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
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.
Smart angle. LLM costs are getting complex fast when you're routing between multiple providers and models. How do you handle latency tradeoffs when optimizing for cost? Sometimes the cheapest call isn't fast enough for real-time use cases.
Optimizing for the expected cost of the full session instead of the cheapest individual request is the interesting part here. Does the routing model also account for context continuity beyond cache economics—for example, provider-specific differences that could cause subtle behavioral drift during a long agent run?
The quality bar for production AI apps is high, so cache aware routing needs good observability.
Auriko
@nicole_h94 Thanks for the feedback! We have detailed log and audit trial for all llm request and all management api key. The goal is to provider enterprise grade control and observability.
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.
Agnes AI
@shawn_idrees Yes the mission of Auriko is to free you up from the tedious work of choosing models!
I like that the focus is not just more models, but using the right route for each request.
Agnes AI
@tammytan516 Thank you for the support!
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.
Agnes AI
@fanny_guillou Exactly - trading and routing, this is the way how you maximize your token usage!