Buy vs Build: The Real Cost of a Synthetic Data Pipeline
Most AI teams struggle with the same bottleneck: not enough labeled, realistic data. Real-world scraping is slow, opaque, and risky. Some datasets are too small or too noisy. Others expose PII or violate contracts. Synthetic data promises an alternative, but the cost of building a pipeline can be surprisingly high. This post breaks down where that budget actually goes and how to think about it.
The hidden costs of real data collection
Almost every team underestimates how much it costs to collect and clean real data. A common estimate: 70-80% of a data project’s budget goes into preprocessing, labeling, and compliance. For example, a Fortune 500 retailer spent $2.1M over 18 months to build a labeled customer interaction dataset, mostly for labeling, deduplication, and legal review. The dataset itself took just $150K to scrape. The rest was operational overhead.
Why a synthetic pipeline isn’t free
Synthetic data pipelines have their own expense buckets. At a high level, they include: - Infrastructure to generate scenarios (simulators, rule engines, or agents) - Quality control and validation steps - Retrospective labeling or annotation of generated trajectories - Cost per successful, realistic sample, which can be higher than expected if the generator needs tuning A financial services firm reported that a first-pass synthetic dataset required two rounds of refinement and an additional $300K in validation effort before it met their model’s accuracy thresholds.
Key tradeoffs to watch
- ●Domain fidelity: synthetic data can be too idealized, missing edge cases that real users encounter.
- ●Sample size: high-fidelity synthetic data is often more expensive per sample than low-fidelity data.
- ●Feedback loop: you may need to iterate on the generator as you discover gaps in coverage.
- ●Compliance: synthetic data must still respect copyright, contract, and regulatory rules.
The sweet spot is when synthetic data gives you high coverage, low privacy risk, and a predictable cost per sample, without requiring a full custom build.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data. This approach produces synthetic datasets and trajectories that mirror actual user workflows, which can be useful for training and evaluating AI agents and models. Because the data is grounded in real environments, it tends to be more faithful than purely rule-based generation. Coasty’s offering is custom and contact-led: you work with the team to define your data needs and they design a solution around those requirements. There is no self-service pricing or fixed package, so the best way to see if it fits your use case is to discuss it directly.
If you’re exploring synthetic data for a specific domain, the best next step is to talk to the Coasty data team. They can walk you through a custom plan that matches your data requirements and budget. Book a data call at https://cal.com/coasty/coasty-data-call to get started.