In 2025, the data being generated is in zetabytes. But only 5% of all data on the internet is publicly available. This shocking fact highlights a major challenge AI developers face today. Companies are rushing to build smarter AI systems, but most encounter a significant roadblock: there’s simply not enough high-quality, annotated training data.

As a result, around 85% of AI projects never reach production, and poor data quality is usually the main reason. But there’s a solution changing the game for AI teams—synthetic data for LLMs and other machine learning models—and it doesn’t cost a fortune.

What Is Synthetic Data in AI Training?

Synthetic data is generated using real data but with some modification, which imitates real-world data patterns without containing any actual personal or sensitive information. Unlike traditional datasets collected from users, synthetic data is produced using algorithms and machine learning models.

Think of it like this: instead of taking thousands of photos of real customers (which raises privacy concerns), companies can generate similar images that have the same statistical characteristics. This solves multiple problems at once—privacy, cost, and the lack of enough data.