Model Router
Route prompts by cost, risk, latency, domain, or task complexity across model providers.
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Private LLM Runtime
NextLLM
Route, evaluate, observe, and govern frontier, open-source, and local language models.
NextLLM lets enterprises compare and operate models across OpenAI, Anthropic, Gemini, Ollama, vLLM, and private GPU clusters with a single quality and policy layer.
Positioned to replace
OpenAI GPT 5.5, Anthropic Opus 4.7
Multi-model
Cloud, local, and open-source
Eval
Quality, cost, latency testing
Private
Run sensitive workloads locally
What Makes It Different
A productized NextBrick operating layer built around real enterprise workflows, not a thin wrapper around one vendor.
Evaluation Harness
Compare factuality, citation quality, latency, and token spend before production rollout.
Local Runtime Control
Operate Ollama, vLLM, and GPU-backed models with observability for memory and throughput.
Policy Guardrails
Apply organization-wide prompt rules, output controls, and sensitive-data protections.
On-Prem Agentic Inference Battlecard v1.0
$3.5M annual savings for an 8 calls/sec workload.
Self-hosted inference with ReAct agent runtime, smart routing, and 4x the throughput of single-model Ollama.
Battlecard
Versus cloud LLM APIs
$3.2M saved vs GPT 5.4
$3.4M saved vs Claude Sonnet
$2.5M saved vs Gemini 3.1 Pro
| Model / Runtime | QPS | p50 Latency | Tokens/sec | Disk |
|---|---|---|---|---|
| NextLLM v1.0 | 4.14 | 1.40 s | 178.9 | shared |
| llama3.2:3b | 1.04 | 6.67 s | 48.3 | 1.88 GB |
| granite3.3:2b | 0.86 | 9.09 s | 36.8 | 1.44 GB |
| phi3.5:3.8b | 0.88 | 7.73 s | 38.0 | 2.03 GB |
| smollm2:1.7b | 1.02 | 7.51 s | 49.8 | 1.70 GB |
| qwen3:1.7b | 1.18 | 5.86 s | - | 1.27 GB |
Smart routing cuts calls by sending lightweight tasks to lightweight models.
Prompt caching and RAG can cut repeated input cost by up to 90%.
Phi, Granite, Llama, Mistral, and Qwen can run self-hosted with no per-token billing.
ReAct orchestration and prebuilt enterprise connectors support tool use and multi-step reasoning.
Use Cases
- Private copilots
- Model benchmarking
- LLM cost optimization
- Regulated AI workloads
Why Teams Choose It
- Avoid single-model lock-in
- Control model spend
- Improve answer quality
- Keep private data private
Product Modules
The product is packaged into clear capabilities so teams can adopt incrementally and expand as the platform matures.
Model Catalog
Router
Evaluations
Prompt Rules
GPU Monitor
Cost Analytics