LLM Platforms
Production Pattern

Xenith LLM Platform

Model routing, caching, and cost controls between your apps and every LLM provider.

Xenith LLM Platform is a model gateway that sits between your applications and every LLM provider (OpenAI, Anthropic, Google, and open-weight models on vLLM). It routes each request to the cheapest model that passes the eval threshold for that task, caches repeated prompts, enforces per-team keys and quotas, and logs prompts and outputs for audit. We deploy it in your account and hand over the routing rules and dashboards.

Typical connect: 1 week
Scoped to your deployment

Routes across GPT-4o, Claude 3.5, Gemini, Llama, and Mistral. Best model for every task.

Deployed as a scoped engagement for your stack - not a self-serve trial or live sandbox.

Xenith LLM Platform: Model routing, caching, and cost controls between your apps and every LLM provider.

How it works

  • Intelligent routing: cheap models for simple tasks, powerful models for complex ones
  • Semantic caching: identical or near-identical queries served from cache
  • Cost optimisation engine with real-time spend dashboard
  • Prompt versioning and A/B testing built in
  • Observability: latency, cost, and accuracy per model, per endpoint

Who this is for

AI product teams spending too much on OpenAI and wanting 40-60% cost reduction
Engineering teams managing multiple LLM integrations from one place
Organisations needing fallback routing when a provider goes down
Teams running prompt experiments and needing structured versioning

What's included

Typical engagement deliverables

Scoped to your stack and compliance needs. We deploy the working system into your environment and hand over runbooks - not a sandbox login.

1

Your existing LLM traffic flowing through the platform

2

Cost dashboard showing spend by model, endpoint, and team

3

Routing rules configured for your use cases

4

Semantic cache live (typically cuts 20-35% of API calls immediately)

5

Slack alert set up for spend anomalies

See it in production

Case study

Financial risk RAG POC: ~$40K vs a ~$300K fine-tuning proposal

Read the results

From the blog

LLM selection guide 2026: the no-BS enterprise buying guide

Read the guide

Want Xenith LLM Platform in your environment?

Book a 30-minute call. We'll map your use case, confirm fit, and outline a deployment timeline - no self-serve trial, no pitch deck.

Typical connect: 1 week from kickoff.