Operations | Monitoring | ITSM | DevOps | Cloud

At Logz.io, we speak with hundreds of companies every month. One thing is consistent across the board: everyone ships logs. But the challenges are equally common: What are the best practices for logging? How do we reduce noise? How should we architect our logs to make them truly useful? The reality is that logs are noisy for everyone. The best time to standardize your logging practices is when you write your first line of code—though that rarely happens. The second-best time is now.

Companies invest millions of dollars in observability platforms, yet they often still struggle to get application monitoring right. This is because most organizations focus on the technology, while neglecting the business. In this article, we’ll show you how to combine business requirements with technological needs. As the CTO of Logz.io, these are based on my experience working with global companies on their application observability needs.

Four months ago, we announced our vision of AI-first observability. Today, we’re not just talking about the future, we’re shipping it. This week marks a significant milestone with several major product announcements that demonstrate our continued momentum as the industry’s leading AI-first observability platform.

Organizations are adding GenAI to their current and future architectures and product roadmaps, requiring Ops teams to ensure LLMs are accurate, fast, secure and cost-efficient. LLM observability tools directly addresses these needs, helping identify and prevent common LLM errors and issues: LLM observability provides the telemetry data for this analysis. LLM observability tools trace requests end-to-end, evaluate outputs, and correlate quality with latency, cost, prompts, tools, and data sources.