What is LLM Gateway?
An abstraction layer between an application and language-model providers, allowing models to be swapped, routed, and managed without changing application code.
Building directly against one model provider's API couples your product to their pricing, availability, and model lineup. An LLM gateway inserts a provider-agnostic interface: the application asks for a completion; the gateway decides which provider and model serves it.
Gateways centralize the operational concerns of production LLM use — provider failover, model upgrades, cost tracking, and consistent handling of streaming and errors — in one place instead of scattered through the codebase.
For buyers, a gateway architecture is a resilience signal: the product is not held hostage by a single model vendor's outage or deprecation schedule.
How BeforeQuery uses this
BeforeQuery's generation layer runs through a provider-agnostic LLM gateway, keeping retrieval, grounding, and citations independent of any single model vendor — models can improve underneath while the answer contract stays the same.
Related terms
RAG (Retrieval-Augmented Generation)
An AI architecture that retrieves relevant documents from a trusted knowledge source and passes them to a language model, so answers are grounded in real content instead of the model's training data.
Eval (LLM Evaluation)
A systematic method for measuring the quality of AI-generated answers — often using a second model as a judge — so quality is tracked rather than assumed.
Prompt Injection
An attack in which adversarial instructions are placed in user input or in retrieved content to manipulate an AI system into ignoring its rules or leaking data.
From definition to deployment
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