Operations | Monitoring | ITSM | DevOps | Cloud

What remains of the 2025 AI hype? After a year of “AI will fix everything” promises, engineering teams in 2025 hit a wall of reality: AI is a tool, not a magic bullet. We’re now seeing a more practical approach: identifying broken workflows and tasks where AI can help and leveraging AI strengths like data analysis at speed and scale to derive meaningful, valuable insights. Looking ahead, 2026 will reward organizations that combine AI innovation with a practical approach.

Now available: Secure, real-time access to your observability data via Logz.io’s Model Context Protocol (MCP) Server. The Logz.io MCP Server brings your logs, metrics, and telemetry data into the Model Context Protocol (MCP), an emerging open standard that lets AI systems query real data securely and contextually, in real time. That means any MCP-compatible LLM, like Claude Desktop, Cursor, your own AI agent… can now connect directly to your Logz.io environment.

We’re expanding the Open 360 AI experience to more users with a modernized navigation and full access to Grafana and OSD dashboards. Your existing dashboards, alerts, bookmarks, and integrations remain unchanged, while new AI-powered capabilities provide deeper explanations and actionable insights. Existing customers can request early access through their account team.

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.

AI-first observability addresses two of the most pressing troubleshooting challenges: complex IT environments and AI-generated code. But understanding how to implement AI in a way that brings ROI, requires cutting through the hype and maintaining realistic expectations, while keeping a forward-thinking vision. In this blog post, we bring practical tips for including AI in your next observability RFP. The article is based on a webinar held with Logz.io founders, CEO Tomer Levy and CTO Asaf Yigal.

OpenTelemetry (OTel) is a unified framework of APIs, SDKs and tools, for collecting, processing, and exporting telemetry data (logs, metrics, and traces) across applications and infrastructure. OTel is especially required in today’s cloud-native world, where applications run on microservices, Kubernetes, and distributed systems.

Infrastructure monitoring is the process of continuously collecting, analyzing, and visualizing data from an organization’s IT infrastructure. With infrastructure monitoring, DevOps teams can maintain system health, meet SLAs, reduce downtime, and detect and resolve issues proactively. This ensures optimal performance, availability, and reliability. Key networks components infrastructure monitoring typically covers.