Jellyfish Software Engineering Intelligence Platform

What do you like best about the product?

I like that it integrates with various data platforms (Jira, IncidentIO, Gitlab), makes sense of the data and combines the data well behind the scenes.

What do you dislike about the product?

Dashboard creation is very restricted. Even with latest custom metrics, there is still a long way to go.

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

One dashboard to rule them all. Gentle guidance towards industry metrics without being very prescriptive.

What do you like best about the product?

graphs and representations good way to see progress

What do you dislike about the product?

navigation sometimes confusing, but not overall its good

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

to track our daily progress, it helps improving skills

What do you like best about the product?

Insights on PR Cycle time, Issue cycles time, CapX, and Team performance in general are my favorite features I use on a regular basis. I love the MCP so I incorporate this data into my own reporting and presentations.

What do you dislike about the product?

I wish I had QA cycle aspects involved, since some of it is QA engineering and it would be nice to have KPIs here.

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

I want to get us to a place where we are comparable to industry standard benchmarks - this helps gauge where we need to go.

What do you like best about the product?

What I like best about Jellyfish is its ability to bring engineering data together in one place and turn it into meaningful, actionable insights. The visibility into engineering capacity, investment allocation, delivery performance, and team productivity makes it easier for Product and Engineering leaders to make informed decisions. I also appreciate the ability to drill down from high-level executive metrics to team-level details, identify bottlenecks, and have data-driven conversations around continuous improvement and business outcomes.

What do you dislike about the product?

While Jellyfish provides valuable insights, there are opportunities to improve the user experience. Some dashboards and reports could be more intuitive and easier to customize, particularly when trying to get specific views for different leadership audiences. At times, interpreting metrics requires additional context to ensure they are not viewed in isolation. I would also like to see more flexibility in reporting, easier drill-down capabilities, and stronger integration with Product metrics to better connect engineering activity with customer and business outcomes.

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

Jellyfish helps solve the challenge of having engineering data spread across multiple tools without a consolidated view of team performance, capacity, investment, and delivery outcomes. It provides a centralized, data-driven view of how Engineering teams spend their time, where investments are going, how effectively work is flowing, and where potential bottlenecks exist.

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)