# Maple

By **makisuo** · Software & Tools

Open-source OpenTelemetry observability platform with traces, logs, metrics, service maps, session replay, and an AI agent MCP server, for backend and platform teams.

- Source: https://maple.dev
- Repository: https://github.com/Makisuo/maple
- Install: `bun install # Local bundle path from README brew install Makisuo/tap/maple maple start`
- Tags: observability, open-source, opentelemetry, self-hosted, mcp, agent, kubernetes, clickhouse
- Pricing: freemium
- Upvotes: 1

## Features

- OpenTelemetry-native OTLP ingest with no proprietary agent
- Distributed trace waterfall with span attribute inspection
- Structured log streaming with severity, service, and regex search
- Real-time service map showing request rates, error rates, and latency per edge
- Browser session replay linked to backend trace IDs via shared session ID
- Kubernetes monitoring via Helm chart with pod, node, and kube-state metrics
- First-class MCP server for AI agent access to telemetry, error diagnosis, and PR creation
- Alerting worker and mobile app included in the monorepo

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## Why it matters
Modern observability platforms charge per host, per seat, and per event, layers that compound fast on any real workload. Maple inverts that model: pay per GB ingested, no per-host or per-seat fees, source available under FSL-1.1 on GitHub.

## The big picture
Maple is an end-to-end observability backend for distributed systems. It collects traces, logs, and metrics over standard OTLP, stores and queries them at ClickHouse speed, and surfaces them through a unified dashboard covering service maps, trace waterfalls, structured log streams, and Kubernetes pod heatmaps.

## How it works
Send OTLP from any instrumented service to Maple's ingest gateway. The gateway applies key auth and org enrichment, then forwards to the collector. From there, ClickHouse handles storage and query. The frontend, built on TanStack Router, renders live service maps, trace trees, and metric charts. Dashboards are persisted in SQLite (local) or Turso (cloud). Per the README, the local binary bundles OTLP ingest, embedded ClickHouse, and the dashboard in a single `maple start` command installable via Homebrew.

## Zoom in
The MCP server is a first-class feature, not an afterthought. Any compatible AI agent can call `list_services`, `find_errors`, `error_detail`, and `propose_fix` over the open MCP protocol. The site demo shows an agent diagnosing a Stripe idempotency collision and opening a PR in 5 tool calls across 4.2 seconds.

## Yes, but
FSL-1.1 is not OSI-approved open source. It restricts certain competitive uses until the code converts to Apache 2.0 (the timeline is set by the license terms, not a public date). Teams with strict open-source procurement policies should review the FSL terms before adopting.

## The bottom line
Maple is the observability stack to reach for when you want OTel-native ingest, honest GB-based pricing, and an AI agent that can read your telemetry and act on it, all from a codebase you can fork and self-host.

## FAQ

### What is Maple?

Maple is an open-source observability platform for distributed systems that collects and queries traces, logs, and metrics over OpenTelemetry. It is backed by ClickHouse for fast analytical queries and ships with a service map, browser session replay, Kubernetes monitoring, and a first-class MCP server for AI agents. The source is available on GitHub under the FSL-1.1 license, which converts to Apache 2.0 over time. It can be used as a hosted service or self-hosted on your own infrastructure.

### How do I install and run Maple locally?

The fastest path is the local binary: run `brew install Makisuo/tap/maple` then `maple start`, which spins up OTLP ingest, embedded ClickHouse, and the dashboard as a single process. For a full development stack, clone the monorepo, run `bun install` (Bun >= 1.3 required), then `bun run dev` to start all apps together. A Docker Compose file is also provided for a multi-service local stack with the API on port 3472, the web UI on 3471, and the OTLP collector on ports 4317 (gRPC) and 4318 (HTTP).

### Is Maple free or open source?

Maple's source code is publicly available on GitHub under the Functional Source License (FSL-1.1), which grants broad rights to read, fork, and self-host while restricting certain competitive uses. Per the site FAQ, the license converts to Apache 2.0 over time. The hosted version starts with a free trial; paid plans are billed at a flat $0.30/GB ingested with no per-host or per-seat fees.

### What is Maple best suited for?

Maple is best for backend and platform teams already using OpenTelemetry who want a single backend for traces, logs, and metrics without vendor lock-in or compounding per-host fees. It particularly shines when you need AI agents to interact with live telemetry over MCP, the built-in MCP server lets an agent list services, search for errors, inspect root causes, and open pull requests autonomously. Kubernetes teams benefit from the Helm chart that joins pod and node metrics directly to spans.

### How does Maple compare to Datadog, New Relic, and Grafana Cloud?

Maple's published pricing table shows Datadog charges $15+/host/month plus $1.27/M events with no self-host option and only partial OTel support, while New Relic charges $99/full-user/month on top of ingest fees. Grafana Cloud is closer in OTel support but uses AGPL components and has no first-class MCP/agent surface. Maple's main advantages per the sources are: no per-host or per-seat fees, native OTel (no proprietary agent), a self-host path, and the MCP server for AI agent access. The FSL-1.1 license is Maple's main procurement caveat versus Grafana's AGPL.

### What are Maple's key limitations?

FSL-1.1 is not an OSI-approved open-source license, which may block adoption under strict open-source procurement policies. The self-hosted mode removed multi-tenant JWT/API-key paths in a breaking change, and MAPLE_ROOT_PASSWORD is now required for self-hosted deployments. Private ingest keys are encrypted at rest with a customer-supplied key, so losing that key means losing access to the keys. The monorepo requires Bun >= 1.3, which may not fit all existing toolchains.

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