Use cases

Where Peermetrics fits in real WebRTC monitoring work

Use this page to connect Peermetrics to concrete operating realities: scale, call quality troubleshooting, and high-stakes production environments.

The strongest buyer language today is around WebRTC monitoring, observability, and production debugging. These examples help translate that into real deployment scenarios.

Featured article

WebRTC monitoring at scale

A practical article on how Peermetrics helps teams monitor real-time systems beyond green infrastructure dashboards and into actual call quality, session behavior, and user-side issues.

Good fit for teams comparing generic observability to RTC-specific monitoring.

Read the article
Featured article

Scaling telehealth video from 500 to 5000 sessions

A strong reference point for teams building in high-stakes environments where scaling, reliability, and troubleshooting become business-critical.

Good fit for healthcare, telehealth, and other production workloads where call failures are operationally expensive.

Read the case study

Common Peermetrics use cases

  • Debugging poor call quality in production when users report issues but infrastructure dashboards look normal.
  • Giving support teams session-level evidence instead of forcing escalation based on vague customer reports.
  • Monitoring WebRTC applications that mix browser behavior, client devices, networks, and SFU infrastructure.
  • Adding observability to self-hosted or hybrid SIP and WebRTC deployments where root cause is otherwise hard to isolate.
  • Supporting high-concurrency workloads such as telehealth, communications platforms, or embedded RTC features inside larger SaaS products.
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Integration path

OpenTelemetry and dashboard workflows

For teams that want RTC diagnostics inside an existing observability workflow rather than inside a separate silo.

Explore the OTel page
Developer workflow

MCP-style local tooling

For teams exploring how session diagnostics can flow into local developer tools and custom assistants.

Read the MCP page
AI-assisted workflow

Session data for better debugging handoffs

For teams that want grounded AI-assisted support summaries and engineering workflows without overpromising automation.

See the AI workflow page