Launching today
Auriko
Trading desk for LLM calls
326 followers
Trading desk for LLM calls
326 followers
Auriko treats LLM providers as trading venues and arbitrages the spread. Built by ex-quant traders, Auriko’s cost-arbitrage engine calibrates to each user’s request patterns and selects optimized inference paths based on token price, cache behavior, latency, reliability, and request quality. Auriko benchmarks show average 30% cost reduction against industry peers and direct providers. See the source: https://www.auriko.ai/reports/llm-cost-arbitrage
Triforce Todos
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?
Can developer set their own priorities, like preferring lower latency over lower cost, or is the routing fully automatic?
How do you measure quality when routing across models? Do you use developer feedback, user behavior, retries, or some kind of evaluation layer?
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.
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?
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.