SemanticGuard

SemanticGuard is an AI gateway that reduces the costs of calling large language model (LLM) APIs by implementing a self-validating semantic cache. Instead of sending every user query directly to providers like OpenAI, Anthropic, Google, or Vertex AI, SemanticGuard intercepts the request, checks whether a semantically similar query has been cached with a verified response, and returns that response if the confidence threshold is met. This caching mechanism relies on multi-layer verification: the system first performs a semantic similarity match, then applies continuous AI‑judged sampling to validate that the cached response remains correct for the new input. The result is a median cost savings of 50% on LLM API bills while maintaining 100% cache correctness according to the company’s public benchmark. The gateway is designed for developers who need a drop‑in solution: a single line of code wraps any existing OpenAI, Anthropic, or Google AI SDK call. For example, using the `@semanticguard/ai-sdk` package, developers can replace the default `fetch` function with a `withSemanticGuard()` wrapper, and all subsequent API calls are automatically cached and tracked. No changes to the existing prompt logic or configuration are required. The caching works across different model providers, so teams using multiple LLMs can benefit from a unified cost‑saving layer. SemanticGuard also provides a dashboard to monitor cache hit rates, savings, and correctness metrics. The public benchmark page details the methodology used to measure correctness: responses are sampled periodically and judged by an LLM evaluator to ensure that cached outputs are identical in meaning to fresh API calls. This approach prevents common issues like stale or hallucinated responses that could occur with simpler