Multi-Tenant SaaS Platform

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a multi-tenant SaaS backend with tenant isolation, per-tenant billing, custom domains, and data residency.
• •Design for a peak load target around 2,894 RPS (including burst headroom).
• •Tenants: ~5,000
• •Total users: ~2,000,000
• •API requests/day: ~50,000,000
• •Per-tenant latency: < 200 ms
• •Noisy neighbor tolerance: Zero - enforce limits

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Sharding: Choose shard keys around access patterns and growth hotspots, not just data size.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Use short-lived tokens and secure key rotation workflows.
• •Apply strict input validation and backward-compatible versioning.
• •Support rebalancing and hotspot detection from day one.
• •Alert on user-impact SLOs, not only infrastructure metrics.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Sharding: Sharding improves horizontal scale but makes cross-shard queries and transactions harder.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.

Practical Notes

• •Shared database with tenant_id column is simplest but riskiest (bugs can leak data). Schema-per-tenant balances isolation and operational cost. Database-per-tenant is safest but hardest to manage.
• •Add tenant_id to every database query via a middleware layer - catch any query missing the filter.
• •Custom domains: automate SSL with Let's Encrypt and a reverse proxy (Caddy or Nginx) that maps domains to tenants.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> Load Balancer -> API Gateway -> API Service -> Auth Service -> Primary NoSQL DB -> Monitoring -> Log Aggregator

Design strengths

• •Monitoring and logs are wired in from day one for rapid incident triage.
• •Security controls are enforced at ingress to protect downstream capacity.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.