Sleuth vs. LinearB vs. Jellyfish

If you're searching for a way to level up your developer team's productivity and experience, measure performance and progress, or align engineering work with business goals, then you've probably come across the term Software Engineering Intelligence (SEI), or simply Engineering Intelligence. You can thank Gartner for using that term.

In this guide, we’ll be comparing three leading EI tools: Sleuth, Jellyfish, and LinearB.

What is Engineering Intelligence?

Think of Engineering Intelligence like Business Intelligence, but laser-focused on software development. These tools give you visibility into everything from delivery status to team morale.

By pulling data from platforms like GitHub, Jira, and PagerDuty, EI tools help you monitor delivery performance, pinpoint bottlenecks, and make smarter resource allocation decisions.

Evaluating EI tools

We begin by defining the comparison criteria, divided into four categories:

Delivery Progress

Delivery Progress is all about the status and progress of currently ongoing Engineering work, using data mainly from issue trackers and version control systems.

We grade EI tools on Delivery Progress on their ability to answer questions like: How’s your team doing with current projects? Are releases on track?

Execution Quality

While Delivery Progress tells us how work is going at present, Execution Quality tells us how well we're doing the work - i.e. how efficient the team has been in the software development process.

With Execution Quality, we dig into how well the work is being done. Are PRs sitting idle too long? Is review time creeping up? This category is all about identifying inefficiencies and finding ways to improve the dev experience.

Alignment

We grade EI tools on Alignment based on how well they track the big-picture view: Are we working on the right priorities? Are resources being spent wisely? What's the impact of the work in terms of goals and outcomes?

Tools that help track goals, initiatives, and ROI stand out here.

Ease of Adoption

In this day and age, any SaaS tool worth its salt shouldn’t require days or weeks in man-hours to "install" anyway.

Adoption goes beyond easy setup—it’s about usability and trust. Great tools make it simple to verify data and build habits around data-driven decisions.

Examples include features that improve data hygiene, and features that instill habits of using data for decision making.

Comparing Sleuth, Jellyfish, LinearB

Let's compare the top three EI tools in the market using the categories described above.

Sleuth vs. LinearB vs. Jellyfish on Delivery Progress

Feature	Sleuth	LinearB	Jellyfish
Projects overview	✅	✅	✅
Project planned vs. completion	✅	✅	✅
Pull Requests by status	✅	✅	✅
Issue Tickets by status	✅	✅	✅
PRs or Issues by project, epic, initiative	✅	✅	✅
Incidents by status, severity	✅	🟧 Yes if incidents are tracked via issue tickets	✅
Bugs by status, severity	✅	🟧 Yes if bugs are tracked via issue tickets	✅
Escalations by status, severity	✅	🟧 Yes if escalations are tracked via issue tickets	✅
Story points-based view	❌	✅	❌

Sleuth, LinearB, and Jellyfish are pretty similarly matched in terms of the ability to report on Delivery Progress, the bread-and-butter of tools in this space.

Because information about planned and current Engineering work is captured in the form of issue tickets and pull requests, all three tools have extensive features to slice and dice such data by status, project, severity, and the like.

Sleuth vs. LinearB vs. Jellyfish on Execution Quality

Feature	Sleuth	LinearB	Jellyfish
PR open time	✅	✅	✅
PR coding time	✅	✅	🟧 Yes but only via issue ticket status
PR review lag time	✅	✅	🟧 Yes but only via issue ticket status
PR review time	✅	✅	🟧 Yes but only via issue ticket status
PR deployment time	✅	🟧 Yes if using GitHub tags as proxy for deploys	🟧 Yes but only via issue ticket status
PR batch size	✅	🟧 Yes but sizing is based on LOC only	✅
PR maturity score	❌	✅	❌
PR review depth	❌	✅	❌
Active vs. done branches	❌	✅	❌
Rework rate	❌	✅	❌
Scope creep	❌	✅	❌

Execution Quality rates tools on their ability to provide deeper visibility into the development work, which practically revolves around pull requests.

For example, PR Batch size indicates how big of a code change the PR is, and that's important because the bigger the chance, the bigger the surface for bugs and risk of failure. The simple way to measure batch size is by looking at the change in the number of lines of code (LOC).

All three tools provide such metrics around PRs, but LinearB also attempts to measure things like PR maturity, review depth, and rework, but those metrics might need interpretation so your mileage may vary.

Sleuth vs. LinearB vs. Jellyfish on Alignment

Alignment is a management concern, so invariably metrics in the category are focused on resources (people), how they are allocated, how they translate to dollars, and their morale and impact on the business.

Feature	Sleuth	LinearB	Jellyfish
Work by type (e.g. new features, bug fixing)	✅	✅	✅
Work by category (e.g. New, KTLO, Security)	✅	✅	✅
Work by planned vs. unplanned	✅	✅	✅
Resource allocation by headcount	✅	✅	✅
Projected investment effort	❌	❌	✅
Expense categorization (CapEx vs. OpEx)	❌	❌	✅
Survey-based DevEx scorecard	✅	❌	✅
Goals tracking	✅	✅	✅
Impact of initiatives on metrics	✅	❌	✅
Headcount and developer effort in dollar terms	✅	❌	✅
Developer effort in FTE terms	✅	✅	❌

Jellyfish and Sleuth lead this category. LinearB lacks the ability to measure team morale or impact, which is an important input in driving alignment at all levels of management.

Jellyfish offers a specific feature geared for Finance or Accounting use: categorization of R&D expenses, but if you don’t need that, Sleuth would be sufficient.

Sleuth vs. LinearB vs. Jellyfish on Ease of Adoption

Feature	Sleuth	LinearB	Jellyfish
"View source" to verify data	✅	✅	❌
Automate PR hygiene	✅	✅	❌
Automate Issue hygiene	✅	✅	❌
Automate report summary writing	✅	❌	❌
Automate scorecard generation	✅	❌	❌
Auto-surface insights on outliers	✅	❌	❌
Auto-surface insights on bottlenecks	✅	❌	❌
Auto-generate reviews (e.g. Weekly Planning)	✅	❌	❌
View and compare teams	✅	✅	✅
Compare to industry benchmarks	✅	🟧 Yes but not in-app	✅
View individual-level data	🟧 Individual data can be explored via slice and dice, but no individual-view dashboard	✅	✅
Custom integration via webhook	✅	❌	❌
Enterprise ready features (e.g. SAML, SSO)	✅	✅	✅

One important driver of Ease of Adoption is data trustworthiness. Features such as “view source” to peek at the data behind a metric or chart, and those that improve data hygiene - such as the ability to auto-notify codeowners of PRs without issue keys - help build trust.

Another important driver is manual burden. Time spent doing monitoring and reporting is time not spent on development or alignment. Features that automate manual things like writing report summary, generating scores, and surfacing outliers in the data can help here.

Finally, habit-forming features are critical for building a data-driven engineering culture. Features such as auto-creation of pre-populated templates for review meetings, such as Monthly CTO Reviews or Weekly Sprint Planning,

Sleuth leads this category given its coverage of features that drive Ease of Adoption.

Conclusion

We’ve just evaluated the top three tools for Engineering Intelligence through four categories: Delivery Progress, Execution Quality, Alignment, and Ease of Adoption. Here's where we end up:

	Delivery Progress	Execution Quality	Alignment	Ease of Adoption
Sleuth	A	B+	A	A+
LinearB	A	A+	B	B+
Jellyfish	A	B	A+	B-

Sleuth, Jellyfish, and LinearB are pretty much even on Delivery Progress.

While LinearB has invested in advanced metrics for Execution Quality, Jellyfish has invested more in the alignment between Engineering and Finance.

Sleuth is deep enough in both Execution Quality and Alignment categories, but Sleuth has invested the most in Ease of Adoption.

Among all four categories, Ease of Adoption is arguably the most important - the success of your productivity initiative or metrics program ultimately depends on how much value the teams are getting from using the tool.

Hopefully this article has given you the head start on your research for the best tools for the job, and the confidence to engage with vendors in this space.

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If you’d like to learn more about Sleuth, you can request a demo here.