Every engineering manager has a gut feeling about their team's weekly rhythm. Monday feels slow. Friday feels like a wind-down. But what does the data actually show?
We analyzed thousands of coding hours from developers across 100+ B2B companies to map developer productivity across the work week — and the results challenge some common assumptions.
"Adding manpower to a late software project makes it later." Fred Brooks wrote that in 1975. Fifty years later, engineering leaders still debate whether it's true.
We looked at real coding data from 100+ B2B companies on PanDev Metrics to understand how team size relates to individual developer productivity. The answer is more nuanced than Brooks suggested — but his core insight still holds.
AI coding assistants went from novelty to necessity in under three years. GitHub Copilot, Cursor, Cody, and dozens of alternatives now sit inside developers' editors, suggesting code, answering questions, and writing boilerplate. A Deloitte report on AI adoption in software development estimates that ~70% of enterprise development teams now use some form of AI coding assistance.
But are they actually making developers more productive? Or just more reliant on autocomplete?
We looked at real IDE usage data from 100+ B2B companies to find out what AI-assisted coding looks like in practice.
You've just hired a senior developer. They start Monday. When will they be fully productive?
HR says "30 days." The hiring manager says "a few weeks." The developer themselves says "give me the codebase and I'll be fine."
Reality is different. Coding activity data tells a more honest story about what new developer ramp-up actually looks like — and it's longer than most organizations plan for.
Swarmia has built a strong reputation as a developer experience-focused analytics platform. The company has published the book "Build" on engineering effectiveness and counts companies like Miro, Bolt, Chess.com, Handshake, and Lovable among its customers. Its free tier for teams of up to 9 developers makes it easy to evaluate. PanDev Metrics takes a broader approach, combining developer analytics with financial tracking and on-premise deployment. Both platforms serve engineering teams — but with different philosophies and capabilities.
Code Climate started as a code quality tool — one of the earliest in the automated code review space — and expanded into engineering analytics with its Velocity product at approximately $15 per developer per month. PanDev Metrics was built from the ground up as an engineering intelligence platform. Both provide team-level metrics, but their origins shape what they do best — and where each falls short.