Synthetic images and simulation
Synthetic image and simulation vendors render labeled scenes for training and testing perception models. On our list they are Rendered.ai, which publishes plans from $5,000 a month, and Parallel Domain, which serves autonomous vehicles and robotics and does not publish prices.
Every vendor page cited here was last checked on .
Who sells synthetic images and simulation?
| Vendor | What it makes | Data types | Delivery | Published price |
|---|---|---|---|---|
| Rendered.ai | Physically accurate, fully labeled synthetic imagery for computer vision, including SAR, infrared, multispectral and X-ray sensors | Images, 3D | Platform plus services | Platform as a service from $5,000 a month (Teams) and $15,000 a month (Organizations); other services priced on request |
| Parallel Domain | Synthetic data and simulation for training and testing perception systems in autonomous vehicles and robotics (PD Platform, PD Forma, PD Sim) | Images, video, 3D | Software platform | Not published |
Some vendors in this table may pay an introduction fee when a team we put in touch signs with them. Whether a vendor pays has no effect on the list, its order or our notes.
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Why do teams buy simulated images?
Collecting and labeling real images is slow, and some scenes are too rare or too dangerous to film: a child running between parked cars at dusk, a cracked weld in an X-ray, a vessel in heavy sea state on radar. A simulator can produce thousands of variations with perfect labels.
The labels are the quiet advantage. A rendered scene knows every pixel's class and every object's depth, so there is no annotation error and no annotation bill for that portion of the data.
Unlike tabular synthetic data, privacy is rarely the reason. The reasons are coverage, cost per label and the ability to test a model on scenarios you choose.
How do you check that simulated data transfers to the real world?
Hold back a real, labeled validation set that the vendor never sees. Then compare three models on it: trained on real data only, on synthetic only, and on a mix. If the mix does not beat real-only on the scenarios you care about, the synthetic data is not earning its cost.
- Check the sensor model. Noise, lens distortion and resolution should match your actual hardware.
- Check per-class results, not only the average. Gains on common classes can hide losses on rare ones.
- Check the variation controls. Lighting, weather, object placement and materials should be adjustable, and you should know which ranges were used.
The differences from tabular work are set out in synthetic tabular data or simulated images.
Where these facts come from
- Rendered.ai home page, read 7 Nov 2026
- Parallel Domain "What is Parallel Domain" page, read 7 Nov 2026
Synthetic image and simulation questions
What is synthetic image data?
Images rendered from 3D scenes or generated by a model, delivered with labels already attached: bounding boxes, segmentation masks, depth or sensor metadata. Because the scene is built, every label is known exactly.
How much does Rendered.ai cost?
Rendered.ai publishes platform as a service from $5,000 a month (Teams) and $15,000 a month (Organizations), with other services priced on request (Rendered.ai home page, read 7 Nov 2026). Those are list prices, not a quote.
What is the domain gap?
The difference between rendered and real images that makes a model trained on one do worse on the other: lighting, textures, sensor noise, motion blur. Measure it on a real validation set before trusting synthetic training data.
Do I still need real images?
Yes, at least for validation and usually for part of training. Teams typically mix synthetic and real data and measure the effect of each mix on a fixed real test set.
Which sensors can be simulated?
It depends on the vendor. Rendered.ai lists SAR, infrared, multispectral and X-ray imagery on its home page. Parallel Domain focuses on perception data for autonomous vehicles and robotics. Ask for samples from your exact sensor model.
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