Glances vs Herald

Glances is an open‑source, cross‑platform monitoring tool that provides a concise, real‑time view of system resources such as CPU, memory, disk I/O, network, and processes. It runs on Linux, Windows, macOS and can be accessed via a web interface, making it ideal for both local and remote monitoring. Designed for system administrators and DevOps teams, Glances aggregates metrics into a single dashboard, supports customizable alerts, and integrates with scripts or APIs for automated workflows. Its lightweight design ensures minimal overhead while delivering comprehensive performance insights.

Herald is an AI SRE (Site Reliability Engineering) tool designed to proactively detect and resolve potential issues within software systems before they impact users or trigger alerts. It achieves this by building a comprehensive context graph encompassing observability data, codebase, CI/CD pipelines, and documentation. This allows Herald to understand normal system behavior and identify anomalies with high accuracy, even for novel incidents that traditional threshold-based monitoring would miss. Once an anomaly is detected, Herald automatically investigates across code, infrastructure, and telemetry to pinpoint the root cause. This investigation process is iterative and data-driven, evaluating multiple hypotheses simultaneously and correlating signals from various sources. The system then presents the determined root cause, often with actionable remediation advice, significantly reducing the Mean Time To Resolution (MTTR) and preventing downtime. Developed by a team with expertise in AI, LLMs, and data systems from UC Berkeley's RISELab, Herald is trusted by leading tech companies. It offers a next-generation approach to incident management, moving beyond reactive alert handling to true proactive incident prevention and contextualized problem-solving, aiming to deliver results in days rather than months.

• Fully open‑source and free
• Runs on all major operating systems
• Lightweight with low resource overhead
• Extensible via plugins and API

• Proactive incident detection with 70%+ accuracy on novel incidents.
• Automated root cause analysis across code, infrastructure, and telemetry.
• Eliminates the need for manual threshold tuning and runbook creation.
• Learns from every investigation to improve accuracy and prevent recurring mistakes.

• No native mobile app
• Limited built‑in visualization compared to commercial tools
• Alerting requires manual configuration or external scripts

• Relatively new technology, reliance on advanced AI and LLMs may require a learning curve for some teams.
• Effectiveness may depend on the quality and completeness of the ingested observability data and codebase.
• Specific pricing details and integration complexity are not fully elaborated in the provided text.