In this interview from the AWS Financial Services Symposium, John Trapani, field chief technology officer of financial services at Datadog, joins theCUBE's Rebecca Knight to discuss how the convergence of cloud migration and AI adoption is intensifying the need for unified observability across financial institutions. Trapani explains that while modernization has been a multi-year trend, the last 12 months have added asymmetric pressure on firms to demonstrate real AI value quickly. He positions Datadog as the single pane of glass that ties together logs, application performance metrics, digital user experience and security — giving complex financial services environments one authoritative place to understand system behavior and resolve issues before they reach customers.
The conversation also explores a compelling real-world case study: Itaú Unibanco, the largest private bank in Latin America and operator of Brazil's Pix instant payment network, consolidated more than a dozen point solutions into Datadog's platform. Trapani details the measurable results — teams reduced incident resolution time by more than 30% and saw nearly 50% fewer incidents overall, with engineers sharing a single real-time view during crises instead of reconciling data from separate tools. He offers practical guidance for technology executives facing similar challenges, advising them to start where pain is greatest. From addressing logging inefficiencies to unifying telemetry across hybrid stacks, Trapani outlines how consolidation delivers not just cost savings but the clean, authoritative data foundation that AI capabilities — including Datadog's Bits AI SRE — require to drive meaningful operational outcomes.
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John Trapani, DataDog
This conversation examines artificial intelligence-driven continuous delivery and governance for financial services. Alex Valentine of Harness appears at the Amazon Web Services Financial Services Symposium 2026 to discuss continuous delivery, governance and platform consolidation.
Valentine appears on theCUBE Research with Rebecca Knight of theCUBE Research to explain Harness's role as the AI platform for everything after code. They outline how the platform automates build, test, security, deployment and continuous verification and discuss challenges unique to financial services such as high auditability standards, cloud migration patterns and reducing engineering toil while maintaining reliability.
Valentine describes a real-world automation of a 150-page Citibank onboarding process into a workflow that runs in ten minutes, dramatically reducing deployment time. They cite Morningstar’s consolidation from 36,000 Jenkins pipelines to 50 reusable templates and emphasize continuous verification and the need for engineering leaders to refactor processes rather than replicate legacy patterns.
In this interview from the AWS Financial Services Symposium, John Trapani, field chief technology officer of financial services at Datadog, joins theCUBE's Rebecca Knight to discuss how the convergence of cloud migration and AI adoption is intensifying the need for unified observability across financial institutions. Trapani explains that while modernization has been a multi-year trend, the last 12 months have added asymmetric pressure on firms to demonstrate real AI value quickly. He positions Datadog as the single pane of glass that ties together logs, appl...Read more
exploreKeep Exploring
What challenges are financial services and insurance organizations facing as they modernize with cloud migration, microservices, and AI, and how does Datadog help address those challenges?add
What was Itaú Unibanco's situation before adopting Datadog, and what changed after Datadog began working with them?add
>> Hello, everyone, and welcome to the AWS Financial Services Symposium here in New York City. I'm your host, Rebecca Knight. I would like to welcome our first guest of the day. He is John Trapani, Field CTO Financial Services at Datadog. Welcome, John.
John Trapani
>> Thank you, Rebecca. It's great to be here.
Rebecca Knight
>> I want to start by looking at the big picture because financial services and insurance organizations are modernizing at a pace we have not seen before with cloud migration, microservices, and AI integration all happening simultaneously. You work with these firms directly on a day-to-day basis. What are you hearing about their growing pains, and where does Datadog fit into this?
John Trapani
>> Sure. That's a great question. So if you think about the last five to 10 years, the trend toward cloud modernization, microservices, that's existed for a while. What has happened over the last two or three years, and especially really I would point to the last 12 months, is the impact that AI is having on all of those plans. So there has always been the push towards modernization, evacuating my data centers. Now, it's being turbocharged because there's this additional sort of asymmetric pressure to show real value with AI very quickly.
Rebecca Knight
>> And where does Datadog fit into this?
John Trapani
>> So we're the unified observability platform, and that's a big way of saying in these very complex environments with very complex stacks, technology stacks, having the single place to go to understand exactly how your environment and how your applications are behaving, to be able to quickly identify problems before they impact your customers, quickly find the resolution, we're that single pane of glass that ties together your logs and your application metrics and your user experience out at the edge in the mobile devices they're using. With that one place, you can tie all of that together with a security overlay as well and only have your entire team look at the one single point and sing from the same hymnal.
Rebecca Knight
>> So let's actually talk about that single pane of glass with one of your customers, Itaú Unibanco, which is, of course, the largest private bank in Latin America and runs Pix, Brazil's instant payment network. Tell our viewers a little bit about the situation before Datadog and what happened once Datadog started working with you.
John Trapani
>> So Itaú was like many customers. They had accumulated over time a variety of different point solutions. I want to say the number was north of 12 or 13 different vendors, each supplying a little piece of the answer for their comprehensive unified observability. But you can't get unification when you have 13 different tools involved in your operation. So what they did was they decided to standardize on Datadog to provide that comprehensive look from logging and all the telemetry that comes from the logs, the application performance metrics, the digital user experience and synthetics that are monitoring those important customer journeys. And there were some notable achievements from having done so.
Rebecca Knight
>> So let's talk, the numbers that came out of that engagement were so impressive, but as we heard from AWS's Scott Mullins, a lot of it is also about the people and the processes. Can you talk a little bit about the changes that you saw with the people and the teams?
John Trapani
>> So I mean, right off the top, the first thing is that the teams are spending about 30% less time, little more than that, on issue resolution when there are incidents. So that by itself is a huge win. I mean, it's a huge win just in terms of no one likes getting woken up at 2:00 in the morning, and when you do, you don't want to spend three hours fixing a problem, right? But it's also that the number of incidents they were seeing because of the great early warning that they were able to deliver means that they're having, not quite, but almost half fewer incidents. So almost 50% fewer incidents in general as a result of having done this. And what that means is a lot of times when the bridge opens when there's a crisis and people start dialing in, rather than have to bring everyone up to speed, because everyone is looking at their own little part of the universe, everyone is looking at the same data at the same time. So just the amount of time they spend when there is an incident and people have to gather on that bridge becomes very, very condensed.
Rebecca Knight
>> Being able to see these problems much more clearly. I'm sure there are a lot of technology executives and senior leaders who are hearing what you're saying and they're wrestling with similar problems. Where do they get started?
John Trapani
>> So in many ways, the journey of a thousand miles begins with a single step. So if you're having an issue with logging, and maybe it's just a cost issue, maybe it's a utility issue where your teams are having difficulty really identifying valuable insight in that data, maybe that data's not well-correlated to your application performance because you're using two different platforms to do it. Starting where you have the most pain is always going to be a safe choice to make. Now, the trend in the industry is towards consolidation to achieve not just cost efficiency but operational efficiency. And increasingly, as AI begins to play a role, like with Datadog's Bits AI SRE, you need fewer authoritative sources of truth in order to get great value out of your AI capabilities. So that's another reason that might make sense to start organizing around a great answer like Datadog.
Rebecca Knight
>> Excellent. Well, John Trapani, thank you so much for coming on the show. A great conversation.
John Trapani
>> Thank you so much.
Rebecca Knight
>> I'm Rebecca Knight. Stay tuned for more of theCUBE's coverage of the AWS Financial Services Symposium.
Rebecca Knight