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Maple vs Grafana

Grafana is a powerful visualization layer, but building a full observability stack means configuring and maintaining Loki, Tempo, Mimir, and Prometheus separately — each with its own query language, scaling model, and failure modes. Maple gives you a unified platform where traces, logs, and metrics work together out of the box.

1
System to manage

Replace Loki, Tempo, Mimir, Prometheus, and Grafana with one platform

0
Query languages to learn

Visual query builder instead of PromQL, LogQL, and TraceQL

5 min
To first data

From OTel Collector config to seeing traces, logs, and metrics

Feature Maple Grafana
Core Observability
Distributed tracing
Log management
Metrics & dashboards
Custom dashboards
Alerting
Platform & Architecture
OpenTelemetry native
Open source
Self-hosting available
No vendor lock-in
AI / MCP integration
API-first design
Pricing & Access
Pricing model
Usage-based, transparent
Usage-based + self-host costs
Team seats included
Unlimited
Varies by plan
Setup time
Minutes
Hours (multi-tool config)
Proprietary agents required
Data retention control
Common challenges

Problems with Grafana that Maple solves

Grafana

Managing separate backends for Loki, Tempo, Mimir, and Prometheus

Maple

One unified platform handles traces, logs, and metrics. No stack to assemble or maintain.

Grafana

Hours of configuration before you can ingest your first trace

Maple

Point your OTel Collector at Maple and start seeing data in minutes. Zero configuration required.

Grafana

Learning PromQL, LogQL, and TraceQL — three different query languages

Maple

A single, intuitive query interface across all signal types. No query language fragmentation.

Grafana

Scaling each backend independently with different resource requirements

Maple

Maple scales as one system. Built on ClickHouse for high-throughput ingestion and fast queries at any scale.

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.

Migration

Switch from Grafana in minutes

01

Point your OTel Collector to Maple

If you're already using the OpenTelemetry Collector with Grafana backends, just change the OTLP exporter endpoint to Maple. Your instrumentation stays the same.

02

Migrate dashboards to Maple's builder

Recreate your Grafana dashboards using Maple's drag-and-drop dashboard builder. Most teams find the setup faster since data sources are pre-connected.

03

Decommission the Grafana stack

Once verified, shut down Loki, Tempo, Mimir, and Prometheus. You've just replaced five services with one.

Pricing

Estimated monthly cost: Maple vs Grafana

Drag the sliders to match your usage and see how the costs compare.

Adjust your usage
50 k series
100 GB/mo
100 GB/mo
10 users
MapleRecommended
$39/mo
Startup plan$39
Team seatsFree
300 GB included
Unlimited — always free
Grafana Cloud
$409/mo
Platform fee$19
Metrics$260
Logs$25
Traces$25
Users$80
Base plan
50k series (10k free)
100 GB (50 GB free)
100 GB (50 GB free)
10 users × $8
Save $370/month

That's 90% less than Grafana Cloud — or $4.4k/year back in your budget.

Start free trial

Estimates based on published pricing as of 2025. Actual costs may vary based on contract terms, volume discounts, and additional features. Maple pricing based on the Startup plan ($29/mo with 300 GB total included data).

FAQ

Frequently asked questions

Is Maple a replacement for the entire Grafana stack?
Yes. Maple replaces the combination of Grafana (visualization), Loki (logs), Tempo (traces), and Mimir/Prometheus (metrics) with a single unified platform. You get traces, logs, metrics, dashboards, and alerting in one system.
How does Maple compare to Grafana Cloud?
Grafana Cloud manages the Loki/Tempo/Mimir stack for you but you're still working with multiple query languages and separate data stores. Maple provides a unified experience where all signals are correlated automatically — plus AI-powered diagnostics and MCP integration that Grafana doesn't offer.
Do I need to learn a new query language?
No. Maple provides an intuitive visual query builder and search interface. Unlike Grafana where you need to learn PromQL for metrics, LogQL for logs, and TraceQL for traces, Maple uses a single query approach across all signal types.
Can I still self-host with Maple like I can with Grafana?
Yes. Maple's source is available under FSL-1.1 and can be self-hosted. The difference is you're deploying one system instead of four or five separate components.
What about Grafana's plugin ecosystem?
Maple focuses on the core observability experience: traces, logs, metrics, dashboards, alerting, and AI. While it doesn't have Grafana's extensive plugin ecosystem, most teams find they don't need it — the built-in features cover the standard observability workflow without additional configuration.
Is Maple built on OpenTelemetry like Grafana Tempo?
Yes, but Maple goes further. The entire platform is built for OpenTelemetry from the ground up — not just the tracing backend. Traces, logs, and metrics all flow through standard OTLP ingestion, giving you a consistent, vendor-neutral pipeline.
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