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With application architectures now built around microservices, containers, and AI/ML workloads, the complexity of managing infrastructure has multiplied. For startups and fast-scaling digital native enterprises, cloud-to-cloud migration from complex hyperscaler cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud to a simpler, cost-effective solution such as DigitalOcean can simplify operations while reducing … Read more

If your business is considering a shift to the cloud on a deadline, lift-and-shift is often the most practical migration approach you can take. This cloud migration strategy lets you move existing applications to the cloud with minimal changes to the underlying architecture, reducing both development time and cloud migration risks. Unlike complete application rewrites … Read more

While IT teams expect spending to grow 4.6% year over year in 2025, software costs are rising even faster, creating a potential budget shortfall for businesses. The answer isn’t to stop investing—it’s to optimize current spending and build a more efficient, recession-ready technology infrastructure that delivers better value per dollar spent. Cutting tech spending blindly … Read more

Every breakthrough in artificial intelligence (from systems that can detect small earthquakes to estimate magnitude calculations to medical imaging systems that can spot tumors that radiologists might miss) starts with a simple step: teaching machines to understand data the same way humans do. Raw data is just noise until someone gives it meaning. A collection … Read more

With the expansiveness of organizational infrastructure, numerous applications, and large amounts of generated data, migrating to the cloud can be a long, expensive, and complicated undertaking. You may find that cloud migration projects are over budget, behind schedule, or deliver poor cloud ROI. So how can organizations minimize this risk and spend? Using a cloud … Read more

Developing production-ready apps can be a long and arduous process involving different project teams, software testing, and hours spent staring at code to to craft the perfect design element or next big feature. But this doesn’t need to be the de facto development process—especially with the number of AI application builders available to software developers. … Read more

Trying to rightsize or provision additional GPU infrastructure when you need it can feel like trying to hit a moving target. A startup building an AI image generation platform might suddenly see requests spike from 100 to 10,000 per hour during a viral moment, scrambling to provision enough GPU power before their service crashes. Even … Read more

Training AI models, processing computer vision tasks, and running real-time applications require powerful GPU resources that many businesses can’t afford to own outright. However, major cloud providers often price their cloud GPU instances at rates that can quickly exhaust budgets—sometimes costing millions of dollars monthly for high-performance configurations. This pricing reality creates a barrier for … Read more

Note: Pricing and product information correct as of August 18, 2025, and subject to change. Companies are racing to build chatbots that can have more natural conversations, AI assistants that can write better code, and systems that can analyze medical data more accurately. This means training massive AI models —like GPT-5 with potentially trillions of … Read more

Companies building AI applications and analyzing massive datasets need serious computing power, but the hardware required to support these computational workloads can be prohibitively expensive. GPUs are a huge capital investment, with high-end GPU units costing $9,500 to $14,000 and enterprise models costing $27,000 to $40,000. Beyond the GPU itself, organizations must invest in servers, … Read more