Highlight.io is an open-source, full-stack monitoring platform combining session replay, error tracking, logging and feature flags with AI-powered insights.
Highlight gives developers everything needed to understand what happens in production: session replay showing exactly what users did, error monitoring with full stack traces, log management and distributed tracing — all open source and self-hostable. Its AI features summarize errors, suggest fixes and surface anomalies across the telemetry data.
Because it is open source, teams can inspect, extend and host Highlight themselves, avoiding vendor lock-in and data sovereignty issues. Modern features like feature flags, AI agent monitoring and product analytics round out a platform aimed at small teams that want big-company observability affordably.
Highlight’s stack covers session replay, error monitoring, logging, tracing, feature flags and product analytics — all open source and self-hostable. Developers replay user sessions with full technical context, and AI summarizes errors and logs to accelerate diagnosis. Modern workloads are covered too, including monitoring for AI agents and LLM features. SDKs support major web, mobile and backend frameworks.
Highlight offers a free tier with usage limits, usage-based paid plans, and a free open-source version for self-hosting.
Startups and engineering teams that want full-stack visibility with the flexibility and economics of open source.
Yes — free tier in the cloud, and the entire platform is free when self-hosted.
Yes — Highlight exposes APIs and SDKs for errors, logs, traces and session data.
Open source plus an integrated session replay/observability suite beats assembling separate closed tools.
Yes — Highlight is open source and can be self-hosted entirely, in addition to its managed cloud offering.
Highlight.io is used by teams that want to understand production behavior without assembling multiple vendors. Developers replay user sessions to reproduce bugs, monitor errors with full stack traces, and search logs with AI assistance. Product managers use the analytics and replay to see how features are used. Feature flags let teams ship safely, and monitoring for AI agents covers LLM-powered features. Startups favor it for its generous free tier and open-source option, while enterprises value self-hosting and data control.
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