Operations | Monitoring | ITSM | DevOps | Cloud

The real economic decision for observability happens at ingest, before storage, billing, and retention choices are locked-in. Until now, the logic governing that decision could only see three broad fields: application, subsystem, and severity. That just changed. TCO routing now matches on any field in the event payload, including nested keys, custom fields, and event body content, using DPXL, the DataPrime Expression Language.

Most observability platforms are built to answer one question: what’s broken right now. Regulators are asking a different one: what happened, exactly, and can you prove it? Digital banking operates under constant regulatory scrutiny, where frameworks like DORA, PCI-DSS, and GDPR require every incident to be fully reconstructed across systems, timelines, and access. Systems can recover quickly, but the ability to explain what happened often remains fragmented across tools and teams.

It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.

Traditionally, achieving deep visibility into distributed systems required significant trade-offs in engineering time. Collecting meaningful application metrics and traces required teams to embed language-specific agents, modify source code, or manage complex library dependencies across every service.

Capital markets systems don’t scale linearly. A macro event, an earnings release, a sudden liquidity shift, and telemetry volume doubles in seconds. In most observability platforms today, that spike means one thing: every byte gets written to a high-cost index before a single query can touch it. There’s no middle ground. You pay full indexing cost for the compliance log that no one queries for six months, the same way you pay for the execution trace you need right now.

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.

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.

We’re proud to announce that Coralogix has earned 196 badges across 15 categories in the G2 Spring 2026 Reports, our strongest G2 performance to date. Placing in 369 reports, this represents a significant leap from Spring 2025, when we placed in 318 reports and earned 141 badges. These results are a direct reflection of the trust our customers place in Coralogix and their willingness to share honest feedback on the world’s largest software review platform.

For mobile-first organizations, the distance between a “slow app” and a “resolved ticket” is often filled with guesswork. Mobile performance is notoriously difficult to capture because it lives at the intersection of device hardware, network stability, and local code execution. Today, we are closing that gap with the launch of Coralogix Mobile Performance.

If you’ve worked with an observability pipeline, you’ve probably experienced schema problems: a field disappears, a type shifts from string to number, or a new label quietly appears. The causes are everywhere. Different teams adopt different naming conventions. A dependency upgrade changes the shape of a library’s log output. Over time, these small, reasonable decisions compound into schema sprawl: dashboards break, alerts misfire, and teams scramble to find out what happened.