Buy vs Build: The Real Cost of a Synthetic Data Pipeline
Every AI team hits the same wall: we need labeled data, but real data is expensive, slow, or risky. Companies often decide to build a synthetic data pipeline to solve this. The math rarely works out as expected.
The hidden cost of a homegrown pipeline
A synthetic data pipeline is not just code. It needs data generation engines, validation pipelines, annotation tools, and iteration processes. A 2023 survey of midsize AI labs found that teams spent an average of 3.2 person-months on initial pipeline setup alone, before any data was produced. That time includes tool selection, infrastructure, and governance. Once running, maintenance adds another 15% of engineering capacity per year. Hardware and cloud compute for large-scale generation also adds up. A team generating 100 million samples for a multimodal model might spend 200k, 400k USD in compute costs in six months, depending on architecture and region. The biggest hidden cost is iteration time. Every new model architecture or evaluation metric requires changes to the pipeline. Each change can introduce regressions that take weeks to debug. Compare that to a vendor who already builds and maintains that infrastructure and can ship new datasets in weeks, not months.
Real tradeoffs: quality, diversity, and control
Quality is the biggest differentiator between homegrown and commercial synthetic data. Homegrown pipelines struggle with edge cases, privacy violations, and distribution drift. A 2024 study on banking chatbot evaluation found that synthetic datasets built with simple templates only covered 40% of real user intents. Teams had to manually curate and validate hundreds of examples, increasing cost and risk. Commercial solutions often start from realistic interaction data. They can capture complex workflows, multimodal inputs, and edge behavior that templates miss. Diversity is another factor. A custom synthetic dataset can cover regions, languages, and device types that an in-house pipeline might overlook. Control is simpler when you have a dedicated partner. You define constraints and objectives, and the vendor handles the engineering work. You get faster time to data and more confidence in real-world performance.
When building makes sense
Building a synthetic data pipeline can be the right choice when you have very specific, hard-to-reproduce scenarios. Think niche domains, internal workflows, or proprietary processes where no external data exists. In those cases, a custom build may be necessary. But you should count the full cost: engineering time, infrastructure, and ongoing maintenance. If your goal is to speed up iteration and reduce risk, a commercial approach often delivers better ROI.
The biggest mistake teams make is underestimating the time and resources needed for a homegrown synthetic data pipeline. The real cost often exceeds initial budgets and delays model development.
How Coasty fits into the picture
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach produces synthetic datasets and trajectories that reflect how humans and agents actually work. Coasty offers a custom synthetic data service. You describe your use case, and the team builds tailored datasets that match your evaluation and training needs. It is a contact-led process: you talk to the Coasty data team to define scope, quality, and delivery, and they create the data for you. There is no self-serve product, no fixed pricing, and no generic packages. Everything is built around your specific problem.
If you are evaluating synthetic data options and want to see what is possible for your use case, the first step is to talk to the Coasty data team. Book a data call at https://cal.com/coasty/coasty-data-call to explore how custom synthetic data can fit your workflow and reduce your total cost of ownership.