Buy vs Build: The Real Cost of a Synthetic Data Pipeline

The hidden price of real data

Collecting, curating, and labeling real data is expensive. A recent analysis of data labeling costs across three industries found that teams typically spend $0.30 to $0.60 per high‑quality label for simple classification tasks, rising to $3 to $7 for complex multimodal interactions. When you factor in data cleaning, compliance checks, and ongoing maintenance, the per‑example cost can climb much higher. Beyond dollars, real data introduces privacy and security risks. Sharing customer emails, call logs, or screenshots for training often requires de‑identification, legal review, and additional infrastructure, which adds both cost and time.

What it really costs to build a synthetic pipeline

Building a synthetic data pipeline from scratch is a substantial engineering effort. You need to model the domain, design interaction scenarios, generate realistic inputs, and label the outputs. One internal deployment effort required an initial investment of roughly $250,000 in engineering time over six months, plus ongoing compute and maintenance costs. The biggest expense was not the GPU hours but the domain experts who designed the scenarios and validated the generated interactions. Building in‑house also means you shoulder all the risk: bugs in the generator can create systematic biases, and you own every update and fix. If the pipeline doesn’t scale or doesn’t match real‑world behavior, you’re stuck maintaining a costly technical debt.

When buying or partnering makes sense

Teams that need high‑fidelity, domain‑specific synthetic data often find that partnering with a specialized provider is more cost‑effective than building in‑house. A mid‑size fintech startup estimated that a custom dataset delivered by a synthetic data partner cost $120,000 upfront and reduced their labeling spend by 70% over the next two years. The partner handled scenario design, generation, validation, and quality assurance, allowing the startup to focus on building models and products. The key is that the provider already has the domain expertise, infrastructure, and quality‑control processes that would take months or years to develop internally.

Key tradeoffs to evaluate

• High‑quality synthetic data requires realistic scenarios that match real user behavior. Poorly designed scenarios can introduce bias.
• You still need to validate synthetic outputs against real examples or expert review to ensure reliability.
• Scaling a custom synthetic pipeline means planning for ongoing scenario updates to keep pace with changing workflows.
• Integration with your data pipeline, evaluation frameworks, and model training workflows adds complexity regardless of whether you build or buy.

How Coasty fits into the picture

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach enables the creation of custom synthetic datasets and trajectories that mirror actual user behavior, which can be used for training and evaluating agents and models. Coasty’s offering is a custom, contact‑led service, not a self‑serve product. You work directly with the Coasty team to define your use case, specify scenarios, and design the dataset you need. This means you get data that is tailored to your domain and workflows, without having to build the underlying generation and validation infrastructure yourself.