Jellyfish vs. Building In-House

Building dashboards has never been easier. Building trusted engineering intelligence has never been harder. See when building makes sense, and why engineering leaders choose Jellyfish when they need more than basic visibility.

Put engineering intelligence to work

Many platform teams can build internal engineering products. The question is where you want to invest your engineering expertise. Jellyfish continuously evolves your engineering intelligence so your team can spend less time maintaining dashboards and data models, and more time helping the organization learn, adapt, and improve.

A trusted engineering work model

As engineering evolves, integrations change, workflows shift, and metrics drift. Jellyfish is built on a battle-tested engineering work model powered by the industry's largest production engineering dataset, giving you a trusted foundation.

Built for continuous AI change

AI models, agents, and workflows evolve faster than internal platforms can keep up. Jellyfish continuously expands AI SDLC intelligence so you can act on the latest insights without constantly rebuilding your own solution.

Intelligence beyond your own data

Your data explains what happened, but not what good looks like. Jellyfish combines benchmarks, behavioral insights, research, and expert guidance to help you understand what to measure, how top-performing organizations operate, and where to improve next.

Beyond the first build

Compare the long-term tradeoffs

Jellyfish

Build Internally

Staying Ahead of AI-Driven Change

Complete AI SDLC visibility that evolves with the ecosystem so your team quickly acts on intelligent recommendations instead of building to catch up.

Build Internally

Every new model, agent, and workflow adds to an internal roadmap that never closes while AI strategy decisions wait.

External Intelligence

Benchmarks, behavioral insights, and expert guidance from the Jellyfish research team, informed by 1,000+ organizations, so you know what to measure and how top performers operate.

Build Internally

AI can summarize your own environment but can’t tell you whether it’s good, or what leading companies do differently.

Guidance and Recommendations

Jellyfish Assistant and Agents turn data into immediate, context-rich recommendations, reducing time to insight and helping teams reach higher performance faster.

Build Internally

Ad hoc queries provide raw data answers and require manual interpretation without context or direction built in.

Data Trust & Fidelity

A battle-tested engineering work model powered by the industry’s largest production engineering dataset and continuously refined as tools, workflows, and AI evolve.

Build Internally

Fast to prototype a dashboard, slow to trust the data. You still maintain integrations, normalize changing sources, and reconcile metric definitions.

Engineering Focus

Engineering stays focused on providing customer value and driving AI transformation while Jellyfish owns the intelligence platform.

Build Internally

AI speeds the first build, but engineers still spends significant time prompting for data and interpreting results instead of acting on insights.

Total Cost of Ownership

Predictable subscription with continuous platform improvements, research, and support, and no internal roadmap to fund.

Build Internally

Hidden costs compound: build, integrations, ongoing metric work, and upkeep, plus LLM API and prompt-engineering costs that are hard to forecast and rise as usage grows.

“Jellyfish is an essential tool for data driven engineering. The implementation was surprisingly quick, and the technical support has been exceptional. Most notable, the functionality to measure GitHub Copilot adoption and impact has been crucial for our AI strategy, allowing us to identify specific improvement opportunities and measure the impact of changes with real data.”

David Parra Perez

Head of Software Engineering at Iberia

“I use Jellyfish to manage signals from Jira, Bitbucket, Confluence, and AI coding, giving me a true bird’s-eye view across everything. The easy integration with APIs is a big plus for me—it saves a lot of time compared to maintaining bespoke Excel files. I really appreciate the AI adoption in the platform, as it makes conversations and yardsticks more consistent, and the setup was incredibly easy with strong presales support.”

Richard Ginsberg

SVP – Head of Engineering Operations at Guidepoint

“When a transformation is moving this fast, operating blind isn’t an option. Jellyfish gave us the data to understand where we were seeing real ROI, where the new constraints were emerging, and how to stay in control of something that’s genuinely changing in front of our eyes.”