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Maple vs Grafana

Grafana is a great visualization layer, but building a full observability stack means configuring and maintaining Loki, Tempo, Mimir, and Prometheus separately. Maple gives you a unified platform where traces, logs, and metrics work together out of the box.

Feature Maple Grafana
Pricing model
Usage-based, transparent
Usage-based + self-host costs
Open source
FSL-1.1 → Apache 2.0
OpenTelemetry native
Distributed tracing
Log management
Metrics & dashboards
AI / MCP integration
Self-hosting available
No vendor lock-in
Custom dashboards
Alerting
Why choose Maple

Key advantages over Grafana

Unified platform

No need to juggle Loki for logs, Tempo for traces, and Mimir for metrics. Maple combines all three signals in a single, cohesive platform.

Simpler self-hosting

Deploy one service instead of managing multiple data stores, each with their own scaling and configuration requirements.

AI & MCP integration

Built-in AI-powered diagnostics and MCP tool integration for automated root cause analysis — not available in the Grafana stack.

Native OpenTelemetry

Purpose-built for OpenTelemetry from day one. No adapters, no translation layers, no compatibility surprises.

Built-in alerting

Alerting is integrated into the platform, not bolted on as a separate component that needs its own configuration.

Zero configuration

Start ingesting data in minutes. No PromQL to learn, no storage backends to tune, no data source connections to wire up.

오늘 첫 트레이스를 확인하세요.

SDK를 추가하고 OTLP를 Maple로 향하게 하면 트레이스가 도착합니다 — 대부분 5분 이내에 설정됩니다.

maple.dev — OpenTelemetry 위의 옵저버빌리티