Image and video decision
TRELLIS2
A competent technical user can build a minimal, self‑hosted image→GLB pipeline using the microsoft/TRELLIS research code and common infra; the vendor's proprietary trained SLAT weights are the main durable advantage, so running your own system loses that model quality but is practical to implement and operate.
Visit website↗$19/mo
$228/yr
Read off the official pricing page.
$100one-off120 h to build
$800/mo12 h/mo upkeep
On cash alone, building overtakes the subscription at 43 seats.
The code exists. It is not what you are paying for.
This project is real, published, and does the core job — and this page still says keep paying. What the subscription buys is proprietary models, and none of that ships in a repository. Fork it anyway if you want to. Go in knowing what it does not carry. What stays hard ↓ · All TRELLIS2 alternatives, with the arithmetic →
What a replacement has to do
- Upload image → run image-to-3D inference → generate/export GLB → preview and store result
What it still won’t have
- Access to the vendor-trained 2B-parameter SLAT model and its tuned weights
- Polished UI/UX, managed queueing, and production SLA/scale
- Integrated cloud storage and account management refinements
- Commercial-grade dataset curation, monitoring, and model updates provided by the vendor
What remains hard
- Proprietary models
Built on Microsoft Research's Structured LATent (SLAT) architecture, it supports up to 2 billion parameters trained on 500K+ diverse 3D objects.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 43 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build an image-to-3D microservice using Python + FastAPI for the backend, a React frontend with three.js for GLB preview, and Docker for deployment. Use the microsoft/TRELLIS repository as the inference backbone (adapt its model code to produce meshes/3D-gaussians and textures) and run inference on a single GPU instance (AWS/GCP). Implement: 1) authenticated image upload and validation (JPG/PNG/WebP), 2) inference worker that accepts uploads, runs model, produces optimized mesh + 4K texture and exports GLB, 3) an artifact store (S3) and a small Postgres DB for jobs, credits, and metadata, 4) a React UI showing progress, 3D preview, and download/export, and 5) an HTTP API for single-image generation and GLB retrieval. Out of scope: retraining large models, building a 2B-parameter proprietary model from scratch. Include error handling, retry/backoff for worker failures, basic unit/integration tests for API endpoints, and containerized deployment manifests (Docker Compose or Kubernetes manifests).
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score32
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productTrellis 2 - Image to 3D Model Generator
- official pricingPricing | Trellis 2
- open sourcemicrosoft/TRELLIS
Integrity checks
What held up, and what did not.
