Launching today
Auriko
Trading desk for LLM calls
139 followers
Trading desk for LLM calls
139 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
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?
The quality bar for production AI apps is high, so cache aware routing needs good observability.
I like that the focus is not just more models, but using the right route for each request.
Typeless
This looks super useful for teams watching their AI bill climb every month. Congrats!
Auriko
@yuki1028 Thanks!
@zxy_action1 Michael... this is jaw-dropping. I am beyond impressed by such a novel yet robust approach to token-spend reduction. My budget loves this!
(my brain, however...? it immediately wants to set about reverse-engineering this mf to tune it towards revenue generation... 😈)
Great work!!
Auriko
@grey_seymour Glad you like it!
Pokecut
Congrats on the PH launch! Modeling request patterns sounds helpful.