Teams training agents or large models hit the same wall: they need plenty of realistic interaction data, but real-world logs are hard to get, risky to share, and expensive to label. You could build a pipeline from scratch, or you could buy a synthetic data solution. The question is not whether to build or buy, but which route actually pays off.
The hidden cost of building from scratch
Building a synthetic data pipeline means engineering a data generation engine, managing the generation lifecycle, and constantly cleaning and validating outputs. One engineering team spent 12 weeks on a custom simulation for an internal chatbot evaluation. They spent 35% of their engineering headcount just on data generation and validation. Another company spent $800k over 18 months on in-house labeling and data prep before they could train a single production model. Those numbers are not outliers; they are typical for teams trying to bootstrap synthetic data production.
Compute and scaling costs
Even after you design the process, you still pay for compute. A synthetic conversation generator that produces 10 million diverse dialogues at 1,000 samples per hour costs roughly $500 per month in GPU time. Scaling to 100 million samples jumps that monthly bill to $5,000. If your generation pipeline is inefficient, or if you need different modalities (screen recordings, browser clicks, natural language), you quickly hit GPU budgets and operational complexity. Most teams find that their compute spend grows faster than their data needs.
Quality and risk tradeoffs
Synthetic data is only as good as your generation logic. A common pitfall is over-reliance on simple templates, which produces sterile conversations that don’t reflect the messiness of real users. Another risk is bias leakage: if your generator reproduces patterns from training data, you may amplify unfair behavior. A large language model team discovered that their synthetic QA dataset contained 12% more gender-biased examples than their original real-world logs. They had to spend an extra $200k and three months manually auditing and retraining the generator.
The bottom line: building a synthetic data pipeline often means juggling engineering, operations, and quality checks, and the hidden costs, time, compute, and risk, can outweigh the upfront benefits.
When buying makes sense
When you buy custom synthetic data, you outsource the generation engine, labeling, and quality assurance. This lets you focus on model training and evaluation instead of data plumbing. A fintech startup that needed realistic customer support transcripts for an AI assistant bought a synthetic dataset from a vendor. They reduced their internal data prep time from 6 months to 3 weeks, and the synthetic data achieved 94% alignment with real support conversations on key metrics. The cost was predictable and tied to quality, not to headcount.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, so it can capture realistic interaction data and produce synthetic datasets and trajectories. Teams work with the Coasty data team to define the scenarios, domains, and quality standards they need. This is a custom, contact-led service, not a self-serve product. There is no public price list or fixed packages; the offering is shaped around your specific data and use case.
If you are tired of juggling engineering resources, compute budgets, and quality risks, consider exploring a custom synthetic data solution. Talk to the Coasty data team to see how realistic interaction data can accelerate your training and evaluation pipeline: book a data call at https://cal.com/coasty/coasty-data-call .
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