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The latest News and Information on Observabilty for complex systems and related technologies.

How to Reduce Your Cloud Costs with Coroot

Cloud costs often grow quietly until they suddenly command everyone’s attention. Gartner estimates that companies overspend on cloud services by up to 70 percent, mostly because they lack clear visibility into where the money is actually being spent. Cloud invoices speak the language of infrastructure: nodes, instance types, regions, volumes, and egress. Engineering teams speak the language of services, deployments, and code.

AI and DevOps in 2025: How Autonomous Engineering Will Transform Software Operations and Reliability

DevOps started as a way to break down barriers between development and operations, but by 2025 the movement has shifted into something far more ambitious. Instead of simply speeding up releases or tightening workflows, companies are now adopting autonomous engineering systems-tools powered by AI that don't just support DevOps practices but actually carry them out.

What's Special About MCP?

AI agents can interact with the world using tools. Those tools can be generic or specific. For example: Generic: Specific: The most general ones, like “run a bash command” and “read and write files” are built into the agent. More specific ones are provided through Model Control Protocol (MCP) servers. Every tool provided to the agent comes with instructions sent as part of the context.

How to Monitor Java Applications on Windows with SolarWinds Observability | APM Setup Guide

This video provides a step-by-step walkthrough for configuring monitoring for Java applications running on Windows using SolarWinds Observability. The demonstration covers the complete process—from adding a new service to instrumenting the application with the Java APM library and verifying connectivity. Topics covered in this video include: This guide is designed for developers, DevOps engineers, and system administrators who need to instrument Java applications on Windows for performance monitoring, distributed tracing, and full-stack observability.

Side-by-Side Variable Comparison for Snapshot Debugging

When you’re debugging a tricky issue in a distributed system, “what changed?” is often the most important question. You add logs, you capture data, you redeploy, and suddenly your browser is full of open tabs, copied JSON blobs, and screenshots of log lines. Comparing behavior between two requests, two users, or two releases turns into a manual, error-prone chore. Lightrun Snapshots were built to fix the data collection side of that story.

Top 7 Observability Platforms That Auto-Discover Services

You can use an observability platform that automatically discovers your services and provides ready-to-use dashboards with minimal setup. If you're running a system where microservices come and go, containers shift around, or serverless functions scale up quickly, this kind of experience saves you a lot of time. You gain visibility as soon as something goes live, without requiring any additional steps on your part. In this blog, we talk about the top seven platforms that offer these capabilities.

Data Observability: Build confidence in the data life cycle

Datadog Data Observability provides a complete solution with quality checks (e.g., volume, row changes, freshness), custom SQL-based monitors, anomaly detection, column-level lineage across systems like Snowflake and Tableau, full pipeline visibility, and targeted alerts when data issues arise.

Search Telemetry Without Limits in a Multi Cloud and AI World

Cribl Search gives you one lens across all your telemetry data no matter where it lives. Instead of forcing teams to move data into one system or jump between tools, you get a familiar pipe based query experience with dashboarding and alerting built in. Storage and query processing stay separate so you decide where your data lives while your users get fast, simple access in one place.

Use OpenTelemetry with Observability Pipelines for vendor-neutral log collection and cost control

Today, many DevOps and security teams operate in a world of complex, hybrid, or multi-vendor environments. As more teams look to avoid lock-in by adopting open standards, OpenTelemetry (OTel) is quickly gaining adoption as the primary open source method for DevOps and security teams to instrument and aggregate their telemetry data. However, OTel alone may lack the advanced processing functions, native volume control rules, and hybrid environment support that large organizations need.

AI Observability: How to Keep LLMs, RAG, and Agents Reliable in Production

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.