Jellyfish v. Flow

Appfire Flow (Pluralsight Flow) surfaces git metrics into simple dashboards to inform teams on engineering metrics. However as the needs and expectations of contemporary engineering organizations have evolved over the years, Flow has not kept up.

Code insight without context

Flow focuses a lot on metrics on developer activity and collaboration, but not much on process, workflows, or delivery. In other words, Flow measures outputs and not on outcomes. They have invested a lot in code-level management and productivity measurement of engineering teams, which we believe are much lower leverage for improving engineering performance than measuring process, delivery, and resource investment.

Real data science

Our patented work model boasts analysis that factors in data from sources across engineering touchpoints; not relying solely on issue counts or story points to infer work events.

Tailored insights

Jellyfish adapts to your team’s work; accounts imperfect processes, immature hygiene, issues at scale. We bring our platform to your engineering practices, not the other way around.

Predictive analytics

Enable proactive planning and decision making for the future; not only a retrospective look at what’s happened. Leverage your data to intelligently infer future work momentum and completion.

The Breakdown

Need more convincing?

Jellyfish

Flow

Measure the real impact of AI

AI is rewriting how software gets built, and Jellyfish is the system of record for understanding it. Our patented data model connects AI adoption, token spend, and tool usage directly to outcomes like speed, quality, and throughput; giving leaders a vendor-agnostic view of what is actually working. From coding assistants to autonomous agents, Jellyfish shows you where AI creates value, where it introduces new bottlenecks, and where to invest next.

Flow

Flow was built for a world before AI-assisted development. Flow’s roots are in first-generation Git analytics, and it has stood still while AI has rewritten how software gets built. It can chart commits, pull requests, and developer activity, but it has no concept of AI adoption, token spend, or which tools are actually impacting delivery.

Align the entire organization

Jellyfish strongly asserts that in order to expect great things from your engineering organization, engineering teams need to establish a holistic and fundamental understanding of what work is being done in order to determine the strategy of where you need to go. Metrics alone don’t cut it; Jellyfish’s patented Work Allocations Model ensures data fidelity, consistency, and accuracy of evaluating where effort is being directed; comparable across teams and organizations.

Flow

Flow offers little in the way of strategic alignment insight. Counting initiative types is barely more useful than just looking in Jira to understand where work is being performed. Without strategic alignment, it doesn’t matter how fast you’re going if the work is not the right stuff. If you were to jump in a car and drive 100 miles an hour but with no idea where you were going; that wouldn’t be progressive or efficient, it would be reckless.

Continuous improvement and momentum

Jellyfish’s boasts a torrent of updates and improvements that address our customers’ experience as well as introducing novel solutions for the ever evolving pain points of engineering organizations. Jellyfish will be a partner in helping solve engineering issues now and well into the future.

Flow

Flow’s platform has remained relatively stagnant the past couple of years. Few improvements to their core data collection and aggregation model has left the product in a place where it’s felt outdated, and yet customers have still been paying a hefty price tag.

Ensure the trust of your teams

Jellyfish’s Intellgience Platform provides value to all members of an engineering team, from the individual devs knee deep in code, to the engineering executives evaluating and setting strategy for years to come. Jellyfish offers a transparent and contextualized interpretive layer to ensure that metrics aren’t used to punish, but to improve; that analysis isn’t leveraged to cut, but to build; that everyone in the organization is privy to the same data, building trust and confidence up and down the organizational hierarchy.

Flow

Flow has been leveraged by engineering leaders since its inception as a way of evaluating developer productivity, however providing little in the way of context or transparency throughout the engineering team. The architecture of the solution emphasizes the concentration of power at the top, rife for misuse and utilization of the platform for punitive measures instead of promoting a culture of transparency and working to build for the future.