Every engineering leader wants to know: is our AI code assistant actually driving productivity, and are we using it to its full potential? For teams rolling out the Windsurf AI Editor, Opsera’s Windsurf Dashboard which extends to Unified Insights provides the unified, real-time visibility you need across code suggestions, editor engagement, automation, and tool effectiveness, all in one place.
Below, we walk through every Windsurf Dashboard metric, explain how real teams use each one, and show where to drop in the corresponding screenshots. This is your technical guide to turning Windsurf adoption data into actionable insights.
Windsurf didn’t start with a dashboard. It started with a question: Why does every meaningful coding flow get interrupted by a tab change? Early adopters say the result feels less like autocomplete and more like pair programming on fast forward.
What sets Windsurf apart is the Cascade agent that sits beside your code. Unlike prompt-driven copilots, Cascade maintains a graph of your entire codebase, watches your cursor, and decides when to fetch context or call external tools. Users can ask it to refactor, generate tests, or even open pull requests; Cascade writes the code, updates files, and pushes changes without breaking focus.
As AI assisted development matures, leaders need answers:
Opsera’s Windsurf Dashboard makes these insights available without spreadsheet wrangling.
These metrics track how engineering teams engage with the Windsurf AI Editor and reveal whether the AI is helping developers ship real code. Three pillars suggestions offered (ideas), accepted lines (value), and acceptance rate (quality), form a feedback loop that measures how AI assistants are actually integrated into daily workflows. Add Active Users to the mix, and you see who uses the editor. These four metrics, part of Opsera’s Windsurf Dashboard are central to understanding whether AI assistance is adopted, trusted, and valuable in your org.
Use case: A low acceptance rate may signal confusing suggestions, outdated training data, or poor prompt design. Teams can dig deeper to refine their use of the editor.
Understanding whether developers are using Windsurf consistently and when they rely on it most, is critical to gauging adoption, spotting momentum surges, and identifying onboarding or engagement gaps. These trend charts help teams surface real behavioral patterns in how AI suggestions convert into accepted code across days.
See a daily breakdown of both suggestions and acceptances. Lets you spot productivity surges during pre-release sprints and identify dips after major releases or onboarding cycles.
This timeline shows how many lines were suggested and how many were actually accepted each day. It reveals when developers rely on AI the most, whether that’s before sprints finish, during big refactoring pushes, or during quiet maintenance periods. A persistent gap where suggestions exceed accepted lines signals opportunity for prompt optimization or coaching.
This metric correlates the number of lines accepted with the number of unique users per day, helping managers see if productivity and adoption are rising together.
These paired charts normalize output across contributors. They help you see if increases in suggestion volume are matched by active usage and whether productivity per developer is rising. That insight keeps you from doubling tool seats without lifting real engagement.
Together, these trends give leaders a real‑time lens into adoption health, making it obvious when rollout, retraining, or daily stand‑ups should focus teams on using the editor more effectively, all without a spreadsheet in sight.
AI isn’t just sitting in your IDE, it’s working in the background to refactor, generate, and improve code automatically. The Windsurf Dashboard also shows where, how often, and with which models your automated workflows are actually running. It helps site reliability, operations, and platform teams understand their automation ROI and whether infrastructure is over- or under-utilized.
The dashboard tracks how many automation (or Cascades) triggered per day (Runs), the number of discrete actions taken inside those workflows (Events), and how many different AI models executed them (Unique Models).
Automation runs act as crucial pressure valves in AI enhanced workflows. They eliminate manual effort, prevent drift, and enforce standards. For teams scaling AI assistance across a growing codebase, these charts make the difference between "one-blast automation" and "efficient, modular, auditable AI."
Use case: If unique models spike, your team might be in an experimental phase; monitor which models achieve the highest acceptance rates.
If automation measures where AI helps, acceptance rate tracking shows how well it does. The Acceptance Rate Trend Over Time chart plots your trusted signal of suggestion quality across sprints, prompt updates, and model versions.
Tracks if the suggestion quality and developer trust are improving. Use it to correlate training, prompt changes, or model upgrades with real workflow outcomes.
It calculates how many AI suggested lines were accepted, divided by total lines suggested, as a percentage. Accepting more suggestions than average, especially trends above 50–60% means team trust and AI integration are strong. This chart is crucial for any AI editor's retrospective, enabling them to analyze what worked, what didn’t, necessary course corrections for the next release cycle.
This section shows exactly where developers are interacting with Windsurf Editor, and how they work in practice. Rather than just languages, it tracks specific tools and workflows in the Windsurf environment helpful to understand which editor actions developers rely on most
This chart breaks down AI-assisted coding across languages. If Python or JavaScript dominate but your roadmap includes Go or Rust, it reveals adoption blindspots. Teams can use this insight to tailor prompt libraries, Cascade rules, and enablement for less-used languages. It also helps governance teams allocate model licenses more efficiently.
This chart reveals how often different development actions are used within Windsurf Editor: Producer style actions such as CODE_ACTION, PROPOSE_CODE, RUN_COMMAND, navigation tools like VIEW_FILE, structural helpers like VIEW_FILE_OUTLINE, search integrations (SEARCH_WEB, FIND, GREP_SEARCH), codebase queries (MQUERY), or agent level interface calls like PROXY_WEB_SERVER.
Usage patterns here show what workflows developers use most. If PROPOSE_CODE dominates but rarely leads to accepted lines, prompts may need tuning. If search tools are underused, teams may not find relevant context quickly.
These metrics offer a structured look into how Windsurf’s different AI models perform, not just in usage volume but in practical efficiency and operational overhead. These insights help teams govern model choice, tune prompts, and align investments with actual impact.
Highlights how many times each AI model (such as Claude, GPT-4, Gemini) was used to generate code across Cascades and suggestions.
High run counts for a new model often indicate ongoing testing or a rollout phase. Declining usage of older models helps you confidently decommission deprecated options. Use this data to watch for model drift, ensure default versions remain optimal, and maintain cost effective model lifecycle governance.
Tracks the total number of messages sent to each model during interactions.
If a model emits high message volume but results in low acceptance, it might be caught in prompt loops or producing redundant output. Efficient models balance minimal messaging with high acceptance rates. Monitoring this helps reduce unnecessary compute costs and improves developer trust.
Summarizes all model activity, which is ideal for reporting to engineering leadership or procurement, and for training evaluation.
This summary is valuable for comparing model effectiveness, guiding prompt refinements, and informing purchasing or licensing decisions
Windsurf Editor is powered by data driven insights and has been proven in real-world production environments. With high adoption rates across top enterprises and millions of active developers, it continues to deliver unmatched performance and efficiency.
A single view improves developer focus, shortens retrospectives, and reduces tool spend, emphasizing dashboard ROI. Leadership teams gain objective release health indicators at a glance, as detailed in Opsera’s unified view of DevOps Performance. Windsurf metrics extends to Opsera’s existing Leadership Dashboard so executives can view cost, risk, and velocity on the same baseline that engineers use day-to-day.
If you’re an Opsera Insights NX user, simply connect Windsurf Editor in the dashboard wizard. Leverage companion dashboards for Security Posture, DORA metrics, and more. Unlock deeper insights and build a more stable, secure DevOps culture.
Start exploring the Windsurf Dashboard today! Schedule a demo to unlock deeper insights, improve time to resolution, and build a more stable, secure DevOps culture: Start exploring the Windsurf Dashboard today.
