Maple is an OTel-native observability platform. Traces, logs, and metrics all arrive through the open standard. No proprietary agents, no SDK lock-in.
OpenTelemetry is the CNCF open-source standard for collecting observability data from distributed systems. It provides a single, vendor-neutral set of APIs, SDKs, and tools to instrument your applications and export traces, logs, and metrics.
With support for every major language and framework, OTel has become the industry default for telemetry instrumentation, backed by contributions from hundreds of organizations.
Your instrumentation stays the same regardless of which backend you choose. Switch providers without touching application code.
Traces, logs, and metrics share a unified data model with correlated context. No stitching together separate tools.
Backed by the CNCF and hundreds of contributors. The spec is governed by the community, and no single vendor sets its roadmap.
Official SDKs for Go, Java, Python, JavaScript, .NET, Rust, and more. Auto-instrumentation is available for most frameworks.
As the industry converges on OTel, instrumentation you write today keeps working with whatever backend you use next.
Collectors, exporters, processors, and connectors: a pipeline you can shape to match your infrastructure.
Send telemetry over OTLP/HTTP, as protobuf or JSON. No translation layers, no proprietary formats.
Maple reads the OTel semantic conventions for HTTP, database, RPC, and messaging spans and builds its views from them.
Traces, logs, and metrics are each stored, queried, and correlated with the others.
Use the OpenTelemetry Collector to route, filter, and batch telemetry before sending it to Maple.
Works with every official OTel SDK. If it speaks OTLP, Maple can ingest it. No vendor-specific libraries required.
W3C Trace Context is supported. Spans and logs from different services correlate by trace ID.
Already using the OpenTelemetry Collector, auto-instrumentation agents, or OTel SDKs? Point your OTLP exporter at Maple with an ingest key. Nothing else in your pipeline changes.
Maple fits into the OTel ecosystem as a backend. It receives, stores, and visualizes the telemetry your existing pipeline produces. Your instrumentation stays as it is.
Add the SDK, point your OTLP exporter at Maple, and traces arrive.
maple.dev: observability on OpenTelemetry