Adjust the inputs below to match your current AI usage. InferLayer routes simple and medium queries to a cheaper model, caches semantically similar answers, and only sends genuinely complex queries to your frontier provider.
Your current usage
Queries per day
Current frontier model
input / output per 1M tokens
Avg input tokens / query
Typical question: 50–200 tokens. RAG or long-context: 500–2,000+.
Avg output tokens / query
Short answers: 50–150 tokens. Detailed responses: 300–800 tokens.
% complex queries
25%
Analysis, reasoning, long code = complex. Simple Q&A, summaries, extraction = not complex. Most enterprise workloads: 15–30%.
Expected cache hit rate
35%
Customer support / FAQs: 40–60%. General assistants: 20–35%. Highly unique queries: 10–20%.
Our own benchmark: 50% across 200 mixed queries. Real client workloads vary by repetition pattern.
Projected savings
Current monthly spend
$0
raw API cost, no optimization
With InferLayer AI
$0
optimized spend
Monthly savings
$0
0% reduction in inference cost
Annual savings
$0
projected at same usage rate
Where the savings come from
Semantic cache hits
—
Cheaper-model routing
—
Frontier model (complex)
—
Assumptions: default CPU/API tier (no GPU). Cache hits served at ~45ms, $0 cost.
Simple / medium queries (~75% of total) routed after cache miss to a cheap API model (GPT-4o mini rates) — not a free local model, so savings are the price delta, not 100%.
Complex queries (~25%) routed to your selected frontier model at full price.
Cache hit rate reflects workload repetitiveness — enterprise workloads typically 20–50%.
Benchmarked June 2026 on the default CPU/API tier (no GPU): ~50% cost reduction vs an all-GPT-4o baseline over 100 live queries, 100% on warm-cache hits, and faster than the baseline. Load tested at 100 concurrent users with zero failures. The GPU/local-inference tier benchmarks separately.
Ready to see it live on your actual traffic?
We’ll run a two-week cost audit against your real workload. No commitments.