Fine-tune and serve open-weight models on your own cloud GPUs without managing ML infrastructure
Cut your AI agent bill
Stage
Not given
Not given
Founded
Not given
Not given
Based in
Not given
LT
Pricing model
Freemium
Freemium
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
Proxy API: 15% of savings on provider bill. Models API: $0.40 per 1M tokens removed (Helene 1.1), $0.90 per 1M tokens removed (Adeline 1). Free tier available with Condense Lite (approximately 20% compression).
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
Proxy API for seamless integration requiring only one base URL and header change
Maintains 98.6% cache hit rate
Preserves instructions, questions, answers, code and error messages
Direct billing from provider with no stored API keys
SOC 2 Type II and ISO 27001 compliance in progress
Zero data retention with GDPR export and erasure support
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
Removes 37.3% of bills on average sessions and 66-72% on sessions over 800k tokens
Pay only 15% of savings on Proxy API with no fee if no savings occur
Built around cache architecture rather than against it
Public benchmark allowing comparison with alternatives before purchase
Operates transparently with audit logs and ability to inspect rewritten requests
Tensorant
condense