Jellyfish vs LinearB vs DX vs Swarmia - What We Learned Evaluating Engineering Intelligence Platforms

General
1

My team spent Q4 2025 evaluating engineering intelligence platforms. Here’s what we learned about the major players.

The Evaluation Process

We shortlisted six platforms: Jellyfish, LinearB, DX (GetDX), Swarmia, Faros AI, and Cortex. Each had a 30-day pilot with one engineering team.

Platform Comparison

Jellyfish

Best for: Enterprise organizations wanting to align engineering with business objectives

  • Virtual time cards and investment allocation tracking
  • Strong business-to-engineering translation
  • Capacity planning features
  • Higher price point, longer sales cycle
  • Best-in-class for “where is engineering time going?”

LinearB

Best for: Teams focused on workflow optimization and delivery acceleration

  • 8.1 million PRs analyzed in their benchmarks
  • WorkerB automation for repetitive tasks
  • Code review optimization features
  • Strong DORA metrics implementation
  • gitStream for automated PR routing

DX (GetDX)

Best for: Organizations prioritizing developer experience measurement

  • Created by the people behind DORA and SPACE frameworks
  • DX Core 4 framework and DXI (Developer Experience Index)
  • Each 1-point DXI improvement = 13 minutes saved/developer/week
  • Qualitative + quantitative measurement
  • Focus on actionable insights, not just dashboards

Swarmia

Best for: Teams wanting straightforward DORA and SPACE metrics

  • Clean, developer-friendly interface
  • Strong Slack integration
  • CI/CD insights
  • Good for teams starting their metrics journey
  • Lower complexity, faster time-to-value

Faros AI

Best for: Enterprise with complex tool ecosystems

  • Unified data model across 100+ integrations
  • AI adoption tracking (GitHub Copilot impact measurement)
  • Custom reporting and data warehouse integration
  • More technical implementation required

Cortex

Best for: Platform engineering and developer portal use cases

  • Service catalog + scorecards
  • Engineering standards enforcement
  • Self-service developer portal
  • Less pure “intelligence” platform, more platform engineering

What We Chose and Why

We went with LinearB + periodic DX surveys. Here’s the reasoning:

  1. LinearB gives us real-time workflow metrics and automation
  2. DX surveys fill the qualitative gap LinearB doesn’t cover
  3. Combined cost was lower than enterprise Jellyfish license
  4. Implementation complexity matched our team’s capacity

Curious what others have chosen and why.

2

Great comparison. From a data science perspective, the metric selection differences between platforms are significant.

Which metrics actually matter?

After analyzing productivity data across multiple teams, here’s what I’ve found:

  1. High signal metrics:

    • Cycle time (from first commit to production)
    • Review turnaround time (correlates strongly with throughput)
    • Work in progress limits (leading indicator, not lagging)
    • Developer-reported friction points (qualitative but predictive)

  2. Low signal metrics:

    • Lines of code (easily gamed, negatively correlated with quality)
    • Commit frequency (measures activity, not productivity)
    • Story points completed (inconsistent across teams)

  3. Context-dependent metrics:

    • Deployment frequency (depends on architecture and risk tolerance)
    • Change failure rate (definition varies widely)
    • PR size (small isn’t always better for every change type)

The platform differentiation is in interpretation, not collection:

All these platforms can collect similar data. The difference is how they help you interpret it.

  • Jellyfish excels at business context (“this is why metrics changed”)
  • LinearB excels at workflow automation (“here’s how to improve metrics”)
  • DX excels at root cause analysis (“this is what’s causing friction”)

One statistical warning:

Be careful with benchmarks. The LinearB “8.1 million PRs” dataset is impressive, but your team’s context may not match. I’ve seen teams optimize toward industry benchmarks that weren’t appropriate for their domain (e.g., pushing for smaller PRs when their monolith architecture made that counterproductive).

Always validate external benchmarks against your own historical data before setting targets.

3

The organizational readiness piece is often overlooked in platform evaluations.

Change management is the real implementation cost:

We piloted Jellyfish and had great data within weeks. But getting teams to actually use it took 6 months. The challenge wasn’t the platform—it was organizational.

Questions I now ask before evaluating any platform:

  1. Who will own this? Intelligence platforms need a dedicated owner (not a part-time responsibility)
  2. What’s the manager readiness? If managers aren’t comfortable having metric-based conversations, the platform becomes shelfware
  3. How will we handle gaming? Every metric can be gamed. What’s our plan when it happens?
  4. What’s our communication strategy? How will we explain this to developers who may feel surveilled?

The rollout pattern that worked for us:

  1. Start with team-level metrics only (no individual visibility)
  2. Let teams self-select for the pilot (volunteers are more forgiving)
  3. Share the “why” extensively before sharing the “what”
  4. Make managers accountable for improvement, not developers
  5. Celebrate wins publicly to build trust

The platforms that failed for us:

We tried Swarmia first and it was technically excellent. But our org wasn’t ready for real-time metrics visibility. Managers panicked when they saw red indicators. We switched to quarterly DX surveys to build measurement maturity before trying again with LinearB.

My advice:

Don’t choose the most sophisticated platform. Choose the one that matches your organizational maturity. You can always upgrade later.

4

From a product perspective, the missing piece in most platform evaluations is the connection to business outcomes.

What I look for as a product leader:

I don’t care about DORA metrics in isolation. I care about:

  1. Feature velocity - How long from product decision to customer value?
  2. Quality - Are we shipping bugs that hurt customer experience?
  3. Predictability - Can I make commitments to customers and stakeholders?
  4. Investment visibility - Are we spending engineering on the right priorities?

The platforms that help product leaders:

  • Jellyfish is the only one that directly answers “where is engineering investment going?” in business terms. I can see percentage of time on new features vs. maintenance vs. tech debt without asking engineering to categorize tickets manually.

  • LinearB helps with predictability. The delivery forecasting features let me give more accurate estimates to stakeholders.

  • DX surveys tell me when engineers are frustrated, which correlates with future slowdowns I need to plan around.

The integration that changed everything:

Connecting Jellyfish to our product analytics pipeline was a game-changer. We can now see:

  • Engineering effort per feature
  • Feature adoption rate
  • Engineering investment ROI per feature area

This let us make data-driven decisions about where to invest next, not just gut feel.

My recommendation:

Whatever platform you choose, make sure it can connect engineering data to product/business data. The platforms that stay siloed in engineering eventually lose executive sponsorship.