Cloud

The move to the cloud promised to save users money and give them insights into their usage and costs. However, the opposite happened. A 2025 report from AAG stated that around 82% of respondents found cloud spending challenging. A cloudzero report from 2024 states that more than 20% of respondents had no clear idea of their cloud costs, with reports for large users sometimes consisting of thousands of rows of hard-to-read usage data.

Many organizations work with clients and infrastructure around the world and face significant challenges ensuring they follow privacy regulations as their application data flows across borders. This post looks at how you can use Bacalhau to handle distributed cross-border processing and anonymize data with Microsoft Presidio to help meet some of these requirements.

When the modern-day internet began emerging in the early 2000s, finding hosting services and resources to run the new wave of dynamic web applications was hard. You needed a database to store application data. These were slow, expensive, and unreliable, regularly bringing applications to a grinding halt when a single instance failed. You needed a server to run interpreted languages like PHP, Python, or Ruby. These were equally expensive, often needed configuration, had security issues, and frequently ran out of memory or CPU resources, again bringing applications to a grinding halt.

Machine Learning requires vast amounts of resources, and distributing these resources across multiple devices and regions helps with cost, speed, and data sovereignty. Bacalhau is an open-source distributed orchestration framework designed to bring compute resources to the data where and when you want, drastically reducing latency and resource overhead. Instead of moving large datasets around networks, Bacalhau makes it easy to execute jobs close to the data’s location, reducing latency and resource overhead.