Cycling through all six. Tap any point to stop.

Measured on six things

Open-source observability platform built on OpenTelemetry.

Security

No HTTPS enforcement, no HSTS, no CSP, no frame protection — only SPF and DMARC pass. Open-source and self-hosting are pluses, but basic security headers are missing.

Screenshots

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Maple

About this app

Maple is an open-source observability platform built natively on OpenTelemetry. It unifies distributed tracing, log management, metrics dashboards, error tracking, browser session replay, and alerting in a single interface. Logs correlate to traces automatically, a live service map surfaces bottlenecks, and an AI/MCP integration lets you query your telemetry in natural language. Flat per-GB pricing, no proprietary agents, and you can read the source.

Key features

1OpenTelemetry-native: ingest traces, logs, and metrics via OTLP with no proprietary agents.
2AI/MCP integration: query telemetry in natural language via MCP-compatible clients like Claude or Cursor.
3Flat pricing: $39/month for 100 GB per signal, then $0.30/GB, with no per-host or per-seat fees.

Target customer profile

Drawn from the product itself, not from a survey.

Demographic

Developers and SREs at startups (10-100 employees)

Pain points

High costs of traditional observability tools, complex setup, lack of granular data, difficulty correlating logs/traces/metrics.

Primary needs

Affordable, easy-to-deploy observability with deep context to debug quickly and efficiently.

Demographic

Platform engineering teams in mid-sized companies (100-1000 employees)

Pain points

Vendor lock-in, proprietary agents, data privacy concerns, limited AI integration.

Primary needs

An open, OpenTelemetry-native solution that allows self-hosting, integrates with Kubernetes, and lets AI agents assist in incident response.

Demographic

Tech leads and CTOs evaluating cost-effective observability solutions

Pain points

Complex pricing models, hidden costs, spending more on observability than necessary.

Primary needs

Transparent pricing, predictable billing, and a tool that offers both cloud and self-hosted options.

Scout Review

Written by AI from measured evidence, scored out of 100.

Maple is a promising open-source observability platform that leverages OpenTelemetry and ClickHouse for fast queries, with a strong focus on AI integration and transparent pricing. The design is polished, and usability is solid with good documentation. However, mobile performance and security headers need improvement. Accessibility is decent but has room for refinement. With a clear differentiator in AI/MCP and a competitive pricing model, Maple has good growth potential, but it must address these gaps to compete effectively with established players.

Measure by measure

Usability

Clear CTAs, intuitive sidebar navigation, and a solid docs site with search and language guides. Onboarding via free trial and local binary lowers friction.

Design

Polished dark theme with orange accents, real product screenshots, but empty black rectangles in feature sections and zero-value dashboard previews undercut the visual impact.

Speed

Desktop performance is excellent (97/100), but mobile LCP of 5.5s is sluggish. ClickHouse backend promises sub-second queries, but site load on mobile needs work.

Security

No HTTPS enforcement, no HSTS, no CSP, no frame protection — only SPF and DMARC pass. Open-source and self-hosting are pluses, but basic security headers are missing.

Accessibility

Lighthouse score 88/100, but fails on contrast, heading order, missing main landmark, and accessible names. No a11y statement, but site is keyboard-navigable and responsive.

Growth

OpenTelemetry-native with AI/MCP integration, flat pricing, and self-hosting options. Targets cost-conscious startups and SREs, with a clear wedge against expensive incumbents.

Improvement areas

Security headers: enforce HTTPS, add HSTS, CSP, and frame protection to meet industry standards.
Mobile performance: reduce LCP from 5.5s to under 3s for better user experience.
Accessibility: fix contrast, heading order, and add a main landmark to improve screen reader support.

Design & Usability

Maple presents a cohesive dark-themed design with a strong orange accent, and the dashboard preview shows real product UI with metric cards and graphs, which builds credibility. However, the use of empty black rectangles as placeholders in feature sections detracts from the overall polish. Usability is a highlight: the primary CTA is prominently placed, the sidebar navigation is intuitive, and the docs are comprehensive with search and per-language guides. The free trial and local binary lower the barrier to entry, making it easy for developers to get started quickly.

Speed & Security

Performance is a mixed bag: desktop scores a near-perfect 97/100, but mobile lags with an LCP of 5.5 seconds, which could frustrate on-the-go users. The ClickHouse backend promises sub-second queries, which is a strong selling point for large datasets. Security, however, is a concern: the site lacks HTTPS enforcement, HSTS, CSP, and frame protection, leaving it vulnerable to common attacks. While the open-source nature and self-hosting option are positive, the missing security headers are a significant gap for a platform handling sensitive telemetry data.

Accessibility & Growth

Accessibility is decent with an 88/100 Lighthouse score, but fails on contrast, heading order, and missing main landmark, which could hinder screen reader users. No accessibility statement is provided, but the site is keyboard-navigable and responsive. Growth potential is promising: Maple's OpenTelemetry-native approach and AI/MCP integration differentiate it from competitors like Datadog and Grafana Cloud. The flat pricing and self-hosting options appeal to cost-conscious startups and SREs. However, the market is crowded, and Maple must prove its AI features are more than a gimmick to sustain growth.

Conclusion

Overall, Maple is a solid choice for startups and SREs looking for an affordable, open-source observability solution. Its OpenTelemetry-native approach and AI integration are forward-thinking, and the pricing is refreshingly transparent. However, the security headers and mobile performance need attention. If you can overlook these issues, Maple offers a compelling alternative to the expensive incumbents. Give it a try with the free trial and see if it fits your stack.

Suggestions for improvement

Enforce HTTPS and add HSTS, CSP, and frame protection headers to improve security posture.
Optimize mobile performance by reducing LCP, possibly through image optimization and lazy loading.
Add an accessibility statement and fix contrast, heading order, and main landmark issues.
Replace empty black rectangles with actual product screenshots or demos to enhance visual appeal.

How we score

Alternatives comparison

Named competitors, point by point. Nobody paid to appear here or to be left out.

	Maple	Datadog	Grafana Cloud	New Relic	Dash0
Pricing model	$39/mo for 100 GB per signal, then $0.30/GB, no per-host or per-seat fees	Per-host and per-feature pricing, often expensive	Usage-based with complex tiering, free tier available	Usage-based with per-user and per-GB costs	Usage-based, simpler but less transparent
OpenTelemetry support	Native OTLP ingest, no proprietary agents	Supports OTel but requires proprietary agents for full features	OTel-native with Grafana Agent	Supports OTel but with proprietary APM agents	OTel-native, simplified setup
AI integration	MCP server for natural language queries and AI-assisted debugging	AI features like Watchdog, but not MCP-based	Limited AI features, no MCP	AI features like New Relic AI, but not MCP	No AI-native features
Self-hosting	Open-source, self-hostable with ClickHouse	No self-hosting, SaaS only	Self-hosting available with Grafana OSS	No self-hosting, SaaS only	No self-hosting, SaaS only

Datadog

A popular comprehensive observability platform with extensive integrations, but notoriously expensive and proprietary.

Grafana Cloud

A managed observability stack based on Grafana, offering metrics, logs, and traces, but with limited AI-native features and a more complex pricing model.

New Relic

An established observability platform with strong APM capabilities, but often criticized for high costs and a complex user interface.

Dash0

A newer OpenTelemetry-based observability platform that offers a simplified experience, but lacks the AI-powered diagnostics that Maple provides.

Comparing options? See Maple alternatives, scored side by side

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