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 generated from product descriptions
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
Riya K.
Jellyfish Delivers Actionable Insights into PR Cycle Time and Team Capacity
Reviewed on Jul 08, 2026
Review provided by G2
What do you like best about the product?
Jellyfish provides insights into my PR cycle time and issue cycle time, while also allowing me to track my quarter-over-quarter growth. This helps me monitor my performance metrics effectively and present data-backed progress.The data is actually useful day-to-day, I can see where my time is going, how much is planned work vs interruptions, and where PRs are getting stuck in review. Found out 40% of my team's capacity was getting eaten by unplanned work. That's not something you notice until you see it in a chart.
What do you dislike about the product?
The dashboard is slow, and UI can be a bit better. Default benchmarks are calibrated for average eng teams — if you move fast, your metrics look "unhealthy" out of the box which is misleading.
What problems is the product solving and how is that benefiting you?
Sprint planning used to be based on gut feel and optimism. Now I have actual cycle time data, PR review lag, and capacity breakdown to reference. Our cycle time dropped from 4.5 days to 2.8 days once we identified review lag as the bottleneck and fixed it. Conversations with my manager about workload are easier too, I don't have to explain why something took long, the data tells the story.
Information Technology and Services
Jellyfish Makes Cycle Time and PR Metrics Easy to Track
Reviewed on Jun 21, 2026
Review provided by G2
What do you like best about the product?
Jellyfish helps me track my Issue Cycle Time, PR Time, Comments on each PR, and track where my bandwidth is going as I work in cross-functional teams. Using the metrics, it helps me get down the numbers for the next quarter and improve on it.
What do you dislike about the product?
To be honest, I feel the User experience can be improved a bit as sometimes it's a bit laggy too , rest they are doing a great job.
What problems is the product solving and how is that benefiting you?
As an engineer, I use Issue Cycle Time and PR Cycle Time to see where things get stuck, whether it’s long-running tickets, slow reviews, or PRs sitting idle, so the team can fix process issues instead of guessing.
It also helps catch unlinked pull requests and review activity through PR comments, which makes it easier to ensure work is tied to the right tickets and nothing slips through the cracks.
It also helps catch unlinked pull requests and review activity through PR comments, which makes it easier to ensure work is tied to the right tickets and nothing slips through the cracks.
Information Technology and Services
Data-driven insights into your development cycle
Reviewed on Jun 20, 2026
Review provided by G2
What do you like best about the product?
The real value for me as a PM is the visibility into our engineering workflow. I can track Issue Cycle Time, PR Cycle Time, unlinked pull requests, and PR comments, all connected to Jira so there's no data silos. It shows exactly where the team is spending their time and where bottlenecks exist. No more guesswork on timelines.
What do you dislike about the product?
The UI could be cleaner, sometimes it takes extra clicks to get insights I need. Minor friction points, but they add up when you're iterating quickly. Rest the team is doing a good job.
What problems is the product solving and how is that benefiting you?
Before Jellyfish, I had no visibility into development cycle times or where the team actually spent their time. Now I can see blockers in real-time, unlinked PRs, slow issue resolution, approval delays, all connected to Jira. The team can iterate faster because bottlenecks are visible, not hidden. Bottom line: better planning, faster shipping, fewer surprises.
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.