Fine-tune and serve open-weight models on your own cloud GPUs without managing ML infrastructure
The operating system for AI applications
Stage
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Founded
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Based in
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VN
Pricing model
Freemium
Paid
Pricing
Free: $0 (1 training run per month, 1 model deployment, 1 member); Starter: $49/month (5 training runs per month, 5 model deployments, 1 member); Pro: $199/month (unlimited training runs, 1,000 model deployments, 10 included members, $30/month for each extra seat); Enterprise: custom pricing. Free i
Pay-as-you-go pricing varying by model and resolution. Example: Happyhorse 1.1 Image to Video at 480p costs 819.97 ₫ per second (special offer ending 10/17/2026); OpenAI Sora 2 Pro at 720p costs 7.950 ₫ per second; Google Veo 3.1 Lite at 720p (no audio) costs 795 ₫ per second.
Key features
Train models with SFT, DPO, or RL using LoRA or QLoRA
Monitor training with real-time loss, time and cost tracking
Evaluate models by comparing base and tuned performance
Serve models as OpenAI-compatible API or wake-on-demand
Support for vision models including Gemma 4 and Qwen3.8
DPO preference learning from paired answers
Run budget-controlled experiments with hour and price limits
Download adapters as standard LoRA files or publish to Hugging Face
Single gateway to all AI models
Switch models freely without vendor lock-in
App Store for product discovery and distribution
Support for 400+ models
Cost control through pay-as-you-go pricing
What makes it different
No training scripts required; workflow runs in console, CLI or via AI agent
GPU costs billed by your provider with zero markup from Tensorant
Data stays in your own storage; Tensorant keeps only workflow records
Free plan covers complete workflow on one model from import to API
Models are downloadable and portable to other platforms
One integration to access any AI model without rebuilding for provider changes
Built-in marketplace for discoverability instead of manual user acquisition
Network effects that increase value for all participants as adoption grows
Operating layer that creates value for ecosystem rather than extracting it
Tensorant
AVIS