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About
Raygun gives engineering teams complete visibility into how users experience their software across web and mobile applications. Its three core pillars — Crash Reporting, Real User Monitoring, and Application Performance Monitoring — work together to surface issues before they impact revenue or user retention. The standout AI Error Resolution feature automatically prompts a connected LLM with rich contextual data from stack traces, environment details, and affected code, delivering fast, accurate fix suggestions without manual investigation. Raygun's Crash Reporting can reduce checkout errors by up to 90% and cut MTTR to as low as four minutes, making it especially powerful for e-commerce teams running on platforms like Shopify, Azure, or Heroku. Real User Monitoring provides granular front-end performance data including Core Web Vitals scores, enabling engineers, analysts, and SREs to pinpoint bottlenecks and optimize page response times — even sub-second improvements that measurably increase conversion rates. The APM layer adds unrivalled server-side visibility for backend teams. Raygun is used by developers, CTOs, and product managers across e-commerce, media, and software technology verticals. It integrates with deployment pipelines, alerting systems, and third-party tools, and includes dashboards, Content Security Policy monitoring, and API specifications to support modern DevOps workflows.
Key Features
- AI Error Resolution: Automatically prompts your chosen LLM with stack trace context, environment data, and affected code to generate fast, accurate fix suggestions.
- Crash Reporting: Detects and diagnoses application crashes across web and mobile with deep error grouping, reducing checkout errors by up to 90% and MTTR to as low as 4 minutes.
- Real User Monitoring (RUM): Tracks front-end performance metrics including Core Web Vitals and page response times from real user sessions to identify and eliminate bottlenecks.
- Application Performance Monitoring (APM): Provides deep server-side visibility into application performance, tracing slow queries, external calls, and backend bottlenecks.
- Integrations & Deployment Tracking: Connects with Shopify, Azure, Heroku, and CI/CD pipelines to correlate deployments with error spikes and performance regressions.
Use Cases
- An e-commerce team uses Raygun's Shopify Crash Reporting to monitor their checkout flow, reducing cart abandonment caused by errors and cutting MTTR from hours to minutes.
- A SaaS company's engineering team leverages AI Error Resolution to automatically diagnose production crashes and receive LLM-generated fix suggestions, speeding up their release cycle.
- A front-end performance engineer uses Real User Monitoring to identify pages with poor Core Web Vitals scores, then optimizes load times to improve Google search rankings and conversion rates.
- A DevOps team uses APM alongside deployment tracking to correlate a backend performance regression with a specific release, quickly rolling back or patching the responsible change.
- A CTO uses Raygun dashboards and alerting to maintain visibility across multiple applications, ensuring SLA compliance and proactively addressing issues before users report them.
Pros
- AI-Accelerated Debugging: The AI Error Resolution feature significantly reduces investigation time by providing LLM-generated fix suggestions enriched with full error context.
- Comprehensive Monitoring Suite: Combines crash reporting, front-end RUM, and back-end APM in a single platform, eliminating the need for multiple monitoring tools.
- E-Commerce Ready: Deep Shopify integration and checkout-specific crash reporting make it especially valuable for online retailers aiming to protect revenue.
- Actionable Performance Data: Core Web Vitals tracking and granular RUM data translate directly into conversion rate improvements, linking technical metrics to business outcomes.
Cons
- Primarily a Paid Tool: Raygun operates on a paid subscription model with no permanently free tier, which may be a barrier for small teams or individual developers.
- Steeper Learning Curve for Full APM: Getting full value from APM and RUM together can require significant setup and configuration, especially for complex multi-service architectures.
- LLM Dependency for AI Features: AI Error Resolution requires connecting your own LLM, adding an external dependency and potential extra cost for teams wanting AI-assisted debugging.
Frequently Asked Questions
What is Raygun used for?
Raygun is used to monitor application health and user experience across web and mobile apps. It provides crash reporting, real user monitoring, and application performance monitoring to help teams detect and fix issues fast.
How does Raygun's AI Error Resolution work?
AI Error Resolution automatically collects contextual data from your error — including the stack trace, environment info, and affected code — and sends it as a prompt to your configured LLM (such as GPT-4 or Claude). The LLM returns targeted fix suggestions directly in the Raygun dashboard.
Does Raygun support Shopify stores?
Yes. Raygun offers dedicated Crash Reporting for Shopify, helping e-commerce teams identify and resolve checkout errors that cause cart abandonment, reducing revenue loss.
What is the difference between RUM and APM in Raygun?
Real User Monitoring (RUM) focuses on front-end performance as experienced by actual users — page loads, Core Web Vitals, and browser-side metrics. Application Performance Monitoring (APM) covers server-side performance, including backend response times, database queries, and external service calls.
Is there a free trial available?
Yes, Raygun offers a free trial so teams can evaluate its crash reporting, RUM, and APM capabilities before committing to a paid plan.
