Stop gambling with your data integrity.

From debunking the "XA is slow" myth to solving cloud-native recovery with LogCloud, we are showing you how to reclaim your developer productivity and bring 100% atomicity to your microservices.

Let's face it: most "resilient" microservice designs are still losing data.

It is not that developers aren't trying. They are. You see it in the endless retry logic, idempotency tokens, and circuit breakers piled into every service. You see it in all the apologetic blog posts explaining why 'eventual consistency' is a feature, not a bug.

But here is the uncomfortable truth: these patterns are treating the symptoms, not the disease. If you've ever faced duplicate messages, inconsistent state across services, or the dreaded partial failure in a distributed system, then you have seen it up close.

The root cause? Missing transactionality.

In the world of microservices and light-weight infrastructure, a lot of teams think they should roll their own eventual consistency "patterns" - let's call this "custom eventual consistency". So they end up coding a lot that needs to be tested for border-case scenarios. Even if this is done, the result rarely covers all cases, and not everybody wants to maintain this kind of code.

If you are building microservices, you have probably heard of the Saga pattern. Maybe you are even using it. After all, it is the default answer you will find on Stack Overflow and in most conference talks when people ask: How do I manage distributed transactions?

Synchronous microservices look great on paper.

They are easy to read, easy to debug, and they map perfectly to how we think: “I send a request, I get a reply.” No surprises, right?

Except when things go wrong. Which in distributed systems is most of the time.