How trivago measures the ROI of AI with DX
trivago tracks an AI-driven 23% increase in throughput
This case study explores how trivago set out to 10x the reach and output of its engineering organization with AI, and how partnering with DX gave leadership the baseline metrics and real-time visibility needed to measure and prove that impact.
“Our internal initiative, Project 6000, is focused on fundamentally transforming how much leverage our team can achieve. DX gave us the objective data and framework to see what’s actually working, where adoption is thriving, and how AI is translating into real productivity gains.” Areej Aldaghamin, Technical Program Manager at trivago
About trivago
trivago is a leading global hotel search and price comparison platform. More than 50 million people use trivago each month to compare prices across hundreds of booking sites, helping them find the right place to stay at a good price while saving time and money. Powered by AI, trivago personalizes and simplifies hotel search, connecting travelers with more than 7.0 million hotels and other accommodations in over 190 countries.
About DX
DX is the leading engineering intelligence platform, trusted by high-performing tech organizations to measure and improve developer productivity, developer experience, and the business impact of AI investments. Developed by leading researchers, DX combines qualitative developer sentiment with quantitative SDLC telemetry to help companies baseline performance, remove friction, and quantify ROI.
The journey to 10x engineering leverage
For over a decade, trivago has leveraged AI and machine learning across its R&D teams. As AI tooling matured, the company set a new organization-wide ambition, Project 6000: for its roughly 600 people to deliver the impact of 6,000.
Engineering is central to that goal. Rather than using AI primarily to cut costs, trivago integrated AI tooling, from code generation to automated testing and troubleshooting, directly into daily workflows to expand capacity. As AI became a top priority across the company, one key question emerged: how do you know if it’s working?
Starting state: In-house tooling and the need for benchmarks
Before partnering with DX, trivago built and maintained their own internal developer platform. While the in-house system successfully cataloged services, teams, and ownership, leadership identified key areas for deeper visibility:
- Understanding developer friction: The internal tool provided clear service maps, while engineering leaders sought deeper insights into SDLC bottlenecks and day-to-day workflow friction.
- Contextualizing org-wide performance: Leadership required external industry benchmarks to evaluate performance against peers and establish clear standards for success.
- Aligning team perspectives: Executives required high-level performance indicators, while engineering managers needed team-level insights to support developers day to day.
“Our goal was to have a single source of truth. We wanted one solution to gain complete visibility across all of our engineering and development operations,” explains Areej Aldaghamin, Technical Program Manager at trivago. Aldaghamin adds, “We’ve always cared about staying competitive, so having industry benchmarking data to measure ourselves against was critical, especially once AI became a top priority across the company.”
Scaling AI visibility with DX
After evaluating several options, trivago selected DX to establish an objective single source of truth and streamline its tooling strategy. Today, trivago uses DX across all levels of the organization: by engineering managers, Platform teams, and executives. Engineering managers use the platform to drive team-level improvements: “DX gives our engineering teams a voice to share what’s happening as well as insight into and how to fix it,” says Aldaghamin. “It translates their perspectives in a way that executives can easily understand.”
At the Platform level, engineering leaders use DX to track AI tool token usage and costs alongside developer feedback and productivity metrics. These insights drive the company’s executive OKR framework, allowing leadership to measure and justify AI investments. Since using DX, AI adoption among developers grew by 35% over six months, reaching over 90%. Carolina Muradas, Strategy and Operations Lead at trivago, shares, "Anyone can maximize token usage. We wanted to maximize output. DX moves us from counting tokens to counting outcomes: pull requests shipped, cycle time, review load, so productivity gains show up in what we ship, not just in what we spend.”
In addition to tracking high-level AI impact data, the Platform team uses DX to guide AI usage across the engineering organization. The team consolidates usage data across multiple AI coding tools into one dashboard to evaluate how different developer cohorts leverage AI. This visibility highlights specific enablement opportunities, allowing leaders to help developers adopt tools more effectively.
trivago also tracks operational performance using the DX Core 4 framework and DORA metrics. Addressing developer friction with DX saw a 23% increase in PR throughput, driven by higher AI adoption. Aldaghamin explains, “DX translates engineering data into a single source of truth so they finally know how we compare to industry benchmarks.”
The future of engineering at trivago
With DX in place, measuring AI impact is a continuous, automated part of how trivago operates. By anchoring their AI rollout to clear benchmarks, cost visibility, and direct developer feedback, leadership has the objective data needed to justify investments, optimize tooling, and advance toward the goals of Project 6000.
