Buy vs Build: The Real Cost of a Synthetic Data Pipeline
Most AI teams hit the same wall: they need more real data, but real data is hard to get, risky to share, or too expensive to label. Synthetic data promises an escape route. But building a pipeline to generate it isn’t free. The real question is: what does it actually cost to build, and when should you buy instead?
Where the costs hide
Start with the obvious: compute. Generating synthetic data means running models or agents repeatedly. A single realistic PC interaction dataset for computer use training can require thousands of GPU hours. Even modest budgets for GPUs and storage add up quickly. But compute is just the tip of the iceberg. You also pay for engineering time to design prompts, validate outputs, and orchestrate pipelines. A custom pipeline might cost six months of senior engineer effort for a single domain.
Label quality is not free
Synthetic data often needs labels. Even if an agent generates realistic screenshots and clicks, you still need to annotate actions, intents, or states. Manual labeling of 10,000 labeled interactions can cost tens of thousands of dollars and weeks of time. Automated labeling helps, but it introduces error rates. If your downstream model is sensitive to mislabeling, the cost of fixing errors can exceed the label cost itself. The cost of quality control can be just as high as the cost of generation.
Domain expertise is a bottleneck
Generating realistic data requires deep domain knowledge. For example, creating synthetic workflows for a specific SaaS product demands understanding user roles, permissions, and edge cases. Relying on general-purpose agents often produces generic output. Specializing agents to your domain takes time and iteration. The cost of domain expertise is often the most underappreciated expense in synthetic data projects.
Real benchmark: building vs buying
A recent benchmark for computer use agents compared two approaches. A team that built a synthetic dataset internally spent about 400 GPU hours and eight weeks of engineering effort, incurring roughly $80,000 in compute and labor costs. Another team that worked with a specialized provider delivered a comparable dataset in six weeks with similar compute spend but far less engineering overhead. The build route required more domain-specific tuning and validation. The buy route shifted the burden of expertise and iteration to the provider.
The tradeoff is clear: building gives you full control and ownership, but at a higher upfront cost and longer time to value. Buying can reduce time and engineering effort, but it requires trusting a partner to deliver high-quality, domain-specific data.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets the team capture realistic interaction data and produce synthetic datasets tailored to specific use cases. Instead of hand-crafting prompts and validating every interaction, teams can describe their needs and let the system generate training and evaluation data. Coasty’s approach is custom and contact-led: there is no self-serve product or fixed pricing. You work directly with the data team to define requirements, scope, and outcomes.
If you need realistic synthetic data for training or evaluating AI agents, the fastest path might be to talk to the Coasty data team. Book a data call to explore your options and get a clear picture of what’s possible. https://cal.com/coasty/coasty-data-call