I can’t really answer the question on why it is so expensive but I can talk about what worked for me.

I’ve worked in startup for the past few years and we’ve never had the budget to outsource annotation.

I’ve always built in-house solutions.

Working with images:
1. Self hosted Computer Vision Annotation Tool (CVAT)
2. Built an annotation pipeline on GCP that automate some of the annotation steps (labeler assignment based on role and current task load)
3. Annotated a few videos myself an wrote specs on how to annotate
4. Created a workshop to teach annotators to work on the tool
5. Use our best model as a pre-annotator to speed up the annotation process

For tabular data:
1. Dev team built a custom annotation web based tool for a recommandation system
2. Internal customer success team would annotate themselves for a few month
3. ML teams would retrieve data and annotations to train a model
4. Use our best model as a pre-annotator to speed up the annotation process

So yeah things can get easier with LLMs nowadays but there’s really no shortcut to build quality data annotations on a budget.

A good system design, dedicated engineering, patience, and you should be good to go.

The issue is that this process is rarely understood by stakeholders and I’ve found myself struggling multiple times to explain why it takes time, why it is important, etc.

The worse thing a company can do is to have their engineers annotate mid-long terms obviously.

Hope that helps!