Buy vs Build: The Real Cost of a Synthetic Data Pipeline
Every AI team faces the same bottleneck: you need more interaction data, but real data is expensive, hard to label, or legally off-limits. Synthetic data promises a solution, but building a pipeline is not free. The choice is not just about technology, it is about money, time, and risk.
The hidden cost of building a pipeline
A synthetic data pipeline has several moving parts. You need a source of truth, a simulation engine, a labeling framework, and infrastructure to store and serve trajectories. Each component adds up. A recent analysis of 15 internal projects found average engineering effort of 4.5 months per pipeline and annualized infrastructure costs between $85K and $320K depending on scale. The costs can spiral when you need to iterate on prompts, adjust simulation parameters, or fix edge cases.
Labeling: the biggest line item
Even if you generate realistic interactions, you still need ground truth. Human annotators must verify actions, check for edge cases, and tag failures. Labeling can easily consume 60% of a project budget. A benchmark on a popular coding assistant showed that human annotation for 10,000 synthetic trajectories cost roughly $12,000, mostly due to quality checks and edge-case reviews. If you do not invest in automated validation, you risk leaking bad data into your model.
Quality vs. scale tradeoffs
You can scale quickly by generating more synthetic trajectories, but each new batch introduces noise. A study on reinforcement learning agents found that synthetic data with <80% accuracy degraded model performance by up to 12% after fine-tuning. To maintain quality, you need continuous evaluation loops, periodic human review, and a feedback mechanism to adjust simulation parameters. This adds engineering overhead that is easy to underestimate.
The real cost of a synthetic data pipeline is not just computational, it is the time you spend maintaining quality, labeling edge cases, and reworking failed batches.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data. This allows teams to obtain high-fidelity trajectories for training and evaluating agents and models. Instead of building a pipeline from scratch, you can work with Coasty to produce custom synthetic datasets tailored to your use case. This approach is custom and contact-led: you discuss your requirements, and Coasty designs a solution that matches your constraints and goals.
Synthetic data can be a powerful accelerator, but the costs of building and maintaining a pipeline are real. If you want realistic interaction data without the overhead of building from scratch, talk to the Coasty data team. Book a data call at https://cal.com/coasty/coasty-data-call to explore how Coasty can support your synthetic data needs.