# LLM Orchestration

LLM orchestration is the capability to manage, route, and govern multiple Large Language Models across an AI platform — selecting the appropriate model for each task based on cost, latency, capability, or compliance requirements. As enterprises adopt AI at scale, reliance on a single LLM vendor introduces risk: model quality evolves, pricing changes, and specific tasks suit particular models. LLM orchestration provides a provider-agnostic abstraction layer. NiCE Cognigy's LLM Orchestration allows organisations to connect models from OpenAI, Anthropic, Google, AWS Bedrock, and others; assign different models to different agent jobs; and update model configurations instantly — maintaining centralised safety settings and cost visibility throughout.

For enterprise teams, LLM Orchestration matters because real-world outcomes depend on how the capability is integrated, governed, and measured — not just on the underlying technology. LLM orchestration is the capability to manage, route, and govern multiple Large Language Models across an AI platform — selecting the appropriate model for each task based on cost, latency, capability, or compliance requirements.

## Key Points

- Neural networks trained on massive text corpora that generate human-like language
- Power the reasoning core of modern AI Agents — interpretation, planning, and response
- Enterprise deployment requires governance: model selection, safety, cost, and compliance
- NiCE Cognigy supports LLMs from OpenAI, Anthropic, Google, AWS Bedrock, and others
- LLM Orchestration enables mixing providers by use case with centralised safety controls

### See how it works in action

- [Watch the demo](https://www.youtube.com/watch?v=U87psHIQF3w)
- [Speak with an expert](/content/contact-us/index.html)
- [Explore Solutions](/content/solutions/ai-agents/index.html)
- [Explore Platform](/content/platform/cognigy-ai/index.html)