# Breaking the Scale Barrier: Building Multi-Tenant SaaS on Power Pages

Page: https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214/breaking-the-scale-barrier-building-multi-tenant-saas-on-power-pages
Text version: https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214/breaking-the-scale-barrier-building-multi-tenant-saas-on-power-pages.md
Podcast: [M365.FM - Modern work, security, and productivity with Microsoft 365](https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214)
Published: 2026-05-28T14:00:02+00:00
Episode link: https://www.spreaker.com/episode/breaking-the-scale-barrier-building-multi-tenant-saas-on-power-pages--72204586
Audio file: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72204586/breaking_the_scale_barrier_building_multi_tenant_saas_on_power_pages.mp3
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Duration seconds: 5162

## Resource

Building multi-tenant SaaS on Power Pages changes the way architects think about Dataverse scalability. Most developers traditionally viewed Power Pages as a portal platform intended for forms, authentication, and moderate business applications. Enterprise-scale SaaS workloads were assumed to require fully custom Azure infrastructure and external databases. Elastic Tables challenge that assumption by introducing Cosmos DB-backed storage directly inside Dataverse, allowing Power Pages to support large-scale operational workloads while preserving the familiar Dataverse developer experience. WHY STANDARD DATAVERSE TABLES HIT LIMITS Standard Dataverse tables are optimized for relational transactional workloads such as CRM systems, account management, and business processes. They perform extremely well for structured business entities but begin struggling under workloads dominated by telemetry ingestion, event logging, audit history, and append-heavy operational data. As tenant counts grow, noisy-neighbor effects appear because all tenants compete for the same relational backend resources. The architecture problems become especially visible when SaaS platforms start accumulating massive volumes of operational records. Bulk write operations slow down, storage costs increase rapidly, and query performance degrades under high-ingestion scenarios. These are not flaws in Dataverse itself but rather signs that the workload no longer aligns with the strengths of Azure SQL-backed storage. Azure SQL excels at relational workloads Operational SaaS data behaves differently Multi-tenant contention creates performance issues Storage costs rise quickly at scale ELASTIC TABLES AND COSMOS DB Elastic Tables replace the underlying SQL engine with Azure Cosmos DB while preserving the same Dat…

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