Launched this week
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 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.
Agnes AI
@ece9cgh Thanks for the support! Yes! quant trading and model routing share similar magic - arbitrage and optimize!
Solid
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.
Auriko
@tkeith Thanks Trevor for your support!
Pokecut
Congrats on the PH launch! Modeling request patterns sounds helpful.
Agnes AI
@anthony_cai Thank you for the support! give it a try!
Typeless
This looks super useful for teams watching their AI bill climb every month. Congrats!
Auriko
@yuki1028 Thanks!
ReplyMind
Big congrats 🙌 Auriko feels practical and fresh, excited to test how it streamlines collaboration.
Auriko
@moon10 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!
Creatium
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.
Auriko
@kelly_king3 Thanks!