In-house or outsourced data annotation? (2025)

Interesting question. As the founder of an AI collaboration platform (Markhub), we live and breathe this problem every day. My take is that the best approach isn't a simple choice between in-house vs. outsourced, but a hybrid model focused on the quality and context of the data.

For our foundational models (e.g., text summarization), we start with powerful base models like Gemini and fine-tune them. But the real magic happens with our proprietary data, and for that, outsourcing is not an option.

Here's our approach: Our own product, Markhub, is our primary annotation tool.

When our early users give feedback—like circling a button on a screenshot and commenting "This color is wrong"—they are, in effect, creating a perfect piece of labeled data: [Image] + [Area of Interest] + [Instruction].

We call this "Collaborative Annotation" or "In-Workflow Labeling." The data quality is incredibly high because it's generated by domain experts (our users) as a natural byproduct of their daily work, full of real-world context. This is something an external annotation firm can never replicate.

So, to answer your question on how the trend will evolve: I believe the future isn't a binary choice between in-house and outsourced. The next wave will be tools that allow teams to create their own high-context training data simply by doing their work. The annotation process will become invisible, seamlessly integrated into the collaboration flow itself.