Building a Multi-Tenant Price-Monitoring SaaS Data Layer for Scalable Retail Intelligence

 


Client Snapshot

A B2B SaaS startup building a price-monitoring product for mid-market retailers. They owned the application and billing; they needed the data engine underneath it.

The Challenge


The founders had built a working single-tenant prototype that scraped [VERIFY: 3] sites. It broke roughly [VERIFY: weekly]. Scaling it to a paying multi-tenant product surfaced problems the prototype had never faced:

  • Each tenant wanted different sites, different SKUs, different check frequencies

  • Anti-bot systems began blocking as volume rose

  • One tenant's heavy schedule could starve another's

  • Every site layout change meant a silent data gap nobody noticed until a customer complained

Objectives

  • A data layer supporting [VERIFY: 7–10] retail sources with per-tenant configuration

  • Scheduled collection with per-tenant isolation and fair scheduling

  • Anti-bot resilience with graceful degradation, not silent failure

  • Historical price storage and change-based alerting

  • Health monitoring that catches breakage before customers do

The Actowiz Approach

We separated the system into four layers: collection (per-source workers), scheduling (queue with per-tenant quotas so no tenant can starve another), storage (append-only time series, never overwriting history), and monitoring (per-source health checks with expected-yield thresholds).

That last layer is the one prototypes always omit and production always needs. If a source that normally returns 5,000 records suddenly returns 40, that's a layout change, not a market event — the system flags it and alerts rather than writing garbage into a customer's dashboard.

Anti-bot handling used a rotating session pool with per-source fingerprint profiles, and a fallback ladder: if the light method fails, escalate; if escalation fails, alert rather than retry infinitely.

Data Delivered

Tenant ID, source, SKU, price, availability, seller, captured_at, run_id, source_health_status.

Format: API + Postgres · Cadence: Per-tenant configurable

Results

  • [VERIFY: 10] sources in production

  • Uptime improved from [VERIFY: weekly breakage] to [VERIFY: 99.2%] successful scheduled runs

  • Source breakage detected automatically in [VERIFY: under 30 minutes], before customer-visible impact

  • Client onboarded [VERIFY: their first paying tenants] on the new layer

Compliance Note

Public product and pricing data only. Per-source rate limits configured conservatively.

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