Open-source engine for faster local language model inference with bit-for-bit identical output
Stage
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Founded
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Based in
SE
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Pricing model
Freemium
Open source
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
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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
Speculative decoding for faster token generation
Prompt caching for context reuse
Real-time prompt processing while typing
Concurrent request batching
Bit-for-bit identical output verification
Multi-GPU and multi-backend support (CUDA, Vulkan)
Vision model support with image encoding
Mixture-of-experts model support
RAD-1 automatic draft token generation
Cross-platform support for Windows and Linux
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
Exact deterministic inference: all default optimizations preserve bit-for-bit identical output compared to plain decoding
4.5× faster performance on editing tasks on small models compared to llama.cpp
Verifiable reproducibility through receipt system that records all request parameters
Nine optimization techniques enabled by default with ability to disable each individually
No dependency on draft models or external selections for speculative decoding
Onde Inference
Kinetic