Every AI team hits the same wall. You need more data to train or evaluate models, but real data is expensive, sensitive, or just not available. Synthetic data promises a way out: generate realistic samples on demand. But building a synthetic data pipeline is not free. It consumes compute, engineering time, and cycles. The real question is whether building in-house or buying ready-made data makes more sense for your budget and goals.
The hidden cost of building a synthetic pipeline
A synthetic pipeline is not just a generator. It needs data generation logic, quality checks, labeling pipelines, storage, and integration with your training stack. A recent survey of 50 AI labs showed that synthetic data projects spend an average of 22% of their total budget on engineering and infrastructure, not just generation. Teams that build from scratch often underestimate the time spent on robustness testing, diversity checks, and alignment with downstream performance metrics. If your team lacks a dedicated data engineering function, those hours cost more than you expect.
Quality and diversity matter more than volume
It is tempting to generate millions of samples, but performance gains flatten once you approach real-world distribution. A case study in a large language model fine-tuning project found that increasing synthetic sample size from 5 million to 50 million only improved validation accuracy from 2.3% to 2.8%. The real gain came from improving diversity and realism. Teams that skip rigorous quality checks end up training on noisy synthetic samples, which can degrade downstream performance and increase debugging time. Synthetic data must match real-world edge cases, not just fill a quota.
Real-world tradeoffs to evaluate
- Compute cost per sample varies widely. Simple text generation can be cheap, but complex multimodal tasks (video, high-fidelity images) can cost 10x more per sample.
- Labeling synthetic data is cheaper than labeling real data, but not free. Automating labeling with rules, models, or human review adds overhead.
- Synthetic data can leak biases present in the generation model. Regular audits and adversarial testing are required to maintain fairness.
- Data drift is a risk. Models trained on synthetic data may not generalize well if real-world distributions shift over time.
Focus on quality, diversity, and alignment with your downstream metrics. Volume alone rarely pays off.
How Coasty fits in the synthetic data picture
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This data can serve as a foundation for synthetic datasets and trajectories that reflect how humans actually work. Instead of building a full generation pipeline from scratch, teams can work with Coasty to produce custom synthetic datasets tailored to their use cases. This is a custom, contact-led service. There is no self-serve platform and no fixed packages. You talk to the Coasty data team to define your requirements, and they design a solution that fits your constraints and goals.
A synthetic data pipeline can be a powerful lever for faster iteration and better model performance. But the engineering and quality costs are real. If you want to move beyond basic generation and get high-quality, realistic synthetic data that aligns with your workloads, talk to the Coasty data team to explore a custom solution. Book a data call at https://cal.com/coasty/coasty-data-call to get started.
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