By Glue Team
LinearB, Jellyfish, and Swarmia measure fundamentally different things: LinearB tracks PR-level cycle time and developer workflow metrics, Jellyfish focuses on executive-level engineering investment allocation and portfolio reporting, and Swarmia measures developer experience and team-level engineering effectiveness. None of them analyze your actual codebase — they track Git activity without understanding code structure, architectural risk, or dependency relationships. Choose LinearB for delivery pipeline optimization, Jellyfish for board-level engineering ROI reporting, or Swarmia for developer experience and team health.
Your VP of Engineering just asked you to "pick a platform for engineering analytics." You've narrowed it down to three finalists: LinearB, Jellyfish, and Swarmia. They all look similar in demos. They all claim to improve developer productivity. They all integrate with GitHub and Jira.
But they measure fundamentally different things, and choosing the wrong one means you'll spend six months optimizing for a metric that doesn't matter to your organization.
LinearB is best for PR cycle time analytics and Git workflow benchmarking. Jellyfish is best for connecting engineering investment to business outcomes and reporting to the C-suite. Swarmia is best for team-level developer experience and workflow optimization. None of the three provides deep codebase understanding - architecture risk, dependency mapping, or code-level intelligence - which is where platforms like Glue fill the gap.
What LinearB Actually Does Well
LinearB built its reputation on one thing: making Git data actionable. Their dataset of 8.1 million pull requests gives them benchmarking power that smaller platforms can't match. If you want to know how your cycle time compares to similar organizations, LinearB has the most credible answer.
The platform tracks five stages of PR lifecycle - coding time, pickup time, review time, merge time, and deploy time. For each stage, you get benchmarks, trends, and alerts. The WorkerB bot automates PR routing and nudges reviewers, which genuinely reduces waiting time for many teams.
LinearB works best for organizations between 50 and 500 engineers who use GitHub and Jira. Setup takes about a week. Time-to-first-insight is fast because Git data is clean and structured.
Where LinearB falls short: It treats all code as equal. A PR that updates a README and a PR that rewrites your payment processing logic get the same weight in cycle time calculations. It doesn't know which services are critical, which code carries architectural risk, or whether faster cycle time actually means better outcomes. It optimizes for throughput without understanding what's being throughput.
What Jellyfish Actually Does Well
Jellyfish answers a question the other two don't try to: "Where does our engineering investment go?" It maps engineer time to business initiatives, product lines, and strategic priorities. If your CFO asks "what percentage of engineering capacity goes to infrastructure vs. new features?" Jellyfish gives you a defensible answer.
The platform connects engineering activity to financial planning, headcount allocation, and portfolio management. For VP and C-suite audiences, the dashboards speak business language. You see investment categories, not commit counts.
Jellyfish works best for large engineering organizations (100+ engineers) where leadership needs to justify engineering spend to the board. Setup takes 2-4 weeks because it requires mapping engineering work to business categories, which is inherently a human process.
Where Jellyfish falls short: It doesn't look at code. You learn that the billing team spent 40% of their capacity on unplanned work. You don't learn that the billing service has brittle dependencies on three upstream services that cause most of those interruptions. Jellyfish tells you where time goes but can't tell you why it goes there at the code level. For teams under 100 engineers, the overhead of categorizing work into business initiatives often isn't worth it.
What Swarmia Actually Does Well
Swarmia is the developer-friendly option. Instead of presenting data to executives, it shows engineers their own workflow patterns - how much uninterrupted coding time they get, how long PRs wait for review, where meetings fragment their day.
The working agreements feature is genuinely clever: teams set their own standards (e.g., "PRs should be reviewed within 4 hours") and Swarmia tracks adherence. This turns metrics into a team contract rather than a surveillance tool. Developers generally like using Swarmia because it validates their experience rather than monitoring their output.
Swarmia works best for GitHub-centric teams of 20-100 engineers that care about developer experience and want to reduce friction in their daily workflow. Setup is the fastest of the three - usually under a week.
Where Swarmia falls short: It knows that reviews take four days but not whether those reviews are blocking a critical feature or a low-priority refactor. Like LinearB, it operates at the workflow level without understanding the underlying codebase. And its business alignment features are minimal compared to Jellyfish - if your leadership needs investment-level reporting, Swarmia won't provide it.
The Comparison That Matters
| Dimension | LinearB | Jellyfish | Swarmia |
|---|---|---|---|
| Primary audience | Engineering managers | VP/C-suite | Engineering teams |
| Core strength | PR cycle time benchmarks | Investment allocation | Developer workflow |
| Data source | Git + Jira | Git + Jira + finance | Git + calendar + Slack |
| Best team size | 50-500 engineers | 100+ engineers | 20-100 engineers |
| Setup time | ~1 week | 2-4 weeks | < 1 week |
| Understands code architecture | No | No | No |
| Developer sentiment | Neutral | Negative (surveillance perception) | Positive |
| Pricing transparency | Moderate | Low (enterprise sales) | High |
| DORA metrics | Yes (strong) | Yes (basic) | Yes (moderate) |
When All Three Miss the Point
Here's the honest gap: LinearB, Jellyfish, and Swarmia all measure engineering activity - what teams did, how fast they did it, where they spent time. None of them measure engineering reality - the actual structure, health, and risk profile of the code being produced.
This matters because the most expensive engineering problems aren't visible in activity metrics. A team shipping PRs quickly might be accumulating technical debt that will cost ten times more to fix later. A service with low commit volume might be the critical dependency that causes outages when changed.
Codebase intelligence platforms like Glue approach this differently. Instead of measuring what engineers do, they measure what the code is - dependencies, change risk, ownership gaps, and architectural complexity. This is why we built Glue as an agentic product OS rather than another analytics dashboard: the question isn't "how fast is the team moving?" but "is the team building something that will hold up?"
If your primary need is cycle time benchmarking, start with LinearB. If you need to report engineering ROI to the board, Jellyfish. If you want to improve developer experience from the ground up, Swarmia. If you need to understand what's actually happening inside your codebase and automate the cross-tool workflows that slow your team down, explore what Glue does differently.
How to Run a Meaningful Evaluation
Don't buy based on demos. Every vendor's demo data looks clean. Here's a framework:
Week 1: Connect each platform to 30 days of real Git and Jira data. Ask each platform the same question: "Why did our last major feature ship two weeks late?"
Week 2: Show the results to three different stakeholders - a developer, a manager, and a VP. Ask each: "Does this tell you something you didn't already know?"
Week 3: Check the platform's answer against reality. Talk to the team that shipped the late feature. Does the platform's explanation match what actually happened? If the platform says "cycle time increased" but the real reason was an unclear spec or an architectural dependency, you have an activity metric masquerading as insight.
The platform that surfaces the root cause - not just the symptom - is the one worth investing in.
Frequently Asked Questions
Q: LinearB vs Jellyfish vs Swarmia — which should I choose?
Choose based on your primary audience and pain point. LinearB is best for engineering managers who need PR-level cycle time analytics, developer workflow optimization, and DORA metrics with industry benchmarks. Jellyfish is best for VPs and CTOs who need portfolio-level investment tracking and board-ready engineering ROI reports. Swarmia is best for teams focused on developer experience and engineering effectiveness with a developer-friendly interface. None analyze your actual codebase — for architecture insights, dependency analysis, and change risk scoring, you need a codebase intelligence platform like Glue.
Q: Can I use LinearB and Swarmia together?
LinearB focuses on manager-level cycle time analytics while Swarmia focuses on developer-level workflow patterns. Some teams run both for different audiences, though the overlap in PR analytics means you're paying twice for some of the same data. A more common pairing is Jellyfish (for executive reporting) plus either LinearB or Swarmia (for team-level insights).
Q: Which platform has the best DORA metrics support?
LinearB has the strongest DORA metrics implementation with the most granular data and industry benchmarks. Swarmia tracks DORA but with less depth. Jellyfish includes DORA as part of a broader reporting suite but doesn't specialize in it. If DORA is your primary framework, LinearB is the strongest choice.
Q: Is Jellyfish worth the price for smaller teams?
Generally no. Jellyfish's value proposition is portfolio-level investment tracking, which doesn't provide meaningful insight for teams under 50-100 engineers. The setup cost (mapping work to business categories) and the licensing cost are hard to justify when your entire engineering organization fits in one Slack channel. Swarmia or LinearB deliver faster value for smaller teams.
Q: Do any of these platforms understand my actual codebase?
No. All three analyze Git activity (commits, PRs, deployments) without understanding the code itself. They can tell you a file was changed but not whether that change introduced architectural risk or affected a critical dependency. For codebase-level intelligence - architecture mapping, dependency analysis, change risk scoring - you need a codebase intelligence platform like Glue or Cortex.