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Power BI vs Custom Dashboards

TL;DR

Power BI for enterprise-wide BI with governance and self-service, Power BI Embedded for analytics inside your product, Grafana for operational monitoring, custom React/D3 for pixel-perfect control, and Streamlit for rapid data-science exploration.

When this question comes up

  • A project needs dashboards and the team is deciding between Power BI licensing and building custom UI.
  • Analytics must be embedded inside a customer-facing application rather than a standalone portal.
  • The workload is operational monitoring (uptime, metrics, alerts) rather than traditional BI.
  • Data scientists want interactive exploration without waiting on a front-end team.

Decision tree

flowchart TD
    start["Primary dashboard audience?"] -->|Enterprise analysts / executives| q_ent
    start -->|End users inside a product| q_embed
    start -->|SRE / DevOps / operational| q_ops
    start -->|Data scientists / researchers| q_ds

    q_ent{"Need governed semantic models,<br/>row-level security, M365 integration?"}
    q_ent -->|Yes| rec_pbi["**Recommend:** Power BI<br/>Pro / Premium"]
    q_ent -->|No — simple KPI page| q_simple

    q_simple{"Team has React / front-end<br/>engineering capacity?"}
    q_simple -->|Yes| rec_react["**Recommend:** Custom React / D3"]
    q_simple -->|No| rec_pbi

    q_embed{"White-label / ISV scenario?"}
    q_embed -->|Yes — per-customer tenancy| rec_pbie["**Recommend:** Power BI Embedded"]
    q_embed -->|No — pixel-perfect UX| rec_react

    q_ops{"Prometheus / Azure Monitor<br/>metrics already flowing?"}
    q_ops -->|Yes| rec_grafana["**Recommend:** Grafana<br/>(Azure Managed)"]
    q_ops -->|No — custom telemetry| rec_react

    q_ds{"Need to share with<br/>non-technical stakeholders?"}
    q_ds -->|Yes — quick prototype| rec_streamlit["**Recommend:** Streamlit"]
    q_ds -->|No — notebook sufficient| rec_streamlit

Per-recommendation detail

Recommend: Power BI Pro / Premium

When: Enterprise-wide BI with self-service authoring, governed semantic models, row-level security, and Microsoft 365 integration. Why: Best-in-class semantic layer (DAX), Direct Lake for sub-second refresh over lakehouses, built-in lineage via Purview, and familiar Excel-like authoring for business users. Tradeoffs: Cost — Pro per-user or Premium capacity ($$$); Latency — sub-second with Direct Lake, minutes for import refresh; Compliance — FedRAMP High in Azure Gov; Skill — DAX + Power Query (low bar for analysts). Anti-patterns:

  • Embedding inside a SaaS product for external customers — use Power BI Embedded or custom React.
  • Real-time operational alerting on infrastructure metrics — use Grafana.

Linked example: Power BI Guide | Power BI + Fabric Roadmap

Recommend: Power BI Embedded

When: Analytics embedded inside a customer-facing application with per-customer row-level security, ISV multi-tenant scenarios, or white-label dashboards. Why: Same Power BI rendering engine with app-owns-data authentication; A/EM SKU pricing scales with capacity rather than per-user; integrates with existing Azure AD B2C or custom auth. Tradeoffs: Cost — A/EM capacity-based, can be expensive at scale; Latency — same as Power BI Premium; Compliance — inherits Power BI compliance posture; Skill — requires front-end integration work (JavaScript SDK). Anti-patterns:

  • Internal-only BI where Pro licensing is cheaper — use Power BI Pro.
  • Need for full custom UX with non-Power-BI chart types — use React/D3.

Linked example: Power BI Guide

Recommend: Grafana (Azure Managed)

When: Operational monitoring, infrastructure dashboards, alerting on Prometheus, Azure Monitor, or Log Analytics data sources. Why: Purpose-built for time-series visualization with native Prometheus, Azure Monitor, and Azure Data Explorer data sources; Azure Managed Grafana provides managed identity and VNet integration out of the box. Tradeoffs: Cost — Azure Managed Grafana pricing by instance tier; Latency — real-time streaming panels; Compliance — inherits Azure compliance; Skill — PromQL / KQL familiarity needed. Anti-patterns:

  • Traditional BI with governed semantic models and self-service authoring — use Power BI.
  • Customer-facing product analytics — use Power BI Embedded or custom React.

Linked example: Power BI Guide (monitoring section)

Recommend: Custom React / D3

When: Pixel-perfect control over visualization UX, custom chart types not available in Power BI, or deep integration into an existing React/Angular SPA. Why: Full design freedom; any chart library (D3, Recharts, Visx, AG Charts); integrates natively with your app's auth, routing, and state management. Tradeoffs: Cost — engineering time ($$$), ongoing maintenance; Latency — depends on API/data layer; Compliance — inherits app compliance posture; Skill — high (React + data viz + API development). Anti-patterns:

  • Enterprise BI with 50+ reports and self-service authoring — Power BI is faster to deliver and maintain.
  • Teams without dedicated front-end engineers — use Power BI or Streamlit.

Linked example: Power BI + Fabric Roadmap (custom integration patterns)

Recommend: Streamlit

When: Data scientists need to share interactive exploration tools with stakeholders quickly, prototyping dashboards before investing in production UI. Why: Python-native; deploy from a notebook to a shareable app in hours; integrates with pandas, Plotly, Altair, and ML model outputs directly. Tradeoffs: Cost — minimal (App Service or Container Apps hosting); Latency — depends on backend compute; Compliance — deploy in your own Azure tenant; Skill — Python (low bar for data teams). Anti-patterns:

  • Production customer-facing dashboards requiring enterprise auth, RLS, and SLA — use Power BI or custom React.
  • High-concurrency BI (>50 simultaneous users) — Streamlit is single-threaded per session.

Linked example: Power BI Guide (comparison section)