Highlight.io is an open-source monitoring platform designed for modern developers who need to understand both frontend user behavior and backend system performance in one unified interface. It solves the fragmentation problem in traditional APM tools by combining session replay, error tracking, logging, and distributed tracing into a single, cohesive system that can be self-hosted or used via a hosted service.
Built with TypeScript and Go, Highlight.io provides SDKs for frontend and backend environments, supports Docker-based self-hosting, and integrates with popular tools via webhooks and plugins. It offers both a free hosted tier and scalable enterprise self-hosted deployments, making it accessible for startups and large teams alike.
What You Get
- Dom-based High-fidelity Session Replay - Captures every DOM change and user interaction using rrweb, enabling exact reproduction of user sessions to diagnose UI bugs and UX issues.
- Outgoing Network Request Inspection - Logs and displays all frontend HTTP requests with full payload data to identify failed API calls, slow responses, or misconfigured endpoints.
- Console Log Capture - Automatically collects and indexes console.error, console.log, and other browser console outputs from user sessions for real-time debugging.
- Customizable Error Grouping & Alerting - Groups similar errors using user-defined rules and sends alerts via email, Slack, or webhooks based on custom thresholds and frequency.
- Embedded Session Replay in Error & Log Views - Click any error or log entry to instantly view the associated user session, providing context for root cause analysis.
- Multi-language SDK Support - Official SDKs for JavaScript, React, Node.js, Python, Go, Ruby, and more, enabling seamless instrumentation across frontend and backend services.
- Distributed Tracing with Automatic Property Collection - Tracks request flows across microservices with automatic metadata injection and searchable trace timelines.
- Powerful Log Search with Contextual Embedding - Search logs by keyword, timestamp, or service, and instantly jump to related sessions and errors for deeper analysis.
- Integrations with Popular Tools - Connects with Slack, PagerDuty, GitHub, Sentry, Datadog, and others via documented APIs and webhooks for workflow automation.
Common Use Cases
- Debugging intermittent frontend bugs - A product engineer uses session replay to watch how users interact with a broken checkout button, seeing exact console errors and network failures that reproduce the issue.
- Monitoring microservice performance - A DevOps team deploys Highlight.io across 20+ Go and Node.js services to trace slow requests, correlate logs, and identify failing dependencies in real time.
- Reducing customer support tickets - A SaaS company links error events to user sessions, allowing support agents to replay what the customer experienced before reporting an issue.
- Post-deployment regression detection - A full-stack developer sets up alerting on new error spikes after a release, then uses embedded session replay to validate if users are impacted.
Under The Hood
Architecture
- Monorepo structure using Yarn Workspaces to coordinate 20+ interconnected packages across frontend, backend, SDKs, and tooling, enabling atomic releases and shared configuration
- Clear separation of concerns through framework-specific SDKs that encapsulate instrumentation logic via middleware and plugin interfaces, ensuring non-intrusive integration
- Backend and frontend codebases are fully decoupled, communicating via well-defined APIs rather than shared code, with Antlr-generated parsers maintaining clean boundaries between parsing and application layers
- Turbo monorepo build system enforces consistent pipelines, while Husky and Prettier ensure uniform code quality standards across all modules
Tech Stack
- Go-based backend services with ANTLR-generated parsers for search functionality, integrated with Makefile-driven deployment workflows
- Next.js frontend with React, TypeScript, and TurboRepo for orchestration, leveraging React Server Components and GraphQL codegen for data fetching
- Extensive SDK ecosystem for instrumentation across frameworks like React, Next.js, NestJS, and Hono, with Redis for caching and PostgreSQL for persistent storage
- Cypress and @testing-library for end-to-end and unit testing, supported by Doppler for environment management and Vercel for deployment
- OTLP endpoint for observability telemetry ingestion, with Yarn 4 and node-modules linker enabling efficient dependency resolution
Code Quality
- Comprehensive test coverage across unit, integration, and end-to-end layers with industry-standard frameworks and clear separation between test suites
- Consistent, domain-driven naming conventions and explicit behavior-focused test descriptions that enhance readability and maintainability
- Robust error handling with custom error classes and structured validation, particularly in data extraction and retrieval logic to prevent malformed state propagation
- Strong type safety and runtime validation in Go and Java codebases, complemented by comprehensive linting and standardized assertion patterns
- Modular code organization with well-defined boundaries between alerting, tracing, and storage components, reducing coupling and improving testability
What Makes It Unique
- Native integration of session replay with full-stack error monitoring and logging in an agent-less architecture, unifying fragmented observability tooling
- Advanced DOM recording that captures Shadow DOM, Canvas, and WebGL elements to accurately reproduce complex modern web applications
- AI-powered error suggestions with visually rich, thematically styled UI that contextualizes stack traces within live user session data
- Open-source-first philosophy with transparent feature comparisons that build community trust and drive organic adoption
- Extensible UI component system using Vanilla Extract for themable, type-safe CSS-in-TypeScript patterns that ensure design consistency
- Embedded competitive intelligence within the product interface, directly articulating unique value propositions to users through structured competitor analysis