Operations | Monitoring | ITSM | DevOps | Cloud

Digital trading firms operate in environments where milliseconds determine profit and loss. During volatile market conditions, platforms can appear fully operational while execution quality quietly degrades. When prices shift in so quickly, even a minor drift in your order-routing path means your competitors are exploiting the delta, while your platform appears perfectly green. For trading firms, observability is not just about uptime.

While AI have usage has been growing steadily for the last several years, the LLM models noticeably improved around the end of 2025. Specifically, they become more viable for software development. We are seeing the results. The feature and product delivery has picked up. One way to visualize this is by looking at the number of pull requests for your organization / software development teams. This chart shows the number of Github pull requests created by a team. Can you spot when AI usage increased?

A typical Next.js deployment can execute code in up to three different runtimes: Edge, Node.js, and the browser. You may already be capturing logs from server-side code, but if you are not capturing the full request from middleware through server rendering to the browser, you are missing a lot of debugging info when things go wrong. TL;DR: A typical Next.js deployment can run in up to three environments; Node, Edge, and the browser.

In a high-pressure incident, speed is everything. During incidents, engineers often jump between tools like traces, logs, and metrics, losing context at every step. This ruins the investigation state, slows down the process, and increases MTTR.

Distributed tracing has become essential for modern software teams. As applications evolve into complex distributed systems with microservices, APIs, databases, and third-party integrations, understanding how a single user request travels through your entire stack is no longer optional, it’s critical for maintaining performance, reliability, and user satisfaction.

As we wrap up RSAC, one theme that repeatedly emerged in conversations with security leaders is that the modern enterprise has reached a critical inflection point where the velocity of machine-generated telemetry has outpaced the capacity of traditional architectures. This trend requires an approach that moves beyond the storage of information to the activation of it in ways that don’t simply exacerbate alert fatigue.