Engineering analytics platforms are crucial for understanding and optimizing software development. Both Weave and Jellyfish aim to provide insights into engineering team performance, but they approach this with different methodologies and feature sets. Weave introduces an AI first approach. They use LLMs & machine learning-driven approach to assess engineering output and productivity, while Jellyfish offers a more traditional suite for managing engineering operations. This comparison will explore how these two engineering productivity analytics tools stack up, helping you choose the best fit for your agile teams.

What the Industry is Saying

While Jellyfish has garnered positive reviews for its DORA based approach, the industry is increasingly recognizing the limitations of these traditional metrics. Platforms like Weave are gaining attention for their innovative solutions:

• On Weave's Advanced Metrics: "Weave (workweave.ai) uses machine learning to assess engineering output, moving beyond traditional metrics like lines of code or pull requests, which have weak correlations to actual engineering effort (correlations of 0.3–0.35)."

• On Weave's Accuracy: "Weave’s proprietary model achieves a much higher correlation to real engineering effort (0.94), providing a more accurate and standardized metric for measuring engineering productivity."

• On Jellyfish’s Metrics: “What I don’t like about Jellyfish is that it can feel a bit too high-level for day-to-day QA work”. People note that Jellyfish can be helpful for finance teams.

Weave: Precision and Insight. Jellyfish: Comprehensive Operations.

Both platforms offer engineering analytics software, but their core strengths differ.

Improve Developer Experience

Weave contributes to developer experience by providing assessments of work, reducing reliance on potentially misleading metrics and enabling data-backed conversations about workload and focus. Its confidential benchmarking also allows teams to understand their performance in a non-punitive way. Jellyfish, on the other hand, aims to cover developer experience through qualitative surveys and tech investment.

Deliver On-Time

Weave enhances delivery by offering a more precise understanding of actual engineering effort and productivity through its ML model. This insight is intended to support realistic planning and identifying bottlenecks, helping teams deliver on time. Jellyfish provides tools for delivery tracking and planning.

Plan Strategically

Weave complements strategic planning by delivering a standardized understanding of engineering output. This allows for more informed decisions regarding resource allocation, investment in specific areas, and aligning engineering efforts with business goals. Jellyfish assists with organizational planning and financial reporting.

Measure Engineering Output Accurately

Weave’s ML-powered model analyzes development activities to provide a standardized unit of engineering effort with a 0.94 accuracy to actual output, an improvement over metrics like LoC or PR counts (0.3-0.35 correlation). Jellyfish relies on a broader set of metrics which, while comprehensive, may not offer the same level of standardized precision in effort measurement.

See How We Compare

Data-Driven Engineering Teams Choose Weave for Precision Analytics

While Jellyfish provides a platform for engineering management, Weave offers an AI-driven approach to understanding engineering productivity. For teams seeking engineering analytics for agile environments, focusing on accurate, standardized, and actionable insights, Weave presents a notable advantage. Its focus on measuring engineering effort aims to set a new standard for engineering productivity analytics.