Confidential and open LLM inference for agents, paid per request in USDC
Stage
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Founded
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Based in
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US
Pricing model
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Paid
Pricing
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LLM inference: Models priced per million tokens input and output, ranging from $0.043 to $3.63 for input and $0.22 to $18.15 for output (e.g., Qwen3.8 27B route C: $0.043/$1.375, Kimi K3 route B: $3.63/$18.15). Virtual machines: Starter $0.159-$0.169/hour, Standard $0.318-$0.335/hour, Pro $0.636-$0.
Key features
Constant-state attention: models serve any context length from the same memory per request
HCGM: models run from disk storage instead of memory
M-Rail: context processed by a model can be reused instantly in future requests
CPUDA/sGPU: run unmodified CUDA programs on CPUs with bit-identical results
Organ grafting: transfer capabilities between models without training
Personal instruction set (ISA) compiled on device from phone signals
Population ISA: compile laws from many people without personal records
Digital twins of customers, fleets, cities or markets
Results identical across all machines and architectures
Computation cost follows novelty not data size
Attested confidential model execution on Intel TDX hardware
Open tier LLM inference without attestation
Price shown before model execution
No account or API key required
Virtual machines for AI swarms by the hour
Phone numbers and calling with metered pricing
Email inboxes with sending allowances
Access via HTTP without signup
416 open source models available
What makes it different
Constant memory cost regardless of context length or data size
Exact reuse: every computed result is kept and reused instantly
Portable execution with bit-identical results across all hardware
Models run from storage without retraining or fitting in memory
Privacy-preserving: raw data never leaves the device
Encrypted memory and disk in processor with no company access to keys
Price transparent and settled before request execution
No account signup, payment via wallet address
Automatic enforcement of calling and messaging consent rules