AI coding tools ROI calculator: Measure your development team’s productivity gains

What is the actual ROI of GitHub Copilot? How much are you spending on OpenAI? Are AI code assistants saving your developers’ time, or simply adding another expense to the budget?

If you’re in search of a GitHub Copilot ROI model, a transparent OpenAI pricing calculator, or a practical GenAI cost calculator, you’re not alone. These tools have become essential for many engineering organizations. However, the costs are increasing, and leaders are seeking more than just anecdotal feedback.

At DX, we work with engineering teams across industries to help quantify developer productivity and evaluate the business impact of GenAI tools. The pattern is clear: budgets are growing, expectations are rising, and most organizations still lack a clear framework for evaluating returns.

This article introduces a structured approach to modeling ROI and explains how to evaluate productivity using the DX Core 4 — a unified framework grounded in DORA, SPACE, and DevEx. You can also try our AI coding tools ROI calculator to project savings and assess whether these tools are delivering what they promise.

What teams are spending, and why it needs to be justified

Spending on GenAI tools is now a material part of many engineering budgets. Here’s what typical spend is looking like for GenAI coding tools:

A mid-sized tech company typically spends between $100,000 and $250,000 per year on GenAI tools. Large enterprises with thousands of engineers often invest more than $2 million annually. To justify that level of spending, engineering leaders need to demonstrate that developer productivity is improving.

What the GenAI productivity data shows

Across hundreds of organizations working with DX, one consistent theme emerges: companies are investing heavily in GenAI tooling, but productivity results vary based on adoption, quality of usage, and internal enablement.

In top-performing engineering organizations, 60% to 70% of developers use GenAI code assistants on a daily or weekly basis. These teams are not only early adopters but also promote usage through training, shared practices, and clear expectations.

Beyond this leading group, adoption across the broader market is more uneven. Approximately half of the developers in most organizations regularly utilize GenAI tools. Still, usage rates vary widely from team to team, often depending on factors such as access to tools, team norms, and the degree to which leadership encourages adoption.

On average, developers report saving approximately 2 hours per week, with high-end users saving 6 hours or more per week. That reclaimed time has the potential to reduce backlog, increase velocity, or free engineers for higher-value work — but only if it’s measured and reinvested intentionally.

From a systems perspective, we’ve found a small but consistent positive correlation between GenAI usage and PR throughput. The signal is modest, but it aligns with industry trends showing early gains in velocity as teams mature their adoption.

The bottom line: adoption and impact are both rising, but results aren’t automatic. Teams that invest in rollout, measurement, and feedback loops tend to unlock more value and do so faster.

Using the DX Core 4 to evaluate GenAI investments

Many engineering teams use GenAI tools, but most lack a consistent method for evaluating their impact. Leaders often discuss time savings informally, and teams tend to rely on anecdotes rather than data to assess their success. Without a shared framework, they struggle to determine whether these tools create real value or simply shift effort elsewhere.

The DX Core 4 provides a structured approach to assessing the impact of GenAI tools across your engineering organization. Drawing on established frameworks such as DORA, SPACE, and DevEx, it breaks down developer productivity into four measurable dimensions: speed, effectiveness, quality, and business impact.

What GenAI looks like across the four dimensions

Say your team rolls out GitHub Copilot to 50 developers. You might use the DX Core 4 to evaluate outcomes like:

• Are cycle times improving, or are PRs moving through faster? (Speed)
• Has output changed, such as more diffs per engineer or quicker task completion? (Effectiveness)
• Are developers reporting less friction and higher satisfaction in surveys? (Impact)
• Has the frequency of rollbacks or bugs changed since adoption? (Quality)

By looking across all four dimensions, you avoid misinterpreting surface-level improvements as overall gains. For instance, if code output increases but satisfaction drops or error rates climb, you’ll know the tool may need to be better integrated or more selectively applied.

The Core 4 framework is in use at over 300 companies across various industries. Organizations using it report gains of 3 to 12 percent in engineering efficiency, a 14 percent increase in time spent on strategic feature development, and a 15 percent improvement in developer engagement.

GenAI tools can support each area of the DX Core 4. They help reduce cycle time by generating code faster, improve task flow by minimizing blockers, and support quality by assisting with test creation or documentation. But these gains are not automatic. Without precise measurement, intentional use, and team alignment, adoption doesn’t always translate to outcomes.

Evaluating GenAI tools through the lens of the DX Core 4 helps ensure that performance gains are real, sustainable, and aligned with what your organization actually values.

How the ROI calculator works

Our AI ROI calculator helps estimate the financial return of GenAI investments using a few practical inputs: team size, fully loaded cost per engineer, tooling spend, and estimated time saved per developer each week. Based on these, it calculates monthly hours reclaimed, the value of that time, and an ROI ratio.

You can adjust adoption rates or pricing assumptions to model different scenarios, from small pilots to full-scale rollouts.

Example: Measuring GenAI ROI with the DX Core 4

A product company rolled out GitHub Copilot to 80 of its 120 engineers. Over the course of two months, they tracked outcomes using the DX Core 4.

Here’s what they saw:

• Speed: Cycle time dropped from 6.1 to 5.3 days
• Effectiveness: Output (diffs per engineer) increased by 7%, mostly on routine tasks
• Quality: No increase in bugs or failed deployments
• Impact: Developer surveys showed a 9-point increase in satisfaction, and 63% said Copilot saved them time each week

To quantify that time, they used experience sampling and found developers were reclaiming about 2.4 hours per week on average.

Cost vs. value

• Time saved: 2.4 hours × 80 engineers × 4 weeks = 768 hours/month
• Hourly cost: Based on $150K/year — ~$78/hour
• Value of time saved: $59,900/month
• Tooling cost: 80 × $19 = $1,520/month
• Estimated ROI: ~39x

Because the team used the DX Core 4 to measure actual changes in speed, effectiveness, quality, and experience, they had a clear and credible case that Copilot was delivering real value, not just activity. This helped justify expanding the rollout across the rest of engineering and justifying the investment to leadership.

The overlooked cost side of GenAI

Many teams underestimate how quickly usage-based pricing can scale, especially with tools like OpenAI’s GPT-4 Turbo. A single integration generating 1,000 completions per day at 2,000 tokens each adds up to approximately 2 billion tokens per month. Depending on the prompt structure and output size, this can cost anywhere from $600 to over $2,000 per month.

As more teams and copilots rely on large language models, it’s easy for monthly spend to reach five figures. Without a way to connect that spend to outcomes, it becomes difficult to know whether the investment is worthwhile or simply growing unchecked.

The DX Core 4 helps put these costs in context. By measuring changes in delivery speed, developer effectiveness, code quality, and satisfaction, teams can assess whether usage-based GenAI tools are enhancing performance, rather than merely incurring additional expense.

Why impact doesn’t always follow deployment

Rolling out a tool doesn’t guarantee results. ROI often falls short when teams don’t drive consistent adoption, set clear goals, or put time savings toward meaningful work. Sometimes, tools are introduced without a plan, resulting in scattered spending and unclear outcomes.

The teams that get the most value tend to do three things well: they support onboarding, track results regularly, and connect tool use to their broader engineering goals. Without this, even the most effective tools struggle to make a meaningful difference.

With increased pressure on engineering budgets, platform and infrastructure leaders must justify every investment. Teams that take the time to model costs and value clearly can make stronger cases, work better with finance, and focus their efforts more effectively.

When teams combine usage data, cost modeling, and metrics from the DX Core 4, they gain a clear picture of how engineering work drives business results.

For a deeper look at how to integrate AI tools effectively into your workflows, read our Guide to AI-assisted engineering.

GenAI ROI calculator

The ROI from GenAI tools can be significant, but only when teams plan carefully and measure consistently.

If you are investing in Copilot, OpenAI, or custom AI integrations, now is the time to benchmark your spend, establish baselines, and track progress using proven methods.

And if you want a more complete view of productivity across your organization, explore how the DX Core 4 can help unify your metrics and uncover opportunities for improvement.