all Jellyfish users, we also offer robust product documentation, in-app AI help chat, and Jellyfish Academy, an online learning portal for use cases and best practices.
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Sentiment is AI generated from actual customer reviews on AWS and G2
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AI Impact Measurement
Tracks adoption and utilization of AI tools, measures AI-assisted pull requests, and connects productivity changes to engineering outcomes including cycle time, deployment frequency, defect rates, and delivery predictability.
Multi-Source Data Integration
Passively ingests signals from source control, issue trackers, CI/CD pipelines, cloud infrastructure, and AI coding assistants to create a unified intelligence layer.
Engineering Performance Metrics
Implements DORA and SPACE framework-based metrics to benchmark engineering performance, measure delivery health, and track developer experience across teams and product areas.
AWS-Native Architecture
Built on AWS infrastructure utilizing Amazon Bedrock for chat and agentic experiences and Amazon SageMaker for intelligent work classification to provide secure and scalable analytics.
Investment Allocation Analytics
Provides unified visibility into R&D investment allocation, tool spend rationalization, and ROI analysis across engineering teams and AI tool investments.
Metrics Aggregation and Visualization
Aggregates metrics from multiple tools to identify and resolve workflow bottlenecks through intuitive dashboards providing contextual insights for team management.
Workflow Automation
Implements policy as code and automation mechanisms to streamline pull request processes and create efficient workflows across the organization.
Resource Cost Tracking
Visualizes team member costs and project delivery status to align resources with business priorities.
Engineering Operations Analytics
Translates R&D data into quantifiable business impact through software delivery management analytics.
Context-Rich Notifications
Delivers context-rich notifications to support active improvement of engineering operations and team performance.
Multi-Source Data Integration
Ingests engineering data from git-based SCM tools including GitLab and GitHub, build tools such as Jenkins and CircleCI, and portfolio management tools like Jira
Value Stream Metrics and Visualization
Transforms engineering data throughout the SDLC into meaningful metrics and visualizations to measure and visualize the flow of value
Work Completion Forecasting
Provides intelligent forecasting capabilities to predict work completion dates and enable more predictable release planning
Delivery Visibility and Monitoring
Delivers complete visibility into the state of deliverables and overall team performance across the engineering organization
Process Health Analytics
Measures and analyzes process health indicators to identify opportunities for continuous improvement and organizational change
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Customer reviews
Prayush J.
JellyFish Cleared Visibility Into Engineering Time
Reviewed on Jun 11, 2026
Review provided by G2
What do you like best about the product?
The best think about Jellyfish is that I can check Issue Cycle Time, PR Cycle Time, Unlinked Pull request, Comments on PR. Also it is connected with Jira, so we can check where the team has been spending most of their time. It helps to track each and everything very easily.
What do you dislike about the product?
To be honest, there is nothing I dislike about Jellyfish. Great work, great team.
What problems is the product solving and how is that benefiting you?
We struggled to see where engineering effort was going and whether it matched product priorities i.e, basically where time is spent. Jellyfish pulls everything Jira and GitHub data into one place, so we can check cycle times, PR turnaround, and team workload without manual checking on each.
Isaac G.
Intuitive Interface and Powerful Jira Integration for Organizational Insights
Reviewed on May 20, 2026
Review provided by G2
What do you like best about the product?
I really like the interface and how easy it is to navigate through the data. If you have your hierarchy set up and use Jira effectively, the integration lets you move up and down through the different levels of your organization and see how data from smaller units rolls up into the larger picture.
What do you dislike about the product?
There was a bit of a learning curve at first as I got used to the navigation, but once I understood where to find the information I needed, I could use favorites, bookmarks, and stars to quickly access the information I rely on regularly.
What problems is the product solving and how is that benefiting you?
Jellyfish provides visibility into our information in a way we couldn’t get anywhere else. In particular, it shows where developers are actually spending their time, not just where they plan to spend it. The signals Jellyfish captures give us a clearer view of reality than our other planning tools. It also enables capitalization tracking without forcing us to rely on time tracking across our work.
David A.
DevFinOps and DORA Metrics That Transformed Our Engineering Operations
Reviewed on May 08, 2026
Review provided by G2
What do you like best about the product?
The DevFinOps capabilities and DORA metrics have changed the way we operate as an engineering organization.
What do you dislike about the product?
I wish there was more in the way of ‘engaging’ users, driving them to the platform.
What problems is the product solving and how is that benefiting you?
Labor capitalization, DORA metrics, AI Impact, synthesizing Jira, Gitlab, and Cursor data.
Padma S.
Clear Visibility Enhances QA Performance Tracking
Reviewed on Apr 30, 2026
Review provided by G2
What do you like best about the product?
I like that Jellyfish provides clear visibility into my work, eliminating the need for manual tracking. The simple dashboards make it easy to understand performance trends quickly. It's incredibly useful for identifying delays in testing or review cycles, which helps me improve consistency and maintain accountability. The setup was straightforward, with smooth integration of tools like Jira and Git, and it was easy to start using. Switching to Jellyfish from more manual tracking methods made performance tracking more structured and data-driven. I appreciate how it gives me a clear view of my work and contributions while using it alongside other tools like Git, Jira, and CI/CD pipelines.
What do you dislike about the product?
The AI insights could be more detailed and tailored to QA needs, like providing insights on test coverage gaps, identifying flaky tests, and recurring defect patterns. It would also be beneficial if the AI could suggest clear actions on where to improve tests or highlight potential risks in upcoming releases.
What problems is the product solving and how is that benefiting you?
I use Jellyfish to track my QA performance with clear dashboards, helping me quickly identify delays in testing cycles. It improves consistency and accountability by providing visibility into my time usage and contributions.
Kamlesh K.
Team Pulse and Stats Make Retrospectives Clear and Actionable
Reviewed on Apr 29, 2026
Review provided by G2
What do you like best about the product?
You can look at Team Pulse, Team Summary and Individual statistics during Retrospective for what went well, didn't go well.
What do you dislike about the product?
AI Impact was not integrated successfully with Amazon Q.
What problems is the product solving and how is that benefiting you?
The biggest advantage is categorizing where the time is being spent by the developers, i.e. we have decided categories for each epic and defect.