Maple review
A flexible OpenTelemetry stack spanning local, self-hosted, and cloud deployments.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Maple is an OpenTelemetry-based observability platform for distributed systems, aimed at developers and teams that want flexibility over deployment. It ingests traces, logs, metrics, browser sessions, and product events, then brings search, correlation, distributed tracing, dashboards, service maps, error tracking, session replay, Kubernetes monitoring, and alerting into one platform. Maple Local is a free single-binary stack for local development, while the Self-hosted plan runs on infrastructure controlled by the operator. Cloud provides a hosted option, and Enterprise targets higher-volume deployments.
The plan structure suits teams that want to start without a hosted commitment. Local costs $0 as a one-time plan and includes OTLP ingest, an embedded database, a query API, and a dashboard, with no account, cloud, or rate limits. Self-hosted also costs $0 as a one-time plan and gives operators control over retention and data residency. Cloud starts at $39 per month, includes 100 GB each of logs, traces, and metrics monthly, and provides 30-day retention. Enterprise is listed at $2,000 and adds higher volume, custom retention, priority support, and the option to run in the customer's cloud account.
Maple's ecosystem fit is strongest for teams already using OpenTelemetry, OTel Collector, Kubernetes, Docker, or Prometheus. Alerting connects with Slack, PagerDuty, Discord, email, and webhooks, while the REST API and MCP server support automation and AI clients. Distributed trace search, waterfall analysis, structured log search with trace correlation, and dependency mapping give the platform a broad investigation workflow rather than a narrow single-signal focus. Choose Maple when open-source deployment, operator control, and broad telemetry coverage matter. Teams that specifically need synthetic monitoring should consider another observability platform.
Maple pros and cons
- Where it wins
- Free Local and Self-hosted plans with no time limit
- OpenTelemetry ingestion for traces, logs, and metrics
- Service maps, session replay, Kubernetes monitoring, and alerting
- Where it doesn't
- Cloud pricing includes signal-volume limits and usage-based billing
- Self-hosted deployment requires the customer's own infrastructure
- No synthetic monitoring support
Maple fact sheet, pricing and score →
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