Pre-Flight Cost Navigation
Knows what inference will cost before it starts.
Like a Roman augur reading signs before battle, AUGUR predicts inference cost before the GPU fires. φ-weighted math estimates output tokens, selects the cheapest model that meets quality thresholds, and prevents over-allocation waste.
Every prompt → Most expensive model → Hope it's worth it
Every prompt → φ-estimate → Right model, right cost
Prompt complexity is classified into 5 tiers based on vocabulary, structure, and domain markers.
φ-weighted formula predicts output token count based on input length, model characteristics, and task type.
Cost-optimal model is selected. Simple queries → small model. Complex reasoning → full model. Zero quality loss.
Post-inference, actual vs. predicted tokens are compared. The φ-weights self-calibrate with every request.
Pre-flight cost estimation provides full transparency on resource allocation per request. Auditors can verify that every inference decision was cost-optimal.
Unexpected cost spikes reveal prompt injection attacks or abuse patterns. AUGUR flags anomalous token predictions as security events for AEGIS review.
Hard per-request and per-session cost caps prevent runaway spend. If AUGUR estimates exceed the budget, the request is downgraded or queued — never overspent.
AUGUR + STENO = double savings (route cheap + compress output). AUGUR + ORACLE = triple savings (route cheap + compress + eliminate repeated context). The compound effect is the moat.
| Metric | Without AUGUR | With AUGUR | Impact |
|---|---|---|---|
| Cost predictability | ±40% variance | ±8% variance | 5× more predictable |
| Model over-allocation | 30-50% of spend wasted | 3-5% waste | 90% reduction |
| Integration effort | N/A | 0 lines (automatic) | Zero engineering |
| At 1M req/month | $22,500/mo | $15,750/mo | $6,750/mo saved |
AUGUR activates automatically through the api.destill.ai/v1 proxy.