Implementing AI solutions - especially those based on computer vision - usually requires collecting and manually labeling vast amounts of data. Acquiring real images, labeling them (masks, bounding boxes, segmentation, 3D data, object and sensor annotations, etc.) and then organizing the training, testing, validation and implementation processes are often time-consuming and expensive. For this reason, a growing number of companies are analyzing the return on investment (ROI) of alternatives, including synthetic data.
From a business perspective, it's important that the ROI is transparent, measurable and comparable to the costs of traditional methods. In this context, the solution offered by SKY ENGINE AI (Synthetic Data Cloud) seems promising, offering tangible benefits that can be translated into savings in time, labor, resources and money.
Below, we delve deeper into this topic - here's an analysis of the costs, potential benefits and methods for calculating ROI using this platform.
Entry and operational costs for Synthetic Data
Moving from traditional data acquisition to synthetic data generation involves significant investment and it's worth estimating these carefully. For SKY ENGINE AI, typical costs include:
- Platform/licensing fees - although often presented as a "managed service," subscription or cloud access costs must be factored in.
- Computational resources (GPU/cloud) - generating large datasets with ray tracing, sensor simulation and multimodality often requires significant computing resources.
- Scene/pipeline setup and preparation costs - preparing scenarios, sensor parameters, materials, lighting and randomization - are time-consuming and knowledge-intensive tasks.
- Validation and testing on real-world data - While synthetic data can minimize the need for real-world data, testing and calibration on real-world samples are often necessary to ensure the model generalizes correctly.
These costs represent the “upfront” and operating investment - which, of course, must be weighed against the savings and benefits.
Benefits and savings - where the ROI comes from
SKY ENGINE AI itself highlights the benefits of using synthetic data. Here are the main ones-and consequently: savings and competitive advantages.
- Significant reduction in data acquisition and annotation costs-generating synthetic datasets can be up to 100x cheaper than collecting and manually labeling real images.
- Significant acceleration of workflow-fast time-to-market-according to the platform's claims, generating millions of images takes days instead of months.
- Higher quality and consistency of annotations ("ground truth")-semantic masks, bounding boxes, depth maps, normal maps, 3D keypoints, multimodality sensors-which translates into better models and fewer errors during implementation.
- Ability to cover rare cases and edge cases-scenarios that are difficult, expensive, or even dangerous to collect in the real world (e.g., manufacturing defects, damage, extreme conditions, rare situations).
- Scalability and flexibility-"data on demand" - when new data is needed (e.g., a new object class, different conditions, a sensor, a new case), it can be generated automatically, without being paralyzed by logistics.
- Reduced dependence on private/sensitive data- which in regulated industries (e.g., medicine, industry, security) can mean lower compliance, anonymization, data protection, etc. costs.
In practice, this means for companies: shorter implementation times, lower annotator costs, fewer errors, faster iterations and most importantly - predictable costs and processes.
Example ROI Calculation
Of course, ROI will depend on the specific project, scope and requirements. However, a simplified comparative calculation can be made. For example, let's assume a traditional project would collect and annotate 10,000 images in 4 months, employing a team of annotators and handling the logistics-costing, for example, €100,000. If we use synthetic data instead, costs can be reduced by a factor of several-while also obtaining a larger, more diverse dataset (e.g., 100,000-500,000 images), faster and fully annotated. Even after factoring in cloud/licensing costs, the difference can be significant. Add to this the lower cost of errors, better model quality, reduced risk and faster time-to-market: the result is a multiple return on investment (ROI).
Summary: when does the Synthetic Data Cloud make sense?
For companies and AI teams that:
- require large amounts of visual data (images, video, sensor data) for various tasks (detection, segmentation, inspection, monitoring, medicine, production),
- must cover a large number of scenarios - including rare, unusual, edge-case, very rare, or difficult-to-capture scenarios,
- need to quickly iterate, test, retrain and develop models - and traditional annotation and data collection are bottlenecks,
a solution like SKY ENGINE AI Synthetic Data Cloud can deliver a significant, transparent, multiple return on investment (ROI). Thanks to automation, savings, speed and flexibility, the risk of delays, excessive costs, or data mismatches is significantly reduced.
Of course - as with any technology - well-designed scenarios, realistic cost assessments, validation tests on real data and a meaningful combination of synthetic and real data are crucial. If we adopt this approach, Synthetic Data Cloud becomes not only a technological tool, but a real element of business strategy, increasing the efficiency, scalability and predictability of AI projects.