From a monolith at its limit to cloud-native Go services.
A representative engagement migrating a SaaS product that had outgrown its monolith to Go microservices, without a risky big-bang rewrite and without taking the product offline.
By the CapregSoft Engineering Team ·
The outcome.
performance gain on migrated hot paths
infra cost reduction via independent scaling
downtime during cutover
This is a representative engagement that reflects the patterns, architecture, and outcomes typical of our work in this area. Specific metrics are engineering targets, not audited per-client results.
Key takeaways.
How CapregSoft approached A scaling SaaS product — the short version.
- Avoid the big-bang rewrite, the strangler pattern moves traffic to new Go services incrementally and safely.
- Migrate the hot paths first so the biggest performance and stability wins land early.
- Feature flags and traffic shadowing prove each new service against the live monolith before cutover, zero downtime.
- Independent scaling on Kubernetes is where the up-to-50% infra cost reduction comes from.
Where it hurt.
The product's monolith had become the bottleneck. Every traffic spike threatened downtime, scaling meant paying for the whole application even when only one part was hot, and deploys were slow and nerve-wracking.
A full rewrite felt too risky, the business couldn't stop shipping for six months, but staying on the monolith was capping growth.
What we did.
Identified the hot paths first, the few parts of the system under the most load, so the migration started where it would relieve the most pain.
Used the strangler pattern: stood up new Go services alongside the monolith and routed traffic to them incrementally, so the old and new systems ran in parallel during the transition.
Extracted services one bounded context at a time, with feature flags and traffic shadowing so each cutover could be verified against the live monolith before going fully live.
Moved to Kubernetes so each service scales independently, and load-tested every extracted service before it took production traffic.
How it actually came together.
Why not just rewrite it?
The instinct when a monolith hurts is to start over. It's almost always the wrong call. A big-bang rewrite means months with no new features, a high chance of recreating old bugs, and a terrifying single switch-over day. Meanwhile the business still has to compete.
We use the strangler pattern instead: new Go services are built alongside the existing monolith, and traffic is moved to them gradually. The monolith keeps serving everything it served yesterday while we peel off one capability at a time. There's never a moment where the whole system changes at once, which is exactly what makes it safe.
Migrate the hot paths first
Not all of a monolith is equally painful. Usually a small number of endpoints carry most of the load and cause most of the incidents. We profile the system to find them and migrate those first, so the earliest cutovers deliver the biggest relief, performance and stability improve before the migration is anywhere near 'done'.
Each extracted capability becomes an independent Go service. Go is the right tool here because the whole point is throughput and efficient concurrency: services handle far more load per instance than the monolith did, which is where the performance and cost gains come from.
Cutting over without holding your breath
The risk in any migration is the switch. We de-risk it with feature flags and traffic shadowing: a new service can receive a copy of real production traffic and have its responses compared against the monolith before it serves a single real user. When the new path is proven correct, we shift traffic to it gradually and keep the old path as an instant rollback. That's how a cutover happens with zero downtime instead of a maintenance window and crossed fingers.
On Kubernetes, each service then scales on its own. Instead of paying to scale the entire application because one feature is busy, you scale only the part under load, which is where the infrastructure savings come from.
What powered it.
This was a Microservices Migration engagement.
Questions teams ask before they call.
What is the strangler pattern in a microservices migration?
It's an incremental approach where new services are built around the edges of an existing monolith and traffic is gradually redirected to them, until the new services have 'strangled' the old system and it can be retired. It avoids the risk of a big-bang rewrite by keeping the product fully working throughout the transition.
How do you migrate without downtime?
By running the new service in parallel with the monolith, shadowing real traffic to validate its responses, and shifting users over with feature flags so the old path remains an instant rollback. No single switch-over event means no maintenance window and no outage.
Why migrate to Go specifically?
Migrations to microservices are usually motivated by performance and cost. Go's efficient concurrency lets each service handle high throughput with modest resources, so migrated hot paths get materially faster and cheaper to run, which is the entire reason for doing the migration in the first place.
How long does a monolith-to-microservices migration take?
Because it's incremental, value arrives early, the first hot paths can be migrated and delivering improvements within weeks, while the full transition typically spans a few months depending on the system's size and complexity. You ship features the whole time.