Orchestrating Large Language Models (LLMs) with AI Gateways
This article discusses the need for LLM orchestration to manage multiple LLM providers, models, and configurations. It explains the core components of LLM orchestration and how an AI gateway can simplify this process compared to a custom DIY approach.
Why it matters
As enterprises scale their use of LLMs across multiple providers and teams, LLM orchestration becomes critical to manage costs, reliability, and governance.
Key Points
- 1LLM orchestration is the practice of managing multiple LLM providers, models, and configurations through a unified control layer
- 2Key orchestration features include routing, failover, load balancing, cost governance, caching, and observability
- 3An AI gateway provides these orchestration capabilities out-of-the-box, reducing engineering overhead compared to a custom DIY solution
- 4The article showcases Bifrost, an open-source AI gateway that provides high-performance LLM orchestration with low overhead
Details
As teams start using multiple LLM providers and models, they often end up with a tangled mess of provider-specific SDKs, manual failover logic, and poor cost visibility. LLM orchestration solves this problem by providing a unified control layer to manage routing, failover, load balancing, cost governance, caching, and observability. Without orchestration, teams can face 15-30% higher LLM costs from duplicate calls, multi-minute outages during provider incidents, and zero per-team cost attribution. An AI gateway like Bifrost makes LLM orchestration practical by providing these capabilities out-of-the-box, reducing the engineering effort required compared to a custom DIY solution. Bifrost offers features like config-based weighted routing, automatic failover, built-in load balancing, budget enforcement, semantic caching, and real-time observability - all with low overhead and high throughput.
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