The GitHub Copilot dashboard shows Copilot usage, and Cursor analytics shows Cursor usage. Neither shows how a developer’s combined AI stack changes delivery, quality, review burden, or cost. That fragmented view makes productivity look better than it is.
Every AI coding vendor measures its own environment. GitHub’s Copilot usage metrics dashboard reports adoption, feature, model, and language trends for Copilot. Cursor analytics reports Cursor usage and team-level activity.
Those dashboards are valuable inputs, but they aren’t a complete view of AI developer productivity.
A developer may use Copilot for inline completion, Cursor for editing, and an agentic tool for a larger task. Each product records only the activity in its own system. Adding the dashboards together can double count users while still missing how the combined workflow affected pull requests, review time, production quality, and spend.
The metrics also use different definitions, time windows, and levels of detail. Without a common measurement layer, the organization has several accurate reports that can’t answer the same management question.
Match the same developer, team, repository, and business unit across every data source. Tool accounts, source-control identities, ticketing systems, and finance records rarely line up automatically.
The measurement layer should show which tools a developer or team used, where the work was done, and which delivery outcomes followed. It also needs to show where tools overlap and where they support different parts of the workflow. A developer active in three tools is still one developer.
Larridin’s Developer Productivity platform connects AI activity with engineering workflows and delivery signals so leaders can look at the full stack instead of interpreting each vendor report separately.
Tool activity becomes meaningful when it connects to the same outcome measures:
Measure these signals at the team, repository, and workflow level before rolling them up. A tool used for routine completions shouldn’t be compared directly with one used for complex refactoring without accounting for the work type.
Larridin’s guide to the AI code share metric explains why adoption and committed AI-assisted output provide different information.
A multi-tool stack combines seat licenses, token consumption, usage-based charges, agent activity, and supporting infrastructure. Attribute those costs to the teams and use cases generating them, then pair spend with durable output and delivery outcomes.
Token Spend & Insights gives finance and engineering a shared cost view across tools and agents. That helps identify idle licenses, unexpected usage growth, and tools whose costs are rising faster than measured value.
A cross-tool ranking can be misleading when teams use different products for different work. The strongest tool for inline completion may not be the strongest for debugging, test generation, documentation, or multi-file changes.
Segment results by workflow, role, team, and proficiency level. A low-performing tool may have an enablement problem, while a high-usage tool may be adding review burden elsewhere.
Larridin’s AI Adoption dashboard shows usage and cost across apps, agents, and teams. Pairing that view with AI proficiency and workflow data helps leaders determine whether differences come from the product, the user, or the surrounding process.
Compare tools within similar workflows rather than declaring one universal winner.
The goal is to determine which tool, workflow, and team combinations produce reliable value in your environment.
An executive view across tools should answer six questions:
Use weekly operational monitoring for cost spikes, adoption changes, and quality issues. A monthly leadership review can focus on tool strategy, workflow performance, and investment decisions.
Not reliably. The dashboards may count the same developers more than once, use different definitions and time windows, and report activity that isn’t directly comparable. Use vendor data as an input to a shared measurement model rather than simply adding the totals together.
Compare tools within similar workflows using cost, adoption, delivery impact, code durability, review burden, and quality. The tool with the highest usage isn’t necessarily the most valuable.
Easier measurement alone isn’t a strong reason to standardize on one tool. One tool may fit most teams, but some workflows may justify an additional product. Cross-tool evidence helps leaders decide where standardization reduces waste and where choice creates measurable value.
Operational teams should monitor important changes weekly. Engineering and finance leaders can review the portfolio monthly, with a deeper quarterly assessment for renewals, standardization, and budget decisions.
Larridin connects AI adoption, spend, workflows, code quality, and delivery outcomes across the tools engineering teams use. Leaders can compare performance without relying on disconnected vendor reports or incomplete activity counts.
Book a discovery call to build your multi-tool AI productivity view.