Sazabi

What Sazabi does

Sazabi is an AI-native observability platform designed for fast-moving engineering teams. It aims to rethink observability from first principles, offering a conversational and intelligent approach rather than adding AI features to legacy tools. The platform provides autonomous alerts that require no setup, conversational debugging to interrogate applications in plain language, and tight integration with major coding agents. Sazabi learns from every incident and deployment, building institutional memory to make future alerts smarter and adapting baselines as systems evolve. It offers enterprise-grade security with SOC 2, ISO 27001, HIPAA, and GDPR compliance, along with data residency controls, end-to-end encryption, and RBAC.

Key capabilities

• Autonomous Alerts
• Conversational Debugging
• Coding Agents Welcome
• Code Search
• Dynamic Visualizations
• Perfect Memory
• Instrument in Minutes
• Works with your apps
• Bulletproof Security
• Zero setup required (for alerts)
• Smarter every day (memory)
• Ask anything (conversational debugging)
• Dynamic and interactive visualizations
• Multiplayer (for incidents)
• Command-line interface
• Query logs
• Search past threads
• Tail and forward logs
• Instrument your app (CLI)
• Manage projects

Use cases

• Getting rich, actionable alerts without setup
• Interrogating applications in plain language for debugging
• Integrating with coding agents like Claude Code, Codex, Cursor
• Pinpointing root causes from alerts to code in seconds
• Generating charts, tables, diagrams, and code blocks on the fly
• Learning from past incidents and traffic patterns
• Sending logs in any format from any cloud or technology
• Incident response in the flow of work
• Detecting sudden jumps in error rates
• Correlating error spikes with recent changes
• Catching query performance degradation
• Monitoring deployments end-to-end for failures
• Spotting silent failures (e.g., no webhooks processed)
• Surfacing UX issues before churn (rage clicks, repeated submissions)
• Tracking cloud spend and flagging anomalous cost spikes
• Learning from every outage to build institutional knowledge
• Adapting baselines as systems evolve
• Surfacing historical context for repeat incidents automatically
• Surfacing and filtering specific errors across services
• Connecting recent deployments or config changes to performance shifts
• Tracing failures back to their origin (root cause analysis)
• Assessing the impact of a bug (users, requests, regions affected)
• Visualizing metric trends (error rates, memory usage)
• Running health checks on services or databases
• Debugging 401 errors
• Investigating webhook authentication failures
• Debugging API key authentication issues
• Debugging session timeout loops
• Investigating database connection pool exhaustion
• Addressing slow queries due to missing indexes
• Troubleshooting database query timeouts
• Resolving database deadlocks
• Debugging connection leaks

AI approach

Sazabi is an AI-native observability platform that uses AI to provide autonomous alerts, conversational debugging, and integrate with coding agents. It learns from incidents and system evolution to adapt baselines and provide relevant historical context. The platform aims to rethink observability from first principles using AI, rather than adding AI features to legacy tools.

Tech named: Claude Code, Codex, Cursor, LangChain

What it says sets it apart

• AI-native observability platform
• Conversational interface (chat-based, not dashboards)
• Intelligent and self-configuring alerts
• Built for teams who ship fast
• Anticipates needs, surfaces insights, eliminates busywork
• Learns from every incident and deployment (perfect memory)
• Integrates tightly with major coding agents
• Enterprise-grade security (SOC 2, ISO 27001, HIPAA, GDPR, data residency, E2E encryption, RBAC)
• Rethinks observability from first principles, not adding AI to legacy tools
• Vision for software to heal and improve itself without human intervention

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.