Jellyfish Software Engineering Intelligence Platform

In my work, I mostly use Jellyfish to provide clear visibility into my work, eliminating the need for manual tracking. The simple dashboard has been able to make it easy for me to understand performance trends quickly. It is incredibly useful because it helps me identify delays in testing or review cycles, which helps me improve consistency and also maintain accountability. The setup was straightforward with smooth integration of tools such as Jira and GitHub, and it was easy to start using. Switching to Jellyfish from more manual tracking methods made the performance tracking more structured. I appreciate the contribution while using it alongside other tools such as GitHub, Jira, and CI/CD pipelines.

I use Jellyfish to track my QA performance with clear dashboards, helping me quickly identify delays in testing cycles. It has improved consistency and accountability by providing visibility into my time usage and contributions.

Jellyfish has positively impacted my organization by helping us gain visibility into our information in a way we could not 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, which eliminates the guesswork.

The best features Jellyfish offers include clear visibility, integration capabilities, and its intuitive interface.

Regarding integration and the intuitive interface, I love the ability to integrate data from tools such as Jira and GitHub, where it simplifies tracking and productivity and project allocation. The intuitive interface and the actionable insights help my team to identify bottlenecks and improve performance, and it also makes it very easy to use, even for non-technical users.

I love the visibility which Jellyfish provides, and I also love the dashboards, where they make it easy to identify delivery risk and understand where teams are spending their time and discuss trade-offs without manually consolidating information from multiple sources.

The biggest issue I have with Jellyfish is how it attributes work done by engineers as AI-assisted work. It is really making a guess based on whether there are AI signals the day that they do appear, which is not really a great determination in my opinion.

The dashboard provides strong visibility, but it can sometimes feel a bit complex or dense when trying to drill down quickly into QA-specific insights. More customizable or role-focused views would make it easier to surface what is most relevant without extra filtering.

I have been using Jellyfish for two years.

I did previously use a different solution, as I switched from LinearB because I needed a great platform that is very easy to use and also cost-effective, and a tool that is very easy to use for both the engineering and also the data team.

I have seen a return on investment with Jellyfish, as prior to it, I manually tracked my developers' time spent on epics each month. This included calculating the cost associated with each epic and determining what was capitalizable versus non-capitalizable. Jellyfish automatically calculates the cost for me by interfacing with Jira, computing the FTEs for each epic. This has saved me a lot of time, saved me at minimum a day during my close processes.

My experience with pricing, setup cost, and licensing for Jellyfish is manageable and quite fair.

Jellyfish has been very helpful, especially before I had to go to disparate systems. I had to go to disparate systems such as Jira, GitHub, and LinearB to get data to understand my engineering team's performance. Now I can go to Jellyfish to have those data and metrics aggregated under the platform to understand the team's and organization's performance.

Regarding Jellyfish's AI capabilities, I think its governance and security are handled in an accurate and very secure manner, as the AI insights built within the product take the heavy lifting of evaluating data of the user. I find the accuracy and reliability of output to be very accurate and reliable always.

I would rate Jellyfish ten out of ten.

Private Cloud

Amazon Web Services (AWS)

My main use case for Jellyfish revolves around the visibility it provides for my organization and how it supports better planning and leadership conversations. As I continue using Jellyfish, I have found its biggest value is how it brings engineering delivery, team allocation, capacity, and investment priorities into one place by connecting data from tools such as Jira and GitHub, which I have been using in my organization. The dashboard makes it very easy to identify delivery risks, understand where teams are spending their time, and discuss trade-offs without manually consolidating information from multiple sources. It is especially useful for creating a shared view between engineering, product, PMO, and the leadership team in my organization.

Jellyfish helps us by bridging the gap between the engineering teams and the business teams, which has aligned our technical work with the business goals strategically. It has been very helpful because it enables us to make data-driven decisions on estimations.

Jellyfish also provides visibility into our information in a way we could not get anywhere else, as 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 enables us capital tracking without forcing us to rely on time tracking of our work.

Jellyfish positively impacts my organization by enhancing team collaboration, providing visibility, and facilitating effective communication within engineering teams. I love it because we can track time and enhance team efficiency through the fantastic metrics in Jellyfish. It allows us to make data-driven decisions more easily. I appreciate it because Jellyfish helps identify issues that hinder effectiveness, calling for more flexible dashboards and improved data accuracy.

Jellyfish has been helpful, especially in my engineering team, in improving productivity by giving us better visibility into development work and helping us identify bottlenecks. I estimate that it has reduced time spent on manual reporting and progress tracking by around twenty percent, allowing my fellow engineers and managers to focus more on development priorities and deliver projects more efficiently.

The best features Jellyfish offers include fantastic metrics, enhanced team collaboration, robust metrics analysis, valuable insights, and an intuitive user interface. I love its very flexible dashboards.

The metrics analysis in Jellyfish facilitates effective tracking and optimization of team performance, which helps us. Valuable insights and an intuitive user interface enhance visibility and performance tracking in our engineering department. I find Jellyfish extremely easy to use and appreciate its intuitive user interface and effortless tracking features.

Jellyfish has been able to handle our data privacy and security very well; its governance and security are excellent.

Jellyfish's AI capabilities are generally reliable and provide useful insights into engineering productivity, resource allocation, and workflow efficiency.

I find the custom dashboarding challenging, although I acknowledge that the team is moving in the right direction.

Sometimes I need to engage support to fix data pulls from GitHub and Jira, but they resolve these issues very quickly.

More customizable or role-focused views will make it easier to surface what is most relevant without extra filtering.

I have been using Jellyfish for two years.

Jellyfish is very stable and reliable in our day-to-day use. The dashboard and engineering analytics work well for tracking productivity, project progress, and resource allocation, and we have experienced minimal disruptions; therefore, the platform provides consistent insights for decision-making.

The scalability of Jellyfish is top-notch because it has been able to scale well with our team's growing engineering activities and reporting needs.

Customer support has been very influential and helpful, as the team provided useful guidance during setup and configuration, and also helped address questions about integration and reporting very well. I would rate customer support nine out of ten.

We did not use another solution, as we did not switch from any.

Deploying Jellyfish is relatively easy with a straightforward setup process and useful integration with our existing engineering tools.

As IT personnel and software engineer, my experience with the configuration process was generally straightforward. Connecting Jellyfish to our existing development tools and setting up dashboards is manageable, and the platform provides useful visibility into our engineering metrics.

Jellyfish integrates reasonably well with our engineering tools and AWS-based environment, helping us gain better visibility into development activities and engineering performance.

Having Jellyfish in our organization has shown us more positive returns than negative; we have seen a positive return on investment mainly through improved engineering visibility and reduced manual reporting. We estimate that it saves around twenty percent of the time previously spent on reporting and progress tracking, allowing engineers and managers to focus more on development work, which has been very helpful in identifying bottlenecks and allocating resources more effectively.

The procurement process for Jellyfish was relatively straightforward. Jellyfish's billing experience is generally straightforward, and the pricing and licensing are manageable once the plan and contract terms are understood. My experience with pricing, setup cost, and licensing is that everything was very manageable, although the overall cost required careful evaluation against expected business value. The setup cost was reasonable, and the implementation did not require significant additional resources, hence everything was straightforward and fell into place within a short period.

We evaluated tools such as Linear B and GitLab Analytics before choosing Jellyfish.

I would rate Jellyfish nine out of ten or five out of five. Jellyfish has been really helpful, especially in my engineering department, by tracking where QA and automation work is going, identifying bottlenecks in the development workflow, and solving the lack of visibility between testing work and broader engineering priorities. I recommend Jellyfish to any organization looking to improve engineering visibility, productivity, and resource allocation. Before adopting it, make sure you identify your key performance metrics, ensure your development tools are properly integrated, and involve engineering managers in the setup process. Take time to customize your dashboards and review the insights regularly. Also, evaluate the pricing against your team's needs and expected return on investment to ensure you get the most value from the platform. My overall rating for Jellyfish is nine out of ten.

Amazon Web Services (AWS)

What do you like best about the product?

The AI insights built within the product takes the heavy lifting of evaluating the data off of the user. The UI is fairly straightforward with pleasing visuals throughout the product. Pulling data from our disparate systems and present a full picture across our engineering organization in one place is a big win. When I do have to deal with the support team, they have been prompt and helpful in resolving my issues.

What do you dislike about the product?

The biggest issue I have with Jellyfish is how it attributes work done by engineers as AI assisted work. It's really making a guess based on if there are AI signals the day that they do a PR which isn't really a great determination in my opinion. Not having the ability to designate static teams has also been a problem when we have teams that are support teams who's work spans multiple JIRA projects.

What problems is the product solving and how is that benefiting you?

Before I had to go to disparate systems such as JIRA, GitLab, Github, and Linear to get data to understand our engineering team's performance. Now I can go to Jellyfish to have those data and metrics aggregated under one platform to understand the teams and organization's performance.

What do you like best about the product?

Jellyfish helps me know where I stand in AI usage and lets me see the usage of my teammates as well. It's useful for understanding how much I depend on AI. I find all the features good, and the initial setup with SSO login was easy.

What do you dislike about the product?

Nothing

What problems is the product solving and how is that benefiting you?

I use Jellyfish to track AI usage, helping me understand my dependency and where I stand. It also lets me see my team's usage, which is useful.

What do you like best about the product?

the ability to quickly view my team's progress in metrics we are tracking.

What do you dislike about the product?

sometimes the interface can be clunky and dificult to navigate. I have to know where I am going to get what I need.

What problems is the product solving and how is that benefiting you?

it is giving me visibility into how my teams are performing

What do you like best about the product?

The effort-based allocation model is the standout. Jellyfish translates raw development activity into an FTE-equivalent split across investment categories (Growth, KTLO, Support) based on actual effort signals rather than ticket counts — which is critical, because counting issues badly misrepresents real work. This is what made my monthly CEO and board reporting credible: leadership could trust an "X FTE went to Growth this month" figure because it reflected effort, not just Jira volume. The mapping of investment categories to our own strategy taxonomy and the month-over-month trend views also let me show direction over time with minimal manual assembly, and the delivery and AI-assisted PR metrics fed our AI-adoption reporting well.

What do you dislike about the product?

The biggest frustration is the newer AI impact reports: there's no way to export the underlying data to CSV. The reports render fine in the UI, but if you need the numbers for your own analysis — cohort breakdowns, month-over-month trends, feeding an executive deck — you're stuck. You can look at the data but you can't take it with you, so getting it into a usable format means manual extraction rather than a simple download. For a metrics platform whose whole value is helping you report, locking the data inside the dashboard defeats the purpose.
On top of that, I've seen the per-tool detail figures and their aggregate rollups diverge by around 10 percentage points, which is a real problem when the aggregate is the number going in front of the board. Both issues are workable, but they add reconciliation and extraction steps to what should be a clean pull.

What problems is the product solving and how is that benefiting you?

It answers the core question my leadership asks every month: where is engineering effort actually going, and is it aligned to strategy? The FTE allocation across Growth/KTLO/Support gives me a defensible, repeatable way to report that, plus the delivery and AI-assisted-development metrics I need to show whether our tooling investment is moving velocity. The benefit is a monthly executive narrative grounded in effort data rather than anecdote or raw counts.

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.

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.

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.

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.