Connect all your tools to AI agents securely. One gateway, zero complexity.
Token optimization → real-world energy
Conduit strips the tool-definition bloat, handshake overhead, and redundant context that AI agents would otherwise burn on every single call. Here's the inference energy that never had to be spent — and what it never had to cost.
Assumes ≈ 0.10 Wh per 1,000 optimized tokens (input-weighted). How we calculate this →
The tokens optimized away and cost avoided numbers are measured from live platform telemetry — every CLI call bypasses the MCP handshake, and every tools/list is trimmed to connected vendors with capped descriptions.
The kWh figure is an estimate: we multiply measured tokens saved by an assumed energy intensity. Because Conduit mostly saves input tokens (prompt/context, cheaper than generated output), we default to a conservative input-weighted midpoint:
0.05 Wh / 1,000 tokens0.10 Wh / 1,000 tokens0.30 Wh / 1,000 tokensPublic per-query figures span ~0.24 Wh (Google Gemini, 2025) to ~2.9 Wh (older estimates); our per-token band sits deliberately at the conservative end. Toggle a scenario above — notice only the energy layer moves; tokens and dollars, being measured, stay put.
Route AI agent requests through authenticated, audited connections. Every call logged and traceable.
Connected-vendor filtering, capped tool schemas, and a CLI fast-path cut the tokens every call would otherwise burn.
Role-based access, API keys, and usage tracking per organization. Control who accesses what.
Fewer tokens means less inference compute — savings we surface back to you in tokens, dollars, and kWh.