Auriko
Auriko is a trading desk for LLM calls, built by ex-quant traders, that arbitrages cost differences across inference providers, promising average 30% cost reduction.
Auriko : Trading desk for LLM calls | Product Hunt
Auriko
Launching today
Trading desk for LLM calls
52 followers
Trading desk for LLM calls
52 followers
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Unified API
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AI Infrastructure Tools
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
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Launch tags:API•Developer Tools•Artificial Intelligence
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In a previous life, I traded options as a quant trader. When I started building with AI agents, I needed to switch models quickly across inference providers. A trader’s OCD for finding the lowest price kept pushing me to figure out which provider was cheapest.
That sent us down the rabbit hole of comparing inference costs. We realized cost is not just the headline input/output token price. A huge part of our spend came from cache pricing, cache-hit efficiency, and routing choices.
We ended up building a system to optimize all of that. And we turned it into auriko.ai.
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3d ago
Is Auriko mainly about monitoring and comparing LLM calls after the fact, or does it help decide where a call should go before it is sent? For developer teams, that distinction matters a lot, especially if they’re juggling quality, latency, and spend across different AI workflows.
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37m ago
Maker
@crystalmei Great question! Definitely pre-request.
When a request hits Auriko, we build the available routing candidate set, apply hard constraints like capabilities, budget, data policy, parameter support, and availability. The routing engine scores every available candidate i across cost, latency, throughput, and success rate, then picks the best one based on your strategy
The request performance data feeds back into routing: we use it to generate provider health and performance signals, then use those signals to calibrate future routing decisions. So the main value is real-time routing, with observability data used to make the router smarter over time.
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11m ago
Maker
Auriko’s core competence is our quant-trading-grade data pipeline, signal generation engine, and inference cost modeling. We track each inference provider’s prompt-caching mechanics, estimate users’ request patterns, and model inference cost with both provider and user signals in mind.
Our data pipeline also generates real-time signals on provider health, latency, and throughput.
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3d ago
The quality bar for production AI apps is high, so cache aware routing needs good observability.
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1h ago
The prompt caching focus sounds valuable. A lot of teams know caching exists but do not optimize around it.
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16m ago
I like that the focus is not just more models, but using the right route for each request.
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6m ago
@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!!
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19m ago
Maker
@grey_seymour Glad you like it!
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10m ago