Raygun Now delivers real-time error tracking and performance insights for modern development teams. This platform helps organizations detect, triage, and resolve issues faster across web, mobile, and serverless environments.
By surfacing high-impact exceptions and service disruptions instantly, Raygun Now supports faster incident response and more predictable releases. The following sections outline its core capabilities, product context, and operational guidance.
| Product Focus | Primary Users | Key Strength | Deployment Model |
|---|---|---|---|
| Real-time error tracking | Developers and SREs | Low-latency alerting | Cloud SaaS |
| Performance monitoring | Engineering managers | Breadth of platform support | Cloud SaaS |
| Release intelligence | DevOps leads | Deployment-aware grouping | SaaS with on-prem option |
| Workflow integrations | Support and product teams | Jira, Slack, PagerDuty | SaaS |
Real-time error detection
Raygun Now captures exceptions and failed requests the moment they occur. Its SDKs instrument applications to send detailed stack traces, environment context, and user impact data in real time.
Groups related issues automatically, reducing noise and helping teams focus on unique defects that affect users. Immediate visibility into error rates supports faster prioritization and prevents delayed reactions to critical problems.
Performance and latency insights
Beyond errors, the platform tracks request duration, page load time, and backend service latency. Detailed traces reveal slow queries, third-party bottlenecks, and client-side rendering issues that degrade user experience.
Interactive timelines make it easier to correlate performance shifts with deploys or infrastructure changes. Teams can set performance thresholds and receive alerts before minor slowdowns escalate into major incidents.
Release and deployment tracking
Raygun Now ties errors and performance metrics directly to specific releases and versions. This makes it simple to identify regression sources and understand which changes introduced instability.
Deployment-aware views also highlight improvements, enabling data-driven decisions about rollout cadence and risk management across multiple environments. The result is more controlled and predictable delivery cycles.
Integration and workflow automation
Built-in integrations connect incident signals with Jira, Slack, Microsoft Teams, and PagerDuty. Custom webhooks allow teams to route alerts into existing incident response and ticketing processes.
By linking error data with collaboration tools, Raygun Now shortens the gap between detection, assignment, and resolution. Engineering teams can respond faster while maintaining context for each incident.
Scaling observability with Raygun Now
Raygun Now serves as a central observability hub for distributed systems and rapidly evolving applications. Its design supports teams under pressure to maintain reliability while moving quickly.
Tracking errors, performance, and releases in one view gives leadership clear insight into engineering effectiveness and user experience quality.
- Enable real-time error capture with minimal SDK configuration
- Group and prioritize incidents using smart dedup and release context
- Leverage performance traces to identify and fix latency hotspots
- Automate on-call escalations through native integrations with Slack and PagerDuty
- Use version-based views to correlate regressions with specific deployments
FAQ
Reader questions
How quickly are new errors surfaced after deployment?
Raygun Now typically surfaces new errors within seconds, enabling near real-time incident detection after each release.
Can I filter issues by deployment version or environment automatically?
Yes, automatic grouping by version and environment lets you isolate issues introduced by specific deployments without manual filtering.
What integrations are most relevant for on-call response workflows?
Out-of-box integrations with Slack, PagerDuty, and Jira allow seamless escalation, assignment, and tracking during on-call rotations.
Does Raygun Now require code changes to enable detailed performance traces?
Most SDKs attach performance instrumentation automatically, though advanced configurations may need minimal initialization code for custom spans.