Real-time routing decisions across your workload — cost, quality, and confidence for every request.
NEXUS analyzes each request — task type, complexity, domain, required quality level. Classification runs in <5ms using a fine-tuned routing head, not a full LLM inference.
LinUCB bandit selects the cheapest model whose Brier-calibrated confidence interval covers your quality threshold. Conformal prediction provides distribution-free guarantees.
Every completed request updates the calibration table. Routing accuracy compounds with volume — NEXUS gets measurably smarter with your workload over time.
NEXUS implements peer-reviewed algorithms from calibration literature. Every routing decision has a computable, auditable confidence interval.
Every model prediction scored against ground truth. Confidence intervals anchored to empirical calibration, not vendor claims.
Contextual multi-armed bandit learns your workload distribution. Routing improves as throughput increases — no manual tuning.
Distribution-free prediction intervals with guaranteed coverage at any threshold. Route with statistical certainty.
Single endpoint replaces 6+ vendor SDKs. Claude, GPT, Gemini, DeepSeek, Llama — unified interface, zero lock-in.
Mixed Opus/Sonnet workloads have the most room to cut. The calculator uses conservative routing assumptions — adjust them to match your traffic.
The calculator assumes roughly three-quarters of requests can route to Haiku or Flash — tasks classified as low-to-medium complexity with high model interchangeability. High-stakes tasks stay on premium models.
Cost Calculator
Adjust for your workload.
Paste any AI task description. NEXUS analyzes complexity, selects the optimal model, and shows you exactly how much you would have saved versus always-Opus.
Open Routing DemoAt 500K monthly requests with 60% Opus usage, NEXUS saves $12,000+/month at the Growth tier. Most customers are net-positive in week one.
100K requests/mo
500K requests/mo
2M requests/mo
Annual pricing available: $4,499/yr for Starter
The Brier score measures probabilistic forecast accuracy — lower is better. NEXUS scores every model on your actual task distribution, not vendor benchmarks. When NEXUS says "94% confidence this model meets your quality bar," that 94% is empirically validated against thousands of prior decisions, not a marketing claim.
Most LLM workloads are overprovisioned — tasks run on Opus or GPT-4o when Haiku or Flash 2.0 produces equivalent output for that task type. NEXUS identifies these per-request, routing each to the cheapest model that meets your configured quality threshold. In our internal benchmarks, calibrated routing held quality parity while cutting model spend between 28% and 72% depending on workload mix — your number depends on yours.
You configure minimum acceptable quality per endpoint or task category (e.g., 0.85 for customer-facing, 0.92 for legal review). NEXUS routes to the cheapest model whose Brier-calibrated confidence interval includes your threshold. If no cheaper option qualifies, it routes to the premium model.
Sub-50ms p99 in our current benchmarks. The routing decision is a lightweight inference call against a cached calibration table — not a second full LLM inference. For latency-sensitive applications, routing adds <5% end-to-end overhead.
No. Routing calibration uses your workload's task metadata and output quality signals — not your content. We do not use customer data to train shared models. Tenant isolation is enforced at the calibration layer.
Early access members get 3 months free at the Growth tier, direct access to the engineering team, and input on routing algorithm configuration.
No spam. No share. Just a ping when we open the beta.