An LLM gateway for Claude, GPT, Gemini and open models at or below list price
Foundation model for mobile, wearables, robots, smart home, automotive and microcontrollers
Stage
Not given
Startup
Founded
Not given
Not given
Based in
AE
Not given
Pricing model
Paid
Open source
Pricing
Per million tokens, with live pricing capped at list price. Models range from $0.07-$0.27 per million input tokens and $0.23-$1.08 per million output tokens. For example: Claude Opus 5.5 at $1.09 input/$5.45 output per million tokens, GPT-6-Luna at $0.08 input/$0.40 output per million tokens, Deepse
Not given
Key features
Works with existing OpenAI and Anthropic SDKs without code changes
Integrates with Cursor, Cline, Claude Code and Vercel AI SDK
Live pricing capped at model provider list price
60+ models including Claude, GPT, Gemini and open-weight models
Intel TDX trusted execution environment with hardware attestation
Pay-as-you-go billing with no top-up fees
Prompt encryption for open models inside TEE enclave
SayGM Desktop AI agent for local file work
Switch models mid-chat in Desktop
Model routing across competing operators for availability
Tool calling with function selection and argument filling
Structured extraction with guaranteed JSON parsing
Text embeddings for local semantic search and matching
Confidence scoring for routing and gating calls
Laddered subnetwork architecture from 2 to 20 layers
LoRA fine-tuning support for customization
400-4k tokens per second inference speed on Raspberry Pi 5
Python package with pip install and Hugging Face distribution
What makes it different
Up to 70% discount vs list price on frontier and open models
Sealed privacy with hardware-verified Intel TDX attestation anyone can check
Supports all major model providers in one API without SDK changes
Transparent live pricing that never exceeds official model provider rates
Beats models 10x its size on mobile tool calling benchmarks
Matches models 2-3x larger on structured extraction tasks
Entire model in a single 8-29 MB binary for tiny devices
Simple Attention Network architecture uses 2x fewer MFLOPs than transformers
Fine-tuned subnetworks outperform larger models like DeepSeek V4 Flash
SayGM