Auriko

Most multi-provider LLM setups start as a routing shim someone wrote on a Friday and end as the most load-bearing piece of infrastructure nobody owns. Auriko is a commercial inference gateway that exposes a single OpenAI-compatible API endpoint across more than a dozen providers — OpenAI, Anthropic, Google AI Studio, DeepSeek, Fireworks, Together AI, and others. You point your existing OpenAI SDK at Auriko’s base URL, pass routing parameters in the request body, and the platform selects a provider per request based on cost, latency, throughput, or a custom objective you define.

The differentiating claim is cache-aware cost arbitrage. Rather than comparing flat per-token prices, the vendor states Auriko models how your prompt patterns interact with each provider’s specific prompt-caching mechanics — so a workload with high cache-hit potential routes differently than one with low repetition. This sits alongside real-time signals on provider health, capacity, and your own usage patterns, which feed into routing decisions rather than acting as a passive dashboard. Budget controls operate at workspace or API key level with dollar-denominated caps and alerts, so staging and production environments can be isolated financially without separate accounts.

The platform fits teams running inference at a scale where provider outages, cost overruns, or cache inefficiencies have already caused production pain. It integrates with the OpenAI Agents SDK, Anthropic’s Claude Agent SDK, Google ADK, LangChain, LlamaIndex, CrewAI, and Vercel AI SDK, among others — meaning most existing agent frameworks can route through it with minimal code changes. Where it breaks: there is no self-hosted deployment option, so teams operating under strict data-residency mandates cannot keep traffic off Auriko’s edge network, even with ZDR-flagged routing. Teams in that position typically fall back to self-managed Litellm or a provider-native gateway.