MetricUI vs Grafana

TL;DR

If you are searching for “dashboard framework” and landing on both MetricUI and Grafana, you are probably comparing apples to oranges. Grafana is a standalone observability platform designed for DevOps teams monitoring infrastructure. MetricUI is an npm package that gives React developers ready-made dashboard components they drop into their own applications. Different audiences, different architectures, different use cases.

The short version: use Grafana to watch your servers. Use MetricUI to build the analytics pages your customers see.

What is Grafana?

Grafana is an open-source observability and monitoring platform, currently on the v11.x line. It connects to time-series databases like Prometheus, InfluxDB, Elasticsearch, and Loki, then lets you build dashboards that visualize those metrics in real time. It is best-in-class at what it does — if you need to watch CPU usage, request latency, error rates, or log volumes across a fleet of servers, Grafana is the answer.

Grafana runs as a server. You either self-host it or use Grafana Cloud. Dashboards are built in Grafana's own UI using its panel editor and query builders. The result is a dedicated web application — a destination your team navigates to.

Its ecosystem is enormous: hundreds of data source plugins, thousands of community dashboards, alerting, annotations, and a mature plugin SDK. For SRE and platform engineering teams, it is essentially infrastructure.

What is MetricUI?

MetricUI is a React component library purpose-built for product dashboards. You install it from npm, import the components you need, pass your data as props, and render them inside your existing React application. No server to run, no infrastructure to manage.

It ships 44 components across 18 chart types, plus KPI cards, filters, cross-filtering, drill-down, export, and AI-powered insights. Eight built-in themes let you match your product's design system out of the box, or you can define a fully custom theme.

MetricUI is designed for product teams that need to ship customer-facing analytics — the usage dashboard in your SaaS app, the reporting page in your admin panel, the analytics view in your internal tool. It is building material, not a destination.

The Fundamental Difference

The core distinction between MetricUI vs Grafana comes down to a single question: are you building a dashboard, or are you visiting one?

Grafana is a destination. It is a full application with its own URL, its own auth, its own UI. Your team opens a browser tab, navigates to your Grafana instance, and looks at dashboards. The dashboards live inside Grafana.

MetricUI is building material. It is a set of React components that live inside your application. Your users never leave your product. The dashboard is part of your app, rendered by your code, styled to match your brand. There is no second application to run.

This is not a quality difference — it is a category difference. A hammer and a screwdriver are both excellent tools. You just need to know which fastener you are working with.

Architecture

Grafana is a server-side platform. It requires a running Grafana server (Go backend, SQLite/PostgreSQL for config storage) plus one or more data source backends like Prometheus. You deploy it, configure data sources, create dashboards in the Grafana UI, and manage users and permissions through its admin panel. Grafana Cloud handles this hosting for you, but the architecture is the same: a separate application your team connects to.

MetricUI is a client-side npm package. You run npm install metricui, import components, and render them in your React app. There is no server, no config database, no admin panel. Your existing React application is the platform. Your existing API is the data source. MetricUI is just the visualization layer.

Data Model

This is where the MetricUI vs Grafana comparison gets most practical.

Grafana pulls data from configured data sources using its query language (PromQL, LogQL, Flux, etc.). You do not pass data to Grafana — you tell it where to find data, and it queries those sources directly. This is extremely powerful for infrastructure metrics that already live in Prometheus or InfluxDB, but it means your data must exist in a Grafana-compatible data source. Business data in your PostgreSQL database requires an additional data source plugin and often significant query work.

MetricUI takes data as props. You fetch data however you already do — REST API, GraphQL, tRPC, server components, SWR, React Query — and pass it to the component. Any shape, any source, any format. If you can get it into a JavaScript array, MetricUI can render it. You own the data pipeline completely.

Embedding & Integration

If you need dashboards inside your product — not on a separate Grafana URL — the integration story is very different between these two tools.

Grafana supports embedding via iframes. Each embedded panel or dashboard loads a full Grafana page inside the iframe, which means a separate page load, separate auth handling, and limited control over styling. For multi-tenant SaaS applications, each customer needs scoped access, which adds significant authentication complexity. Note that @grafana/ui and @grafana/scenes exist on npm, but these are for building Grafana plugins — not for embedding Grafana charts in external React applications.

MetricUI components render as native React elements in your component tree. No iframes, no separate auth, no extra page loads. They participate in your React state, respond to your context providers, and render in the same DOM as the rest of your application. Multi-tenant scoping is just a matter of which data you pass as props.

Theming & Branding

Grafana ships with built-in light and dark themes. Customization beyond that is limited unless you are on Grafana Enterprise, which supports white-labeling (custom logos, colors, login page). The standard open-source version looks like Grafana — and your users will recognize it.

MetricUIships 8 theme presets (indigo, emerald, rose, amber, cyan, violet, slate, orange) and supports fully custom themes. Every component uses CSS custom properties, so they inherit your application's design tokens. Your dashboard looks like your product, not like a third-party tool. For SaaS applications where brand consistency matters, this distinction is significant.

Licensing

This is a detail that matters more than most people realize, especially for SaaS companies.

Grafana is licensed under AGPL v3 (changed from Apache 2.0 in Grafana v7). The AGPL requires that if you modify Grafana and provide it as a network service, you must release your modifications as open source. For internal use this is rarely a problem, but if you are embedding Grafana in a commercial SaaS product, the AGPL has real implications. Grafana offers a commercial license for this scenario, but it comes with Enterprise pricing.

MetricUI is MIT licensed. Use it in any project, commercial or otherwise, with no obligations. There are no copyleft concerns for embedding it in your SaaS product.

Comparison Table

When to Choose Grafana

Grafana is the right choice when:

• You need to monitor infrastructure — servers, containers, Kubernetes clusters, network devices
• Your data already lives in Prometheus, InfluxDB, Elasticsearch, or other time-series databases
• Your audience is your internal engineering, SRE, or DevOps team
• You need production alerting with escalation policies and notification channels
• You want a standalone monitoring application with its own URL and access controls
• You need to correlate metrics, logs, and traces in a single platform

For these use cases, Grafana is genuinely unmatched. Its plugin ecosystem, query language support, and alerting capabilities are the result of over a decade of focused development. Do not try to replicate this with a component library.

When to Choose MetricUI

MetricUI is the right choice when:

• You are building customer-facing analytics — usage dashboards, reporting pages, admin panels
• The dashboard must look like part of your product, not a third-party tool
• Your data comes from your own API, database, or data warehouse — not from Prometheus
• You need the dashboard to participate in your React state, routing, and component lifecycle
• You are a SaaS company and need MIT licensing without AGPL concerns
• You want interactive features like cross-filtering, drill-down, and export built in
• You need to ship fast — install from npm and start rendering, no infrastructure to provision

If your users are your customers (not your engineering team), and your data is your business data (not your infrastructure metrics), MetricUI is designed specifically for this.

Can You Use Both?

Yes — and many teams should.

The MetricUI vs Grafana decision is not either-or. These tools occupy completely different parts of your stack. A common and sensible setup looks like this:

• Grafana for your internal ops team — monitoring application performance, server health, error rates, and deployment metrics. Your engineers open Grafana when something alerts.
• MetricUI for your product — the analytics dashboard your customers see when they log in. Usage stats, billing summaries, performance reports, activity feeds. This lives inside your React app and looks like the rest of your product.

They might even show some of the same underlying data, just framed differently. Your Grafana dashboard shows request latency as an SRE metric with alerting thresholds. Your MetricUI dashboard shows the same latency data to your customer as “API response time” in a polished card with a sparkline. Same data, different audience, different tool.

See It In Action

This is what a MetricUI dashboard looks like — built from a single CSV with 25 lines of code. No server, no infrastructure, no iframe.

import {
  Dashboard, DashboardInsight, SectionHeader,
  KpiCard, AreaChart, DonutChart, Waterfall, MetricGrid,
} from "metricui";
import "metricui/styles.css";
import { DollarSign, Users, TrendingDown, BarChart3 } from "lucide-react";

const data = [
  { month: "Jan 2024", revenue: 84200, users: 3120, churn: 4.2, conversions: 186, enterprise: 38400, startup: 28600, smb: 17200 },
  // ... 12 months from the same CSV
  { month: "Dec 2024", revenue: 127450, users: 5120, churn: 2.4, conversions: 342, enterprise: 59400, startup: 41200, smb: 26850 },
];

export default function SaasDashboard() {
  const latest = data[data.length - 1];
  const prev = data[data.length - 2];

  return (
    <Dashboard theme="indigo" exportable
      ai={{ analyze: myLLM, company: "Acme SaaS", context: "2024 metrics" }}>
      <SectionHeader title="Key Metrics"
        description="December 2024 vs November. Click any card to drill down." />
      <MetricGrid>
        <KpiCard title="Revenue" value={latest.revenue} format="currency"
          comparison={{ value: prev.revenue, label: "vs Nov" }}
          sparkline={{ data: data.map(d => d.revenue), type: "bar" }}
          icon={<DollarSign className="h-3.5 w-3.5" />}
          description="Monthly recurring revenue across all segments." drillDown />
        <KpiCard title="Active Users" value={latest.users} format="number"
          comparison={{ value: prev.users, label: "vs Nov" }}
          sparkline={{ data: data.map(d => d.users) }}
          icon={<Users className="h-3.5 w-3.5" />}
          description="Unique active users this month." drillDown />
        <KpiCard title="Churn Rate" value={latest.churn} format="percent"
          comparison={{ value: prev.churn, invertTrend: true, label: "vs Nov" }}
          sparkline={{ data: data.map(d => d.churn) }}
          icon={<TrendingDown className="h-3.5 w-3.5" />}
          description="Monthly churn rate. Lower is better."
          conditions={[{ when: "below", value: 3, color: "emerald" },
            { when: "between", min: 3, max: 4, color: "amber" },
            { when: "above", value: 4, color: "red" }]} drillDown />
        <KpiCard title="Conversions" value={latest.conversions} format="number"
          comparison={{ value: prev.conversions, label: "vs Nov" }}
          sparkline={{ data: data.map(d => d.conversions), type: "bar" }}
          icon={<BarChart3 className="h-3.5 w-3.5" />}
          description="New paid signups this month." drillDown />
      </MetricGrid>
      <SectionHeader title="Revenue Breakdown"
        description="Click any chart to drill down into the data." />
      <MetricGrid>
        <AreaChart data={data} index="month" categories={["enterprise", "startup", "smb"]}
          title="Revenue by Segment"
          subtitle="Stacked monthly revenue across Enterprise, Startup, and SMB"
          format="currency" stacked drillDown />
        <DonutChart data={[
          { id: "enterprise", label: "Enterprise", value: latest.enterprise },
          { id: "startup", label: "Startup", value: latest.startup },
          { id: "smb", label: "SMB", value: latest.smb },
        ]} title="Dec 2024 Mix" subtitle="Click a slice to drill into segment details"
          format="currency" drillDown />
      </MetricGrid>
      <Waterfall data={data.map((d, i) => ({
        label: d.month.split(" ")[0],
        value: i === 0 ? d.revenue : d.revenue - data[i - 1].revenue,
      }))} title="Month-over-Month Revenue Change"
        subtitle="Positive and negative swings with running totals"
        format="currency" drillDown />
      <DashboardInsight />
    </Dashboard>
  );
}

The Bottom Line

Grafana is an observability platform. MetricUI is a dashboard component library. Grafana is where your team goes to monitor infrastructure. MetricUI is what you use to build the analytics pages inside your product.

If you are an SRE team looking for a monitoring stack, Grafana is the answer. If you are a product team looking to ship customer-facing dashboards in your React app, MetricUI is the answer. If you are both — and most growing companies are — use both.

The right tool for the right job. Grafana has been perfecting infrastructure observability for over a decade. MetricUI exists so you do not have to bend an observability platform into a product dashboard framework — or build one from scratch.