Engineering KPIs that matter for software teams

What are engineering KPIs?

Engineering key performance indicators (KPIs) are quantifiable metrics that tell leaders about the performance, efficiency, or impact of engineering. Each KPI is often used as a “North Star metric” for reporting or to monitor progress against strategic goals.

At top companies, no single metric is treated as sufficient. Google’s Developer Intelligence team, for example, measures code reviews not just for speed (time to complete), but also ease (how intuitive the process is) and quality (usefulness of feedback). This balanced view helps surface tradeoffs that raw numbers alone would miss.

Why track engineering KPIs

There are two main reasons engineering organizations use KPIs:

Reporting to stakeholders

Executives need to demonstrate the ROI of engineering investment to boards and peers. KPIs allow leaders to clearly communicate how engineering is delivering value across quality, efficiency, and impact.

At LinkedIn, for example, the Developer Insights team provides leaders with metrics like build time, deployment success rate, and a Developer Net User Satisfaction score to quantify how engineers feel about their tools and workflows.

Tracking strategic progress

To ensure your strategy is working, you need KPIs to measure it. Peloton tracks time-to-10th pull request to gauge onboarding effectiveness, paired with deployment frequency and change failure rate to ensure new engineers are both productive and delivering quality work.

The Core 4: DX’s framework for engineering KPIs

Instead of treating KPIs as disconnected measures, DX recommends using the Core 4 as the foundation. These four categories capture the full scope of engineering performance and align directly with executive concerns.

1. Business impact KPIs

Metrics like project ROI, time-to-market, and cost of delay measure how engineering creates value for the business. Some companies even tie engineering metrics directly to revenue. For example, scaleups like GoodRx translate time lost into dollars saved when inefficiencies are reduced.

2. System health KPIs

System health metrics track uptime, latency, throughput, and scalability to ensure reliability. LinkedIn adds nuance by measuring CI determinism—the opposite of test flakiness—to ensure build pipelines deliver trustworthy results.

3. Developer experience KPIs

These measure satisfaction, engagement, and ease of delivery. Scaleups like Notion and Postman treat ease of delivery as a north-star KPI because it reflects cognitive load and the true day-to-day experience of engineers.

4. Delivery efficiency KPIs

Metrics such as deployment frequency, lead time, and mean time to recovery reveal how quickly engineering can turn ideas into impact. Etsy goes further with experiment velocity, measuring how fast teams can design, run, and learn from experiments.

20 most important engineering KPIs

There are hundreds of software engineering KPI metrics available, but the Core 4 helps narrow focus to what matters most. Here are 20 high-value KPIs aligned to the Core 4 categories.

Business impact examples

• Project ROI: Net project benefits vs. cost
• Time-to-market: Speed from development start to release
• Feature adoption rate: Percentage of users adopting new features
• Project status alignment: Delivery vs. planned roadmap
• Cost of delay: Financial impact of postponed delivery

System health examples

• Application latency: Time from action to response
• Uptime/availability: Percentage of operational time
• Incident resolution time: Speed of incident recovery
• Scalability index: System capacity to handle growth
• Error rate: Percentage of operations that fail
• Throughput: Requests or transactions processed per time unit
• Network latency: Speed of data transfer between components
• Capacity planning accuracy: Actual vs. predicted usage

Developer experience examples

• Developer satisfaction: Survey-based engagement and sentiment
• Perceived productivity: Engineers’ self-reported productivity
• Code churn: Frequency of changes in the codebase
• Code review efficiency: Time and quality of code reviews
• Test coverage: Share of code covered by automated tests

Delivery efficiency examples

• Deployment lead time: Time from commit to production
• Mean time to recovery (MTTR): Speed of restoring service after an outage

How to start measuring engineering KPIs

Define engineering goals: Start with high-level objectives, such as improving developer productivity or increasing release frequency, then work backward to select KPIs. (Guide here)

Use qualitative and quantitative data: Google blends logs, surveys, and diary studies to validate their KPIs, while LinkedIn uses real-time feedback to supplement quarterly surveys.

Monitor and report regularly: Establish rhythms that align with executive reporting cycles, ensuring transparency with both the boardroom and engineering teams.

Measuring AI adoption and impact with KPIs

With the rapid rise of tools like GitHub Copilot and other generative AI assistants, boards and engineering leaders are investing heavily in AI to improve developer productivity. Studies show that developers using Copilot complete tasks up to 55% faster, reduce review time by nearly 20 hours per month, and see a 1.57x higher merge rate for AI-assisted pull requests.

Yet despite this promise, many organizations struggle to measure the real impact of AI. Leaders often receive only basic utilization snapshots, which don’t explain whether AI is truly improving productivity, adoption is consistent, or risks like quality tradeoffs are being introduced.

DX recommends extending the Core 4 with an AI-focused lens:

Business impact KPIs for AI

• ROI of AI adoption: Time saved on specific tasks, extrapolated into total dollar impact
• Cost efficiency: Developer hours reclaimed through AI assistance

System health KPIs for AI

• Incident rate with AI-assisted code: Ensures speed gains don’t compromise stability
• AI suggestion acceptance rate: Tracks value and trust in AI-generated outputs

Developer experience KPIs for AI

• AI adoption rate: Percentage of engineers regularly using AI tools
• Satisfaction with AI tools: Developer sentiment around usefulness and reliability

Delivery efficiency KPIs for AI

• PR completion time with AI: Reduction in coding and review cycles
• Cycle time impact: Tracking whether AI adoption accelerates delivery to production

To capture these insights, DX recommends a mixed-methods approach:

Telemetry metrics track high-level throughput (e.g., PRs, cycle time).

Experience sampling quantifies ROI in real time (minutes saved per task).

Surveys measure adoption, satisfaction, and perceived productivity.

Organizations that set these mechanisms early build a longitudinal view of AI’s impact, validating ROI while enabling better rollout strategies and use case education.

Why engineering KPIs matter for executive leaders

Developer experience, infrastructure, and platform engineering functions are critical, but the responsibility to connect engineering metrics to business outcomes ultimately sits with senior engineering leaders.

KPIs reveal developer needs: Chime tracks developer satisfaction scores for every tool, giving leaders a clear picture of where friction is slowing down engineering.

KPIs provide context for investment: Metrics like ease of delivery balance speed with sustainability, helping leaders allocate resources strategically.

KPIs connect to business impact: When leaders track outcomes through the Core 4, they ensure engineering performance is tied directly to organizational goals.

Using an engineering KPI dashboard

Most organizations use an engineering KPI dashboard to track progress across the Core 4. At LinkedIn, the Developer Insights Hub allows leaders to create tailored dashboards for every function.

A good dashboard integrates operational data with developer sentiment, surfaces actionable insights, and helps VPs and CTOs make decisions with clarity.

DX brings these dimensions together in one platform, helping engineering leaders identify the KPIs that matter most and translate them into measurable business outcomes.

Turning engineering KPIs into business outcomes

The Core 4 ensures engineering KPIs are not just numbers but levers for meaningful change. By connecting business impact, system health, developer experience, and delivery efficiency, leaders can diagnose problems, allocate resources wisely, and build sustainable engineering organizations using proven software engineering KPI frameworks.

The bottom line: Engineering leaders who implement comprehensive KPI measurement see measurable improvements in team productivity, reduced time-to-market, and stronger alignment between engineering investments and business outcomes. Companies like LinkedIn, Google, and Peloton didn’t become industry leaders by accident—they built systematic measurement practices that connect daily engineering work to strategic objectives.

Your next step is choosing the right measurement foundation. Start with the Core 4 framework, select 5-7 KPIs that align with your current strategic priorities, and establish regular reporting rhythms that keep both your engineering teams and executive stakeholders informed. The organizations that measure effectively today will be the ones that scale successfully tomorrow.

With DX’s AI Measurement Framework and Core 4 reporting, engineering leaders move beyond surface-level tracking to understand what truly drives productivity and satisfaction through comprehensive engineering team metrics.

Request a demo to see how DX helps executives measure performance with clarity and maximize developer effectiveness using the most impactful KPIs for engineering teams.