Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
Stage
Not given
Not given
Founded
Not given
Not given
Based in
SE
Not given
Pricing model
Freemium
Not given
Pricing
SDK: Free for building. Device plans: Garage $0/mo (up to 5 active devices), Pro $49/mo + $0.05 per active device, Scale custom pricing. Onde Cloud: Per million tokens ranging from $0.12 to $6.00 for input tokens, $0.005 to $1.20 for cached input, and $0.33 to $30.00 for output tokens depending on t
Not given
Key features
Native SDKs for Swift, Rust, Flutter, React Native with single unified API
85ms first-token latency on Apple Silicon
Zero server cost for on-device inference
No data egress - inference stays on-device
GDPR and HIPAA compliant by architecture
OpenAI-compatible Onde Cloud API
Global state sync between device and cloud
AES-256 encrypted cloud burst
Data never trained on - no model fine-tuning on customer data
forward_backward() operation for computing loss and accumulating gradients
optim_step() for updating the adapter
sample() for generating from current or saved policy
save_state() to save and resume training state
Tinker API compatibility
Separate trainer and sampler resources
Synchronous and asynchronous RL patterns
Frozen base model weights with LoRA adapter updates
Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
What makes it different
Runs entirely on-device with zero latency round-trips
Built with vertical integration from device runtime to cloud infrastructure
Same unified API across four language platforms with no platform abstraction tax
Compliant by architecture - data residency follows the device
Free tier for development with full SDK access
Managed training and sampling infrastructure, no setup required
Tinker-compatible API for easy migration
Ready-made SFT and RL recipes through Loops cookbook
Onde Inference
Loops