Buy vs Build: The Real Cost of a Synthetic Data Pipeline
Most AI teams face the same bottleneck: they need high-quality training data, but real data is either scarce, expensive to label, or comes with privacy risks. Synthetic data promises a way out, yet many organizations underestimate the full cost of running a synthetic data pipeline. The real expense isn’t just compute, it’s people, infrastructure, and risk.
The hidden costs of a build
Building a synthetic data pipeline from scratch means managing three major cost centers. First, you need a robust simulation engine that can realistically mimic your domain. Second, you need labeling infrastructure to annotate synthetic examples. Third, you need a validation pipeline to ensure the synthetic data actually improves model performance. A mid-sized team might spend six months building a prototype, incurring costs of 150k, 250k in salaries and cloud compute alone, before they even test real-world efficacy.
Compute and data volume
Generating synthetic data at scale requires GPU and CPU resources. A typical text-based synthetic dataset for a large language model might need 50, 100 million generated samples. Running that through a generative model on AWS A100 instances can cost 10, 20k per month for a few months of active generation. You also factor in storage, data cleaning, and reformatting. These compute costs compound quickly, especially if you need multiple synthetic variants for different tasks or languages.
Quality and validation tradeoffs
Not all synthetic data is equal. You must validate that synthetic examples match real-world distributions. Techniques like domain randomization, human-in-the-loop review, and alignment with human-annotated benchmarks help. However, each validation step adds overhead. A small team might spend 20, 30% of their total synthetic data budget on manual review and statistical checks. If you skip validation, you risk training on noisy data that degrades model performance.
The real cost of a synthetic data pipeline includes compute, people, and validation, not just the initial build. Teams that underestimate validation can waste months and hundreds of thousands of dollars on data that doesn’t improve their models.
When to outsource a synthetic data pipeline
If you lack specialized simulation expertise or need data quickly, outsourcing can be more cost-effective. Outsourcing lets you skip the learning curve, avoid long hiring cycles, and focus on model development. A custom synthetic data service can generate high-quality trajectories and interaction datasets tailored to your agents. You still own IP and control quality, but you save on infrastructure, specialized roles, and iterative experimentation time.
Outsourcing a synthetic data pipeline can reduce upfront investment and accelerate timelines, especially when you need domain-specific interaction data for computer use agents.
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 behavior, which is valuable for training and evaluating agents and models. Coasty offers a custom synthetic data service. It is contact-led, meaning you talk to the team to scope your needs. There is no self-serve portal or public price list. The offering is built around your use case, not a one-size-fits-all package.
If you need realistic interaction data for AI training or evaluation, consider partnering with Coasty. The best way to explore your options is to book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .