Engineering teams track deployment frequency religiously, but struggle to connect their sprint work to actual business impact.

DORA metrics have become the gold standard for measuring engineering performance – and for good reason. They provide valuable insights into deployment velocity and operational excellence. But there's an important gap: while DORA metrics tell us how efficiently we're shipping PRs, they don't reveal how much work those PRs should actually take.

This is where Weave Points comes in, an improved approach that focuses on measuring output of engineering work, not just the speed of delivery.

What Are DORA Metrics?

DevOps Research and Assessment (DORA) began with the goal of gaining a better understanding of the practices, processes, and capabilities that enable teams to achieve high velocity and performance when it comes to software delivery. The startup was acquired by Google in 2018, and continues to be the largest research program of its kind. [1]

According to our research, DORA metrics first appeared in the 2014 State of DevOps Report and have become the "gold standard for DevOps measurement."

The Four DORA Metrics

1. Deployment Frequency How often you deploy code to production. Software leaders can use the deployment frequency metric to understand how often the team successfully deploys software to production, and how quickly the teams can respond to customers' requests or new market opportunities.

2. Lead Time for Changes The time from commit to production. High deployment frequency means you can get feedback sooner and iterate faster to deliver improvements and features.

3. Change Failure Rate The percentage of deployments that cause failures in production.

4. Mean Time to Recovery How quickly you restore service after an incident.

Why DORA Still Matters in 2025

The latest DORA State of DevOps Report, published in 2024, surveyed "more than 39,000 professionals across many industries globally." [3] The research continues to show correlation between high DORA performance and business outcomes.

But here's where it gets tricky.

The Problem with DORA: What You're Missing

According to our research, measuring DevOps process ≠ measuring team productivity! While DORA measures the mechanics of software delivery, it ignores the contents.

Key Issues Include:

Gaming the System

• Deployment Frequency: Teams break updates into "micro-deployments" without actually getting more done

• Lead Time: Work gets split into smaller chunks without delivering more value

Missing the Human Element In October 2023, the DORA team cautioned against using these metrics to compare teams. Creating league tables leads to unhealthy comparisons and counterproductive competition. [4]

Value Blindness The key question isn't just about deploying code quickly – it's whether that deployment creates value for the business and customers.

Limited Context As Goodhart's law reminds us, "When a measure becomes a target, it ceases to be a good measure." [4] Teams optimize for metrics rather than outcomes.

Enter Weave Points: A Different Philosophy

While DORA measures delivery mechanics, Weave combines LLMs and domain-specific machine learning to understand engineering work. Instead of asking "how fast did you ship?" Weave asks, "What actually got done?"

How Weave Works

Weave uses AI to measure engineering work. We run LLMs + our own models on every PR and review, analyzing both output and quality.

The key difference? This isn't a line of code calculator; this is an actual estimate of the key metric: "How long would it take an experienced engineer to make this change?" [7]

What Makes Weave Different:

1. Understanding Work Substance

   • Built with a custom machine learning model that is trained on an expert-labeled data set of PRs

   • Analyzes both output quality and engineering effort

2. Work Classification Teams can see how much bandwidth goes to new features, bug fixes, or "keeping the lights on"

3. AI Impact Measurement Weave tells you how much work is getting done, how good it is and how much is done by AI.

4. Individual Insights Individuals use Weave to see how they're doing and where they can improve. [5]

DORA vs. Weave Points: The Real Comparison

When to Use Each Framework

Choose DORA When:

• You're optimizing delivery pipelines

• DevOps maturity is your primary focus

• You need industry benchmarking

• Regulatory compliance requires it

Choose Weave When:

• You want to understand actual productivity

• Individual and team insights matter

• AI impact measurement is important

• You're justifying engineering investments

The Smart Strategy: Complementary Measurement

The best engineering teams don't choose one or the other – they use both:

• DORA answers: "How efficiently are we delivering?"

• Weave answers: "What are we actually delivering and how valuable is it?"

Implementation Best Practices:

1. Start with DORA to establish your delivery baseline

2. Add Weave for productivity and value insights

3. Cross-reference both to find optimization opportunities

4. Focus on trends, not absolute numbers

Getting Started with Modern Metrics

Whether you're dealing with traditional delivery challenges or trying to measure AI's impact on your team, the key is starting with what matters most to your context.

For teams looking beyond traditional delivery metrics, Weave provides X-ray vision into engineering work. The platform uses AI to understand not just how fast you're shipping, but what you're actually building and how valuable it is.