Systems | Development | Analytics | API | Testing

At Koyeb, we run a serverless platform for deploying production-grade applications on high-performance infrastructure—GPUs, CPUs, and accelerators. You push code or containers; we handle everything from build to global deployment, running workloads in secure, lightweight virtual machines on bare-metal servers around the world. One of the core metrics we track is time to HTTP 200: the time between a successful deployment (excluding build) and the moment the app is ready to accept traffic.

Integration testing is a critical phase in software development that ensures different components of a system work together as intended. This blog covers the importance of integration testing, its methodologies, and best practices. Learn more about component and integration testing methods, benefits, and challenges.

Regression testing is the process of re-running tests to make sure new code changes don’t break what used to work. It acts like a safety net. If your app used to calculate prices correctly, and now it doesn’t because of a new update. Regression tests are what tell you something broke. Now here’s the real question: should you run those tests manually or automate them? That’s where the discussion around manual vs automated regression testing begins.

Software changes fast. Every new update, bug fix, or feature risks breaking something that used to work. That’s why teams rely on regression testing to make sure the old stuff still runs smoothly. But here’s the challenge: you can’t test everything, every time. Regression test suites get large, fast. Running all of them slows teams down. That’s where a risk-based approach for regression testing makes all the difference. Instead of testing everything, you test what matters most.

API protocol benchmarking helps you measure and compare the performance of communication protocols like REST, GraphQL, and gRPC in microservices. It’s not just about speed - it’s about finding the protocol that works best for your system under realistic conditions. Benchmarking identifies bottlenecks, helps with scalability, and ensures your architecture performs well under load.

The journey from disconnected data silos to self-service automation and predictive visibility is well underway for many finance organizations. But the million-dollar question remains: what’s the actual return on investment from this transformation? Beyond time savings and process improvements, modern finance transformation drives strategic value that directly impacts the bottom line. Adding AI into the mix turbocharges these benefits.

Creating dashboards and reports shouldn’t require a technical background, or hours of your users’ time. When applications lack user-friendly analytics, customers are forced to depend on IT just to understand their own data. That’s where conversational BI comes in. Whether you’re a business leader or a data analyst, conversational BI adapts to how you work, learns your preferences, and helps you move from question to insight faster than ever before.

Today, speed is everything, and that has put businesses under immense pressure to develop and deploy applications faster than ever before. The rapid expansion of low-code/no-code (LCNC) development platforms has been driven by this requirement for speed.In fact, Gartner predicts that by the end of 2025, a staggering 70% of new applications developed by enterprises will use LCNC technologies.

How financial institutions in the Gulf are embracing digital transformation and why assurance is their strongest ally.

Enterprises like yours manage terabytes to petabytes of data daily. Collecting and storing this massive volume of information is already complex. But the real challenge lies deeper. It's in delivering data effectively, securely, and in ways that empower your teams to innovate. This blog will deep dive into the current state of enterprise data automation and examine the limitations of legacy solutions. Then, we will explore how top-performing organizations approach automation in their industries.