Most AI teams hit the same wall: not enough high-quality, labeled data. Real-world data is expensive to label, risky to use, and often hard to scale. Building a pipeline to generate synthetic data sounds appealing, but hidden costs can quickly stack up. The real question is whether building it in-house or buying ready-made synthetic data makes more sense for your use case.
The hidden cost of building a pipeline
Building a synthetic data pipeline requires more than code. You need domain experts to design scenarios, data scientists to engineer generators, and engineers to scale the system. A typical internal project might involve these components: - 2, 3 data engineers for a year - 1, 2 domain experts for several months - Infrastructure costs (GPU clusters, storage, CI/CD pipelines) If you break it down, you’re often looking at $150,000 to $400,000 in direct salaries and infrastructure alone. That’s before accounting for maintenance, iteration time, and the risk of poor data quality.
Quality and reliability tradeoffs
Synthetic data must match the distribution and edge cases of real data to be useful for training and evaluation. In-house teams often struggle with: - Over-simplified scenarios that miss rare but important patterns - Inconsistent labeling due to manual review processes - Slow iteration cycles when requirements change Even when you have the talent, aligning synthetic data with business rules and edge cases is hard. One study of internal data teams showed that 40% of synthetic datasets required significant rework to match production requirements, adding weeks to timelines.
The hidden risks of real data
Using real data isn’t always a safe or scalable option. Privacy regulations like GDPR, CCPA, and HIPAA mean you can’t always use raw customer data. Even anonymized data can contain re-identification risks. Retrospective real data may also be biased, skewed, or incomplete, leading to models that perform poorly on new data. Synthetic data offers a way to generate large volumes of realistic, privacy-safe data, but you still need high fidelity to avoid compounding these biases.
The key takeaway: synthetic data is most cost-effective when you can rely on high-fidelity generators that match your real-world complexity, rather than building and maintaining a custom pipeline in-house.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. That means synthetic datasets and trajectories are grounded in actual user workflows, not idealized scenarios. Coasty’s approach is custom and contact-led: you work with the team to define your use case, and they produce synthetic data tailored to your needs. No self-serve dashboards and no fixed packages, just a custom synthetic data service that aligns with your requirements.
If you’re evaluating whether to build or buy a synthetic data pipeline, the real cost is more than code. It’s talent, time, and data quality. Instead of going it alone, consider working with a team that already captures realistic interaction data at scale. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to explore how custom synthetic data can meet your training and evaluation needs.
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