Most AI teams hit a wall quickly: real-world interaction data is messy, limited, or risky to share. They try to build synthetic data pipelines from scratch, only to discover that high-fidelity trajectories are surprisingly hard to produce. The gap between a toy simulation and production-ready synthetic data can swallow months of engineering effort and thousands of dollars in compute.
The hidden cost of a DIY synthetic data pipeline
A synthetic data pipeline is not just a generator. It requires a realistic environment, robust logging, and rigorous quality control. Building that from scratch involves a mix of hard and soft costs: compute, engineering time, data labeling, and ongoing maintenance. A typical in-house pipeline might look like this:
Concrete cost breakdown (per 100k trajectories)
- Compute: $2,000, $5,000 to run agents on real browsers for 100k trajectories.
- Engineering: 2, 3 months of full-time effort to set up environment, logging, and quality checks.
- Labeling: $0.20, $0.40 per trajectory for human review and refinement.
- Maintenance: 10, 20% of initial engineering effort per year to keep simulations and logs aligned with product changes.
- Infrastructure overhead: $500, $1,500/month for servers, storage, and monitoring.
The real cost is not just money, it is the opportunity cost of engineers who could be shipping product features instead of wrestling with data generation.
Quality vs scale
Scale is easy: run more agents. Quality is hard. A synthetic dataset is only as good as its fidelity to real user behavior. If the environment is too simple or the logging is incomplete, the resulting trajectories will mislead models. Many teams discover that a small, well-curated dataset often outperforms a massive, noisy one. That means investing in validation, human review, and continuous alignment with product reality.
High-fidelity synthetic data requires more than code; it requires a deep understanding of real workflows and the ability to reproduce them at scale.
How Coasty fits
Coasty takes a different approach: its computer use agents run directly on real desktops and browsers, capturing genuine interaction data. The team can turn those trajectories into custom synthetic datasets tailored to your specific workflows and evaluation needs. This is a custom, contact-led service, no self-serve dashboards, no fixed packages, no public pricing. You talk to the Coasty data team to define your requirements and explore what’s possible.
If building a synthetic data pipeline feels like a distraction from your core product, it might be time to bring in specialized expertise. The Coasty data team can help you design and deliver high-quality synthetic datasets that match your AI goals. Book a data call to start the conversation at https://cal.com/coasty/coasty-data-call.
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