Operations | Monitoring | ITSM | DevOps | Cloud

Customer logs data is always messy. Being (and building!) an observability platform, we get to see all the beautiful, creative ways it can be messy, every single day. And yet, our customers expect, quite fairly, I might add, perfect query results and peak performance. Info SigNoz is an open-source observability platform that can be your one-stop solution for logs, metrics and traces.

OpenTelemetry backends provide storage, analysis, and visualization for telemetry data (traces, metrics, logs). This guide lists available OpenTelemetry-compliant backend options, categorized by use case: APM platforms, storage backends, visualization tools, and distributed tracing systems. For detailed comparison, see OpenTelemetry Backend Comparison.

If there was ever a year for AI observability, it was 2025. Vendors released assistants to cover a variety of use cases. Coralogix released the first agent (distinct from assistants!), Olly, an autonomous, multi-agent observability platform. The direction of travel is clear, but many vendors and users are about to run into some significant problems with their data layer.

As enterprises face increasing pressure to manage vast and complex IT environments, the demand for faster and more efficient IT management is rising. Traditional operating methods are proving insufficient, making the adoption of Agentic AI essential for organizations aiming to achieve truly autonomous IT operations. This innovative technology enhances decision-making and enables businesses to remain agile in a rapidly evolving digital landscape.

Connecting day-to-day development work to real user outcomes can be challenging. As a result, engineers and product teams often struggle to effectively prioritize projects together. While the goal of improving user experience (UX) is the same, each team relies heavily on different—and often siloed—forms of monitoring to understand their app, creating a disconnect in metrics and visualizations that can be hard to communicate.

The performance of your Kubernetes operators often influences the behavior of the applications they manage. Operators automate the day-to-day management of your applications by executing critical activities, which may include scaling replicas, performing upgrades, and recovering from failures. For example, a PostgreSQL operator can ensure that standby servers are always deployed, that the database’s failover is correctly configured, and that data is backed up on schedule.

Security teams face a critical question: “What logs should we collect, and what detections should we enable to protect against threats targeting our industry?” For a bank in the northeast, this isn’t academic. Threat groups like FIN7, Lazarus Group, and Carbanak specifically target financial institutions with sophisticated attacks ranging from SWIFT compromise to ransomware.

If there is one truth I have learned working with financial services firms in 2025, it is this: AI is no longer optional, it is operational. From risk modeling to customer experience, algorithmic trading to automated compliance checks, AI is now embedded into the fabric of modern finance. But there is a second, quieter truth. AI only creates value when it is used responsibly, measurably, and at scale.