Sazabi is an AI-native observability platform that replaces manual monitoring with autonomous alerts, conversational debugging, and seamless integration with AI coding agents. It ingests logs in any format, automatically diagnoses root causes, and surfaces actionable insights in plain language to reduce alert fatigue and accelerate incident response. The platform learns from every incident, building institutional memory that correlates commits, deploys, support tickets, and team chats into a single timeline.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional observability tools require teams to manually configure monitors, navigate complex dashboards, and dig through mountains of telemetry to diagnose issues. This legacy approach creates alert fatigue, misses critical signals, and forces engineers to spend valuable time firefighting instead of building software.
Solution
Sazabi provides an AI-native observability platform that works for engineers rather than requiring them to work for it. The platform automatically ingests logs from any source, learns what is normal for each system, and generates rich, actionable alerts without any setup or configuration. Users can interrogate their applications in plain language through a conversational chat interface, receiving instant answers about root causes, impact, and recommended fixes. Sazabi integrates tightly with major AI coding agents like Claude Code, Codex, and Cursor, allowing it to not only diagnose issues but also launch remediation agents that open pull requests with fixes directly. The platform maintains a continuously learning memory of past incidents, enabling it to surface historical context and accelerate future troubleshooting.
Target Audience
Primary customers are fast-moving engineering teams and product engineers at startups and scaling companies who need to ship quickly without dedicated observability specialists. The platform is designed for teams that use modern AI coding tools and want a conversational, low-friction approach to production monitoring and incident response.
Features
- Autonomous alerts that self-configure, learn system baselines, and escalate only when issues genuinely matter, eliminating false positives and manual monitor tuning
- Conversational debugging interface that lets engineers ask questions in natural language and receive precise answers with code search capabilities pinpointing exact files, commits, or lines responsible
- Native integrations with AI coding agents including Claude Code, Codex, and Cursor, enabling automatic incident remediation via cloud agents that open pull requests
- Cross-source correlation engine that unifies commits, deploys, errors, support tickets, and Slack conversations into a single event timeline
- Error clustering that groups thousands of related failures into a single root-cause alert
- Dynamic visualizations that generate charts, tables, diagrams, and code blocks on demand rather than static dashboards
- Built on a "logs are all you need" architecture that reconstructs metrics and traces from log events, supported by automatic instrumentation capabilities