Most ML teams face the same reality: they need more data, but real data is expensive, risky, or just not available. You can scrape more, pay for labeling, or you can generate synthetic data. The decision between building a pipeline yourself or buying or partnering for a custom solution has a real financial impact.
The hidden cost of building from scratch
Building a synthetic data pipeline from the ground up means handling data generation, validation, and integration. A small internal team might spend months getting to a usable dataset. A 2024 survey of midsize AI teams found that 62 percent of synthetic data projects exceeded their original budget by an average of 34 percent. That gap usually comes from unexpected validation cycles, edge cases, and the cost of domain experts to review outputs.
What you actually own when you build
If you build your own pipeline, you own the code and infrastructure. But you also inherit all maintenance and evolution costs. Synthetic tasks change as models improve. You need to regenerate data, adjust formats, and keep up with new data requirements. Teams that build their own pipeline often underestimate how frequently they must retrain or re-validate the data to stay aligned with evolving model capabilities.
Time is money in data labeling
Even with synthetic data, you still need some label alignment. A typical labeling workflow might take 3 to 5 hours per example for complex tasks. Scaling to hundreds of thousands of examples quickly becomes a bottleneck. Synthetic data reduces the labeling burden but does not eliminate it. In practice, teams often find that the cost of labeling and validation still dominates the total project spend.
Quality and realism matter
Bad synthetic data hurts models more than no data at all. Poorly generated tasks can introduce systematic biases or unrealistic scenarios. A recent benchmark showed that synthetic data trained models suffered a 7 percent drop in performance on out-of-distribution tasks compared to models trained on carefully curated real data. The gap shrinks when synthetic data is tightly aligned with real-world interaction patterns and validated rigorously.
The most effective synthetic data pipelines combine automated generation with human-in-the-loop validation and continuous alignment with real-world use cases.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This approach lets teams capture realistic interaction data and generate custom synthetic datasets and trajectories tailored to their specific tasks. Coasty’s offering is a custom, contact-led service: you work directly with the data team to define requirements, expectations, and success metrics. There is no self-serve portal, no fixed pricing, and no generic offerings. You get a solution designed around your use case.
If you are evaluating synthetic data for training or evaluation, a custom service can save time, reduce risk, and align outputs with your real-world workflows. To explore what a synthetic data solution could look like for your project, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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