Operations | Monitoring | ITSM | DevOps | Cloud

We're proud to share that Elastic has been named a Leader in the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment (doc, November 2025). We believe this recognition validates our ongoing mission: to deliver an observability platform that is open, extensible, and AI-driven to power full-stack observability that unifies operational and business data at scale, allowing SRE teams to move from detect and resolve problems faster.

Observability works best when everyone who needs visibility can get it without the risk of unintentional changes. Honeycomb’s role-based access control system helps teams strike that balance with a selection of Owner, Member, and Read-Only member roles. This control gives teams more flexibility in how they share access across their organization, helping you scale visibility safely without sacrificing control.

AI in network operations is evolving faster than ever. But while new models and agents are emerging almost daily, they’re often working alone, with each confined to its own context, data, and domain. One model might analyze telemetry, another handles automation scripts, and a third generates summaries or recommendations. Each model might be intelligent on its own, but without a way to share context, they end up thinking in isolation, limiting what they can achieve together.

The OpenTelemetry Java Agent provides zero-code instrumentation for Spring Boot applications through bytecode manipulation. This guide covers setup, configuration, auto-instrumentation capabilities, and production deployment strategies for implementing distributed tracing and observability.

It’s widely acknowledged that most application performance problems stem not from the application itself, but from the underlying database. Slow or inefficient database queries are often the primary cause of these issues, acting as the biggest driver of application performance incidents. If you’ve been troubleshooting slow API calls or sluggish services, chances are the root cause likely resides within your database layer.

A decade ago, network monitoring was straightforward. You had a data center, some branch offices, MPLS circuits connecting everything, and a handful of applications running on-premises. Set some SNMP thresholds, configure a few alerts, and you were covered. When something broke, the problem was usually obvious: a failed switch, a saturated link, a misconfigured router. Today's networks bear zero resemblance to that world.

Model Context Protocol (MCP) gives large language models (LLMs) a secure way to interact with your Graylog data and workflows. Instead of writing complex queries, you can ask questions in plain English, such as: Analysts gain speed, administrators maintain control, and security stays intact.

I want to challenge a deeply held belief in our industry, one that I once championed myself: the idea that more data is the answer. We've spent a fortune building vast data lakes of network telemetry, believing that if we could just collect everything, we would achieve a state of operational nirvana.

Does it ever feel like your network operations team is constantly running, yet always struggling to keep up? The ticket queues are long, troubleshooting is a complex detective story, and every new application deployment adds another layer of anxiety. This constant state of reactive firefighting isn't a sign of a bad team; it's the symptom of a broken process. This operational friction, the invisible tax on every action your team takes, has a name: waste.

We’re currently at KubeCon + CloudNativeCon North America 2025 in Atlanta, and it’s a great opportunity to connect with the community and share some of the progress we’ve made this year. It’s been a busy period of development, new releases, and community engagement, all guided by our focus on delivering simple, reliable, and efficient monitoring & observability solutions.