Auriko - Trading desk for LLM calls
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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/ll...
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I like that the focus is not just more models, but using the right route for each request.
@tammytan516 Thank you for the support!
@thys_beesman Thanks and great question! It's both.
The routing engine decides where the llm request should go before it is sent. For each request, Auriko builds the eligible provider set, applies hard constraints like capabilities, data policy, budget rules, parameter support, and availability, then scores the viable routes accounting for expected cost, latency, throughput, capacity headroom, and cache economics. The cost model accounts for provider-specific prompt caching mechanics and the workload’s reuse patterns, so routing is based on expected request-level economics, not just static model prices.
We then feed the request metadata back into our signal generation engine for future routing decisions. This helps us maintain a dynamic and effective awareness of the routing candidates.
How do you measure quality when routing across models? Do you use developer feedback, user behavior, retries, or some kind of evaluation layer?
@phoenixhu We measure metadata such as latency, throughput, error rate, fallback, cache-hit and use that to inform our routing engine. And user can check all information in detailed request logs
Congrats on the PH launch! Modeling request patterns sounds helpful.
@anthony_cai Thank you for the support! give it a try!
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?
@abod_rehman Definitely. There are two ways to control this in Auriko.
1. you can set hard constraints, such as a TTFT ceiling. 2. you can use softer control by increasing the weight of response time (TTFT) in the composite routing score. Both affect the routing decision!
@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!!
@grey_seymour Glad you like it!
This looks super useful for teams watching their AI bill climb every month. Congrats!
@yuki1028 Thanks!
Big congrats 🙌 Auriko feels practical and fresh, excited to test how it streamlines collaboration.
@moon10 Thanks!
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.
@fanny_guillou Exactly - trading and routing, this is the way how you maximize your token usage!
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.
@ece9cgh Thanks for the support! Yes! quant trading and model routing share similar magic - arbitrage and optimize!