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We brought home the win. 🏆 Rise has been named the Digiday Technology Award winner for Best Data Team. A huge moment for the team behind the data, infrastructure, and intelligence that power Rise every day. What makes this recognition especially meaningful is that it reflects work happening across the company: building systems that can handle enormous volumes of data, making sense of increasingly complex signals, and turning that intelligence into better decisions in real time. It’s also a reflection of where we’re headed. As Rise continues to build in in-app, the ability to work with complex data at scale becomes even more critical. Congratulations to everyone who helped make this happen. This one belongs to the team. 👏 #DigidayAwards #AdTech #InApp #Data #Programmatic

What does efficiency at scale actually look like? Join us to see how Rise reduced infrastructure costs while improving performance across complex workflows.

I’m excited to share that next week I’ll be speaking at Google Cloud Next conference in Vegas. This is no doubt Google Cloud’s biggest event of the year, with some of the most interesting companies and leaders sharing what they’ve been working on and where they’re headed next. I’ll be talking about our partnership with Google Cloud, and how we’re leveraging BigQuery’s newest Fluid Scaling to push the boundaries of how we operate at scale. From the start, this has been a very strong partnership. Sitting together, solving problems, testing early ideas, and building through the challenges that come with scale. A big thank you to ‏Boaz Maoz‏ , ‏Shlomi Shani‏, ‏Tomer Vermuth‏Yoni Daniel‏ ‏Elit Ben Basat Nuriel‏Yael Warman Yacov Nagose‏ and the all team. The collaboration and trust we’ve built over time are a big part of why this works. We’ve also been fortunate to be part of the Fluid Scale beta. Early access is one thing, but making it work in production is where the real work begins. In practice, that means building infrastructure that can manage growing data complexity while staying efficient at scale. And just to give you a preview, we’ve already reduced our infrastructure costs by 25% while scaling slot consumption and speeding up our entire workflow. Looking forward to sharing more at Next. #GoogleNext #GoogleCloud #BigQuery #DataInfrastructure #DataEngineering #CloudComputing

With Q2 around the corner and a full calendar of trips ahead, it feels like we’re heading into a different kind of quarter. Ad tech has always moved fast, with new environments constantly surfacing, new rules taking shape, and new trends emerging. Every conversation we had these past few months, across supply and demand, pointed to the same thing. The industry is pushing toward better outcomes. What does that actually mean? Less noise, fewer black boxes, and a clearer line between what runs and what actually drives performance. Today, that gap still exists. There’s still too much waste, too many assumptions, and not enough systems that can consistently connect signal to action at scale. At Rise, we spent the last few months sharpening our edge. We doubled down on the fundamentals we’ve been building for years. A system that can operate at massive scale, apply intelligence in real time, all while staying efficient and ROI positive. That doesn’t happen by accident. It takes deep infrastructure work, strong data foundations, and the right partnerships. We’ve invested heavily in all three. And I’m proud of where we are. A system that can ingest, process, and act on data at the user level, in real time, end to end. That’s not trivial. But the last few months were also about exploring and building new areas we can unlock. With LLMs and the infrastructure to actually run them properly, we have an opportunity to bring a new level of precision and quality to outcomes. Not just optimizing within the system, but enabling it to act with more data, context, and intent. We look at this as a shift toward more agentic outcomes. Outcomes driven by systems that can actually interpret more signals, adjust in real time, and make decisions that consistently lead to better performance. This is where we’re heading. And it opens up possibilities that didn’t feel realistic even a year ago. As we move into Q2, we’re taking this on the road to meet partners, share what we’re building, and learn from the market as it evolves. If you’re curious about where this is going, take a look at where we’ll be in Q2 and feel free to reach out → https://lnkd.in/dmi9Uxug Would love to connect, share more, and explore how we can work together. #AdTech #Programmatic #AI #DigitalAdvertising #Innovation

WebMCP is coming to Google Chrome. WebMCP isn’t just a technical proposal. It’s a signal that agents will stop pretending to be humans and start interacting through structured interfaces. That changes the mechanics of the open web. Real time decisioning in programming is built around human behavior. Impressions, clicks, sessions, cookie level signals. An agent doesn’t “browse.” It executes. It won’t scroll your homepage. It will request a price, compare options, complete a transaction and move on. If that becomes normalized at the browser level, then a few things follow. First, inventory and traffic will look different. Some “users” won’t generate pageviews the way we’re used to measuring them. Second, intent signals could get cleaner. An agent acting on behalf of a user is explicit about the task. That’s more deterministic than inferring intent from five content pages and a scroll depth. Third, monetization models will get pressure tested. If discovery shifts from browsing to execution, then value shifts closer to the outcome. For programmatic, that’s uncomfortable and exciting at the same time. We’ve already seen how infrastructure changes reshape the market. Mobile did it. Header bidding did it. Privacy changes did it. Agent-native interaction could do the same. The question isn’t whether agents will interact with the web, It’s whether our pipes, measurement, and optimization logic are built for that reality. Rise

In programmatic, there’s no such thing as a “small” inefficiency. When you operate at real scale (millions of events per second) tiny technical decisions compound fast. What looks harmless in testing can quietly turn into instability, wasted cloud spend, and unpredictable systems in production. One of our lead engineers, ‏Aryeh Klein‏, recently shared a great breakdown of this reality in an article about how a seemingly minor dependency issue led to massive memory spikes at scale, and how fixing it properly brought the system back to stability. The takeaway for me wasn’t just technical it was strategic: at programmatic scale, infrastructure discipline is business discipline. Being truly data-driven and cloud-native means building systems that behave predictably when traffic spikes, costs are under pressure, and real-time decisions matter. This kind of work rarely shows up in pitch decks but it’s exactly what our partners rely on when performance, trust, and scale are non-negotiable. 👉 Link to the article in the comments. Rise