Power your AI systems with seamless orchestration across GPT-4, Claude, Gemini, Llama, Mistral, and open-source models. Achieve cost efficiency, reliability, and performance through smart routing, caching, and multi-model resilience.
LLM Orchestration is the coordinated management of multiple Large Language Models (LLMs) across providers to deliver scalable, cost-effective, and robust AI systems. It involves intelligently routing queries, managing model fallbacks, monitoring usage, and optimizing responses in real time — enabling organizations to leverage the best model for every use case.
Dynamically route requests to the most suitable model based on task complexity, latency, cost, or accuracy.
Ensure reliability with automatic failover to backup models during downtime or API errors.
Reduce response time and costs with intelligent caching and fine-grained token usage tracking.
Prevent abuse and maintain fairness across APIs with smart rate limiting, quotas, and authentication layers.
Automatically select the most effective LLM per request using metadata, prompt analysis, or historical performance data.
A/B test new models or prompt templates in production using controlled traffic routing.
Gain deep visibility into latency, token usage, and cost metrics for each provider and model.
Build enterprise-grade AI systems with orchestrated multi-LLM pipelines designed for resilience, efficiency, and transparency.
Dynamically route general queries to cost-efficient models and complex tasks to premium LLMs — automatically balancing performance and budget.
Maintain uninterrupted service with built-in provider failovers, active monitoring, and SLA enforcement.
Compare LLM outputs side by side, benchmark accuracy, and automate model upgrades.
Unified control plane with SSO, detailed audit logs, and organization-level governance.
Roll out and test prompt changes safely in production using traffic shadowing and real-world comparisons.
Guarantee latency and uptime targets with proactive monitoring, rate limiting, and intelligent retries.