Looking for the best Jellyfish alternative? Start here.
TL;DR: If you’re a VP of Engineering, CTO, AI leader, or Head of Developer Productivity, DevEx, or Platform Engineering at an organization with several hundreds or thousands of engineers— Jellyfish will simply not be a sufficient solution. Faros may be a better option.
What is Jellyfish Software?
Jellyfish is a Software Engineering Intelligence Platform (aka Developer Productivity Insights Platform) built primarily for technical program managers at small to medium businesses. It centers around Jira data to help teams understand project allocation time for dev teams, track delivery predictability, and standardize metrics. For organizations with unified Jira processes and clean Jira hygiene, Jellyfish offers a straightforward solution with a fixed set of metrics that work well when Jira truly serves as your single source of truth.
When does Jellyfish fall short?
Jellyfish's Jira-centricity becomes a constraint as organizations scale and mature. In larger, more technically complex environments, the SDLC extends far beyond task tracking. Builds, tests, deployments, incidents, and collaboration patterns all generate critical signals that Jira simply doesn't capture. Jellyfish’s black-box metric calculations and fixed reporting can't adapt to the nuanced questions engineering leaders need answered.
One core issue is that Jellyfish's methodology requires perfect alignment between your Git activity and Jira tickets. As one engineering leader put it:
"Jellyfish methodology hinges on the linkage of your Git data to items of work, in our case Jira. We have 30% of PRs that are not linked to Jira tickets which means that by default they are not properly allocated to a 'Team'."
When nearly a third of your engineering work becomes invisible to your metrics, you're flying blind.
The platform's rigid organizational model further compounds the problem.
"There is no mapping from employee to team. In Jellyfish, a team is mapped to a Jira project/scrum via field config. It also forces a 3-layer Group > Division > Team hierarchy, which causes a lot of overhead."
Real engineering organizations are fluid; engineers shift between projects, contribute across teams, and don't fit neatly into three-layer hierarchies tied to Jira configuration.
If you’re a VP of Engineering, CTO, AI leader, or Head of Developer Productivity, DevEx, or Platform Engineering at an organization with hundreds or thousands of engineers—Jellyfish will simply not be a sufficient solution.
What do Jellyfish users complain about?
Across public Jellyfish reviews are countless mentions of rigid workflows, data inaccuracies, and non-actionable insights limiting the value for engineering leaders trying to achieve ambitious business goals. Below, you’ll find a compilation of recurring themes from real Jellyfish customers experiencing these limitations first-hand.
- Jellyfish cannot handle real-world workflows. This includes an inability to reflect how engineering teams actually operate. Initial setup for Jellyfish requires teams to adapt their processes to use the platform, since workflows cannot be tailored to specific needs. For large and complex enterprises with thousands of engineers, this is impossible. Plus, with workflows changing so much from AI, companies simply cannot have a solution that’s built around Jira’s human-curated data.
- Jellyfish’s AI impact solution is more marketing than substance. Whilst Jellyfish is rebranding for the AI era, their actual solution leaves a lot to be desired. Frustrated users note that Jellyfish does not track AI usage properly, and it does not have the capability to track AI power users over time.
"Jellyfish’s AI impact pieces leave a lot to be desired. What net impact does AI Copilot contribute? Those are all pieces that are not very well done in Jellyfish." — VP of Engineering
“We are so AI focused that most of the old-school players like Jellyfish don’t make the cut because they can't really. They're not there yet." — VP of Technology
- Jellyfish requires flawless Jira data hygiene. Jellyfish requires strict adherence to one established process. Any work that is not connected to a Jira ticket will not be tracked. Furthermore, Jira data in enterprises is often “messy” and statuses are not always up-to-date, so Jellyfish will generate misleading metrics—making their dashboards ineffective and unreliable for decision-making.
- Jellyfish lacks customization and flexibility. Jellyfish’s limited customization options do not satisfy enterprise needs and force teams to make significant changes in order to use the software. In Jellyfish, permissions are rigid, and users cannot apply custom filters nor fields to sort data according to their needs. Many Jellyfish users note that the software’s reports and dashboards would be more useful if it allowed for greater flexibility—and many still have to explore and analyze metrics manually.
“The problem I have with Jellyfish is the inability to fine-tune or remove outliers from the data. I'm having to manually clean up the data." — Director of Engineering Operations
- Jellyfish provides visibility, not answers or direction. While Jellyfish can surface what’s happening, their root-cause analysis is limited, which makes it hard to understand why an issue is occurring. Others report low confidence in its prescriptive and predictive capabilities, which can make it risky to rely on for business-outcome decisions like resource planning.
- Jellyfish can’t adapt to team structures. Due to Jellyfish’s Jira-bound approach, the software cannot be easily configured to reflect your teams’ actual structure—forcing users into an artificial 3-layer hierarchy that has to be manually maintained every time someone switches teams, which is too much overhead for any enterprise. One reviewer noted that their enablement team is organized in Jira by label-based boards, not by projects—and since Jellyfish doesn’t support grouping tickets or sprints by label, Jellyfish isn’t usable for that team today.
- Jellyfish users struggle to trust the numbers. Numerous Jellyfish users have reported concerning data discrepancy issues and difficulty validating the information coming from the software. This ongoing issue creates a lack of trust amongst users and upper management alike.
"Previously, I used Jellyfish and I could not stand it because it would make the most opaque decisions and their data is very opinionated. But they wouldn't tell you where those things were coming from." — Senior Engineering Manager of Infrastructure
- Jellyfish exhibits poor data freshness. A number of users say that Jellyfish could improve with real-time data freshness. Enterprise environments create hundreds of thousands to millions of builds per month, a scale that Jellyfish cannot handle. When syncing data is slow, it causes delays in metric monitoring.
- Jellyfish’s metrics do not meet expectations. Some users complain that Jellyfish lacks helpful out-of-the-box metrics and does not include several basic metrics they consider essential. Others wish they could create custom metrics and surveys that are actually relevant to their company.
"If there’s anything you want to change, you have to wait months and months for them to do it. For example, the surveys had very canned questions that didn’t apply to our teams. We weren’t able to change them ourselves, so we had to ask them to do it, and it took them several months to implement our requests.” — Senior Engineering Manager of Infrastructure
- Jellyfish requires lengthy ongoing support. Several users cite recurring bugs in Jellyfish, forcing managers to rely heavily on support. Troubleshooting is time-consuming because Jellyfish lacks self-service and built-in tools to identify data gaps, recommend fixes, or guide teams toward better data configuration.
- Jellyfish users often feel overwhelmed. Several users noted that the amount of data within Jellyfish is overwhelming. Moreover, users find the data to be hard to decipher and act upon—even after talking to a Jellyfish implementation consultant.
| Jellyfish limitations | Jellyfish user complaints |
| Cannot handle real-world workflows | Jellyfish doesn’t fit how teams actually work. Setup forces teams into Jellyfish’s rigid workflow, which is especially painful for complex enterprises. |
| AI impact solution is immature | Jellyfish struggles to consistently capture AI usage data. When it is captured, only recent data can be seen. There is no capability to track over time. Other advanced capabilities do not actually exist. |
| Requires flawless Jira data hygiene | Jellyfish is only accurate when everyone follows the same process, because it’s based on human-curated data. If that’s not your case, the metrics are wrong and dashboards can’t be trusted. |
| Lacks customization and flexibility | Dashboards and reports are rigid. Teams have to change how they work to match Jellyfish’s hardcoded logic, because Jellyfish won’t adapt to them. |
| Users struggle to trust the numbers | Data discrepancies and difficulty validating the numbers erode trust among users and leadership. Managers still have to calculate metrics in spreadsheets. |
| Provides visibility, not answers or direction | Jellyfish shows what happened but doesn’t explain why. Its recommendations and predictions aren’t trusted enough for high-stakes planning (like resourcing). |
| Inaccurate team/org structures | Jellyfish’s Jira-bound approach models teams and org structure incorrectly. That makes team-level reporting misleading or unusable. |
| Poor data freshness | Data updates lag. Teams can’t rely on Jellyfish for real-time tracking, especially with more complex enterprise integrations. |
| Metrics don’t meet expectations | The out-of-the-box metrics aren’t enough and several basic metrics are missing. Teams also can’t create the custom metrics they actually need. |
| Requires heavy ongoing support | Bugs and data issues push teams to support constantly. There aren’t built-in diagnostics to find gaps, explain issues, or guide fixes—so troubleshooting takes too long. |
| Users often feel overwhelmed | There’s too much data and not enough clarity on what to do with it. Even with implementation help, it’s hard to turn dashboards into action. |
Summary: Top complaints in Jellyfish software reviews Why Faros is the better choice for enterprise engineering organizations
Unlike Jellyfish, Faros is built for the complexity and scale of modern enterprise engineering. Where Jellyfish forces your organization to conform to its Jira-centric model, Faros meets you where you are—integrating with your entire SDLC to produce powerful insights without requiring teams to change how they work.
Faros measures engineering work everywhere it happens, not just in Jira
A key difference between Faros and Jellyfish is the data foundation used to represent engineering operations. Jellyfish treats Jira as the primary system of record, while Faros is designed to avoid relying on any single tool as the sole source of truth. Faros ingests data from 100+ sources—including Git, CI/CD, incident management, quality and testing tools, deployment platforms, and custom systems—to provide end-to-end visibility across the software development lifecycle. For example, Faros can integrate with GitLab, Bitbucket, and Datadog—tools that are not covered in Jellyfish’s integration set. Furthermore, whereas Jellyfish’s Jira-centric approach often undercounts or misrepresents engineering activities that aren’t tied to Jira issues, Faros ingests all engineering activity and is better positioned to capture what teams are actually doing across the full SDLC.
“Faros has tons of integrations and plenty of room for custom integrations. The key problem Faros is solving for us is unifying many data sources from various places and types into a single source for review.” — Engineering Manager
“Faros provides incredible flexibility to connect data sets we've never been able to correlate before. SDLC insights analysis, portfolio project + cost analysis, resource utilization, custom service level meta insights, organizational level data aggregation.” — Senior Manager, Engineering
“Extremely customizable to suit your needs and fantastic resources to aid setup. It is extremely helpful to see all your data in one place.” — Software Engineer
Faros does not require perfect data to generate accurate insights
Since Faros does not depend on flawless Jira hygiene to produce accurate metrics, the platform delivers value with the data you have today. It starts with clean, machine-generated signals from PRs, builds, and deployments, so you can establish baselines and identify hotspots even when ticket tracking is inconsistent. Faros handles data collection, normalization, and validation automatically, so imperfect data never blocks you from getting started. As Tulika Garg, Director of Developer Enablement & Ecosystem at Autodesk, puts it:
“There will always be data hygiene problems. But if you can get a smaller set of clean data into Faros—for us it was PR data and build data—you can get those initial benchmarks. Then you can move on to improving data hygiene because you’ve built a data mindset in your engineering teams.”
Faros generates transparent, trustworthy metrics
While Jellyfish users struggle with black-box calculations and data discrepancies that erode trust, Faros’ metrics are transparent, traceable, and auditable. This is by design: Faros users can trace each metric back to the underlying sources, see the definition, and understand the logic used to compute it, so teams can validate outputs instead of debating them. Since Faros normalizes and validates data on ingestion, it reduces inconsistencies across teams, tools, and workflows, so stakeholders can trust that everyone is looking at the same truth.
“For over a year, my team has been working to build organizational insights data that highlight gaps in processes and development practices using standard CI/CD DORA metrics combined with non-standard, internally generated metrics. The platform itself handles much of the logic and heavy lifting that would be an instant de-motivator for a lot of organizations.” — Senior Manager, Engineering
“As a centralized hub for tracking and analyzing engineering metrics, Faros has helped us align on common performance and operational goals and foster more collaboration across teams by sharing learnings, insights and best practices to optimize processes and drive business outcomes.” — Chief of Staff to CTO
Faros tells you what’s happening, why, and what to do next
Jellyfish tells you what’s happening from your Jira data. Faros goes further. In addition to explaining current states, Faros tells you what changed, uses root-cause analysis to explain what’s driving it, and then recommends concrete actions teams can take next. These insights and actionable recommendations are pushed to where work happens, with proactive notifications and automations that alert Slack or Teams, so issues can be addressed before they escalate. That proactivity, trust, and transparency make Faros dependable for high-stakes decisions like resourcing, forecasting, and planning.
“Faros provides easy access to valuable engineering insights, accelerating our metrics journey with their industry expertise and enabling us to take targeted actions for meaningful change.” — Engineering Manager
“The ability to customize metrics, reports and dashboards tailored to our specific use-cases has empowered product and engineering leaders to make data-driven decisions regarding resource allocation, cycle time and time to delivery.” — Chief of Staff to CTO
“Faros has helped us gain visibility into our engineering health. Especially since we can store our metrics in one place, we can easily connect the dots and analyze different aspects of engineering health. The options for visualizations, filters, and alert workflows are a huge plus. This makes it easy for us to automate reports, reducing the amount of manual work after the query has been built.” — Director of Engineering
Faros provides an industry-leading AI impact solution
Understanding the what, why, and what next is especially important in the AI era. Faros provides the industry's most comprehensive solution for understanding AI's impact on engineering operations and business outcomes. Faros helps engineering organizations measure and maximize the ROI of AI coding tools like Claude Code and Cursor, as well as agentic AI by establishing pre-AI baselines across velocity, code quality, security, and developer satisfaction, then tracking changes with precision analytics that filter out noise and confounding factors. The platform includes pilot-to-scale frameworks with enablement tracking and executive dashboards to ensure AI adoption sticks and scales effectively. Faros continuously translates productivity gains into ROI, tying license costs directly to measurable impact on throughput, quality, rework rates, and engineering costs. This enables AI leaders to make confident, data-driven decisions about where to invest in AI across their engineering organization—moving beyond gut feel to verifiable economic returns.
“Faros is helping with understanding AI's impact on our development practices, accelerating our AI adoption in the SDLC and making us more proficient in leveraging its capabilities.” — Engineering Manager
“Faros makes it easy to see everything about our SDLC and operational effectiveness in one place. We can easily correlate qualitative feedback from developer surveys to quantitative data about how they are working (pulled automatically from code repositories and ticketing systems). Faros has also helped us evaluate tool adoption (like Copilot) and measure impact across product teams.” — Senior Director, Computer Software
Faros is built for how large engineering teams actually work
Most engineering organizations do not fit into rigid, three-layer hierarchies tied to Jira projects. Faros is designed for the reality that teams are structured differently across companies, and that engineers regularly move between projects, contribute across teams, and collaborate fluidly. Its flexible org model maps work to the right teams and stays accurate as org structures evolve. This avoids forcing teams into a predefined configuration or duplicating structures just to make the tool work.
“Faros helps us centralize metrics across a large tech stack so we can more effectively understand how we build software. With a large product portfolio and global engineering team, Faros helps us scale our business by keeping up with our accelerating pace of delivery.” — Senior Director, Computer Software
“I have tried several team management tools but Faros has changed everything. It has helped me gather and analyse all operational data in one place, measuring productivity and finding areas for improvement. Creating report dashboards through Faros.ai is their best part.” — Team Lead
Faros is built for the enterprise, committed to your outcomes
Faros is designed for complex enterprises with hundreds or thousands of engineers. The platform easily handles massive data volumes—800,000 builds per month, 11,000 repositories—without performance degradation. The platform is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, with deployment options (SaaS, hybrid, on-prem) that meet strict enterprise security and compliance requirements. And unlike Jellyfish's canned metrics and slow support, Faros lets you create custom metrics, dashboards, and automations that adapt to your organization's unique needs. When priorities shift or new questions emerge, you can be certain that the Faros team will be there to support you.
“Technology aside, Faros the team has been a great partner from day one, helping us to onboard and working through our questions with direct communication, and handling issues as they arise, being remarkably quick with turnarounds when a technical problem needs to be resolved.” — Senior Manager, Engineering
“Another note is their partnership: since the beginning, the team has taken the initiative to understand what we need, making the setup process simple. They are really on top of requests and responsive when it comes to providing guidance and recommendations.” — Director of Engineering
“Support from the Faros team has also been exceptional and we are continuing to organically increase engagement across our globally distributed engineering team.” — CoS to CTO
“Along with the service itself the support team is incredibly helpful.” — Software Engineer
Faros: The top Jellyfish alternative in 2026 for large engineering teams
If your engineering organization has outgrown Jellyfish's Jira-centric limitations, Faros is a better option. See how leading engineering teams use Faros to get trustworthy metrics, real root-cause analysis, and proven AI impact measurement—without overhauling how their teams work. Book a demo to see Faros in action.
