I’m Utku Altinkaya—CTO, product strategist, and builder of revenue-scale travel infrastructure.
On this blog I share practical insights at the crossroads of architecture, margin optimization, and commercial growth for B2B travel.
I’m currently leading LodgingBase: a next-gen distribution layer that cuts look-to-book waste and lets suppliers manage margins with precision at global scale.
If you’re paying per API call, every extra look costs you real dollars, but it goes way beyond that. Here is how you can calculate the actual cost of each look to optimize your traffic. Understanding Look to Book Ratio The Look to Book ratio measures how many search requests (looks) are needed to generate a single confirmed booking. The basic formula is: $$ \text{Look to Book} = \frac{\text{Number of Searches}}{\text{Number of Bookings}} $$ ...
The journey from monolith to microservices, integrating GIS logic, and launching a GPT-4 powered chatbot.
Yolcu360 is the largest online car rental platform in Turkey. At its inception, the system was a traditional Django monolith—typical for early-stage startups. When I stepped into the CTO role, we were facing performance bottlenecks and instability during traffic surges. For example an ad campaign with discounts in a popular tv show would degrade system performance and waste marketing budget. This is not a unique story. Many startups build MVPs quickly, and those temporary decisions become permanent as the business scales. Over time, technical debt compounds, and the monolith becomes a liability. You often hear “just a refactor will fix it,” but in reality, few systems can sustain that illusion. ...
How we built a travel search engine that served 450,000 requests per minute under 300ms, and what I’d do differently today.