Operations | Monitoring | ITSM | DevOps | Cloud

As businesses scale, so do their observability needs, but many find themselves stuck with costly, inflexible platforms that no longer serve them. Despite mounting frustrations, the complexity of migration keeps companies from making a change. The risk of losing critical data, disrupting workflows, or rebuilding everything from scratch often outweighs the benefits of switching. Most vendors offer little to no migration support, forcing teams to manually reconfigure dashboards, alerts, and integrations.

Are you tired of feeling like you're in the dark about the services you're paying for? Are you getting what you paid for? Many businesses are in the same boat when it comes to Service Level Agreement (SLA) monitoring and reporting. It’s great having an SLA (or Service-Level Agreement) for the provision of a service, but you need to go further to really understand if the standards specified in the SLA are actually being met. That’s where SLA monitoring and reporting comes in.

Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware. Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance.

Linux log files are text-based records that capture system events, application activities, and user actions. They're stored primarily in the /var/log directory and provide essential information for debugging issues, monitoring system health, and maintaining security. This guide covers the most important Linux log files and a few detailed techniques for reading and analyzing them.

Pulling observability data together is rarely clean. Metrics come from everywhere, formats vary, and making sense of it takes some work. OpenTelemetry Collector and Prometheus fit perfectly here. The Collector handles ingestion and processing from different sources, while Prometheus stores and queries the data. Simple, effective, and no vendor lock-in. In this blog, we cover how to integrate the Collector with Prometheus, common pitfalls, and ways to control costs.

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings? New Relic did this for the Rails revolution, Datadog did it for the rise of AWS, and Honeycomb led the way for OpenTelemetry.

Migrating to a new logging platform can be a complex operation, especially when it involves both active and historical logs. Observability Pipelines offers dual-shipping capability, making it easy to route active logs to your new platform without disrupting your log management workflows. But migrating years worth of historical logs—which are critical for investigating security incidents and demonstrating compliance with applicable laws—requires a different approach.