Auriko

Trading desk for LLM calls

API

Developer Tools

Artificial Intelligence

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A 30% inference cost reduction that requires zero change to how our teams build is a rare operational win, and treating providers as trading venues is a genuinely clever framing.

This is so good! We are constantly experimenting with different model providers and from testing this out so far, it's worked great, especially compared to other model routers.

the cost angle makes sense but I'd worry about behavioral drift - even at the same nominal price point, different providers running "the same model" can have different quantization, latency profiles, or subtle output differences. if you're routing a request to whichever venue is cheapest at that moment, how do you keep output consistency for something like a customer facing agent where behavior needs to stay predictable

How does Auriko handle providers with different caching rules? Some make caching easy to reason about, while others expose less detail. Does Auriko normalize all of that for developers?

Love that you guys came from the quant trading world and applied real arbitrage logic to LLM routing instead of just defaulting to whatever provider has the shiniest SDK. The benchmarking transparency page is a nice touch too.

Congrats! A trading desk for LLM calls is a framing I haven’t seen before and it clicks immediately, model costs do behave like a market. My question: when Auriko routes a call to a cheaper model to save money, how do I protect quality? Can I set a floor per task type? Saving 40% on inference means nothing if my customer-facing outputs get worse and I find out from a complaint.

The trader instinct behind this makes complete sense to me. Treating those choices like a live market feels like the sort of thing only people who have lived it would ever think to build.

A trading-desk framing for LLM calls makes sense. Once teams have more than one model and more than one workload, the real work becomes routing, cost control, and knowing why a call behaved the way it did. The audit trail matters as much as the cheaper token path.

treating LLM providers as trading venues is a genuinely smart framing from people who understand arbitrage. token price differences between providers are real and most teams just pick one model and stick with it out of inertia. the cache behavior optimization is the part i'd want to dig into more, prompt caching can drop costs dramatically on repetitive agent workloads but only if you're structuring requests to actually hit the cache. does auriko handle that automatically or does it require some setup on how you're sending requests?

This is a smart wedge most teams are eating unnecessary inference cost simply because provider selection is usually a one time decision baked into the code rather than something dynamic. A 30% reduction is meaningful at scale. Would love to know how request quality is scored in your benchmarks, and whether the savings hold up for latency sensitive production workloads or mainly batch use cases. Excited to see this evolve bookmarking for our team's eval.

someone on my team has been comparing different inference providers manually to keep costs under control. I'll definitely share Auriko with them because it could save a lot of effort.

Can developer set their own priorities, like preferring lower latency over lower cost, or is the routing fully automatic?

quant background makes sense for this, arbitrage is fundamentally about finding mispriced spreads and providers pricing caching differently is exactly that. the tension I'd want to understand: prompt caching usually rewards staying on the same provider for a session so the cache stays warm, but a router optimizing per-request could bounce a session across providers chasing the best price each time and never let any single cache warm up. does the routing engine account for cache-state as its own signal, like "this provider already has a warm cache for this context, don't move away from it even if a competitor is nominally cheaper this instant"

The 30% cost reduction number, is that on top of what you'd already save by using OpenRouter or similar, or is that the comparison baseline?

Congrats on the launch!
For teams running agents that have really strict latency requirements, can you set a hard ceiling on response time and let Auriko optimize cost within that constraint, or is it more of a balance between the two?

Big congrats 🙌 Auriko feels practical and fresh, excited to test how it streamlines collaboration.

Love the “trading desk for LLM calls” framing. Cost optimization across providers is becoming a real pain point as AI apps scale.

How do you balance cost savings with output quality and latency, especially for production workloads?

Congrats on the launch! , Doesn't cheapest-per-request routing fight with caching though? If you hop providers to save on one call you lose the warm cache you built at the last one, and the next 20 calls cost more. Curious if the router accounts for that or just prices each call on its own.