Overview#
Performance problems that reach users have already cost you. The Performance Dashboards module analyses metrics across frontend, backend, database, and infrastructure layers continuously, surfacing degradation patterns before they produce visible symptoms. Teams that act on anomaly alerts rather than user complaints resolve issues faster and with less operational pressure.
Key Features#
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Real-Time Performance Monitoring: Live dashboards display response times, throughput, error rates, and resource utilisation across all platform components. Customisable widgets support time-series charts, heat maps, percentile distributions, and comparison tables tailored to your team's needs.
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AI-Powered Anomaly Detection: Machine learning models continuously analyse performance patterns to identify regressions and anomalies. Automated alerts notify the team when performance deviates from established baselines, not just when hard thresholds are crossed.
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Multi-Layer Visibility: End-to-end observability from client-side browser performance through API gateway, backend services, and database layers. Distributed tracing connects related operations across the full request lifecycle so root causes are visible rather than hidden.
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Executive Dashboards: High-level KPI views for leadership showing platform health, user experience scores, uptime metrics, and trend analysis at a glance without requiring technical interpretation.
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Engineering Dashboards: Deep technical analysis views with granular metrics, performance breakdowns by endpoint and service, slow query identification, and resource utilisation patterns for detailed diagnostics.
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Automated Optimisation Recommendations: The system analyses performance data and suggests specific optimisations for code, infrastructure scaling, caching strategies, and architectural improvements with estimated impact.
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Customisable Alerts: Configure threshold-based and anomaly-based alerts for any metric. Route notifications through email, Slack, PagerDuty, or webhooks with configurable severity levels and escalation paths.
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Historical Analysis: Compare performance across time periods, releases, and configurations. Identify trends, seasonal patterns, and the specific impact of deployments on system performance.
Dashboard Types#
- Platform Overview: Overall health, uptime, active users, and key business metrics
- API Performance: Endpoint response times, throughput, error rates, and availability
- Database Performance: Query performance, connection utilisation, and optimisation opportunities (PostgreSQL primary)
- Infrastructure Health: Resource utilisation, capacity planning, and scaling indicators
- User Experience: Page load times, client-side performance, and user journey analytics
- Custom Dashboards: Build tailored views with drag-and-drop widgets for specific monitoring needs
Use Cases#
- Proactive issue resolution by detecting performance degradation before it impacts users through anomaly detection and intelligent alerting.
- Capacity planning using historical trends and growth patterns to plan infrastructure scaling ahead of actual demand.
- Release validation by comparing pre- and post-deployment performance metrics to identify regressions introduced by new releases quickly.
- SLA monitoring with real-time tracking of service level objectives and automated reporting for stakeholders.
- Cost optimisation by identifying over-provisioned resources and inefficient operations through utilisation analysis.
Integration#
- APM Tools: Integrates with New Relic, Datadog, and Dynatrace for comprehensive application performance data.
- Log Platforms: Correlate performance metrics with log data from your existing logging infrastructure.
- Infrastructure Monitoring: Connect with Prometheus, Grafana, and cloud-native monitoring for infrastructure-level metrics.
- Alerting Channels: Route alerts through email, Slack, Microsoft Teams, PagerDuty, and custom webhooks.
Open Standards#
- GraphQL (June 2018 specification): all dashboard metric queries, mutations, and real-time subscriptions are served through a GraphQL API conforming to the GraphQL specification.
- OAuth 2.0 (RFC 6749) / JSON Web Tokens (RFC 7519): every dashboard endpoint validates a Bearer JWT and enforces fine-grained permission scopes per the OAuth 2.0 scope model.
- OpenTelemetry (OTLP) / W3C Trace Context: the platform registers W3C Trace Context middleware so distributed trace headers are propagated across every dashboard API call for end-to-end latency attribution.
- Prometheus / OpenMetrics exposition format: a metrics endpoint in the OpenMetrics text exposition format is mounted at startup; pool-health and request-latency gauges are scraped by Prometheus and feed the infrastructure and API performance dashboard panels.
- EU Directive 2022/2555 (NIS2): the cyber-incident panel enforces the Directive's 72-hour full-notification deadline, surfacing overdue incidents and tracking early-warning and formal-notification timestamps.
- ISO 8601 / RFC 3339 date-time format: all metric timestamps, audit entries, and API responses use ISO 8601 strings with UTC offset, conforming to the ISO 8601 / RFC 3339 interchange format.
Availability#
- Enterprise Plan: Included (all dashboards, AI anomaly detection, custom dashboards)
- Professional Plan: Core performance metrics included; advanced analytics and AI recommendations available as add-on
Last Reviewed: 2026-02-05 Last Updated: 2026-04-14