SaaS · Platform rebuild

Pulling a multi-tenant platform out of a monolith without a big-bang rewrite

We took a fragile monolith that every customer shared and rebuilt it into a multi-tenant platform with real isolation, shipping incrementally so the business never stopped. No flag day, no rewrite-and-pray.

Multi-tenancyStrangler migrationData migrationSystem design
ClientServiceJob queueData storeskip-locked workers
The challenge

One database backed every tenant, so a heavy customer could slow the whole platform and a bad migration risked everyone at once. Deploys were rare and tense because the blast radius was the entire product. They couldn't pause to rebuild, and they couldn't keep scaling the thing they had.

What we built
  • A tenancy model with clear isolation boundaries, so one customer's load, data, and failures stay contained instead of spreading.
  • The strangler pattern: carving capabilities out of the monolith one seam at a time, with traffic shifted behind a routing layer and the old path kept as a fallback.
  • A data migration that ran online, dual-writing and reconciling until the new store was proven correct before cutover.
  • A deploy pipeline that made shipping a single service routine, so changes stopped carrying the weight of the whole platform.
The outcome
  • Tenants are isolated, so one customer's spike no longer degrades the rest.
  • The team migrated with no flag day and no customer-visible downtime.
  • Deploys went from rare and risky to small, frequent, and boring.
FAQ

Common questions

No. We use the strangler pattern, carving capabilities out of the monolith one seam at a time behind a routing layer, with the old path kept as a fallback. The business keeps shipping the whole way through. There is no flag day and no rewrite-and-pray cutover.

The data migration runs online: we dual-write to the new store and reconcile continuously until it is proven correct, then cut over. Nothing moves in one risky batch, so a bad migration can't take down every customer at once.

Containment. With clear isolation boundaries, one customer's load, data, and failures stay contained instead of spreading. A heavy tenant no longer slows everyone, and the blast radius of a problem shrinks from the entire product to a single tenant.

We build a pipeline that makes shipping a single service routine. Once changes stop carrying the weight of the whole platform, deploys go from rare and tense to small, frequent, and boring, which is the point: lower stakes per change means lower risk overall.

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