
Auriko | One API for Every LLM, Zero Markup, Cache-Aware Cost Arbitrage
使用一个 API 访问和切换多个 LLM 提供商,同时优化推理成本。
justin2025 · Product Hunt
Auriko Trading desk for LLM calls
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使用一个 API 访问和切换多个 LLM 提供商,同时优化推理成本。
justin2025 · Product Hunt
Auriko Trading desk for LLM calls

自动将提示词路由到最便宜的模型,节省API成本。
u/ASDKING100 · Reddit
Launched an AI API router tonight, and found a bug hours in that would've taken real payments without ever upgrading the account Built LLMLite over the past few weeks — it classifies each prompt and routes it to the cheapest model that can actually handle it, instead of hitting GPT-4o for everything. Free tier, no card needed to try it. Tonight, right as I was about to launch, ran a real transaction to test the payment flow end to end. Paddle processed it, webhook fired, signature verified

Compress LLM prompts and docs to reduce token usage and API costs.
@marcusyul · X
THEY JUST GAVE AWAY 100 MILLION FREE TOKENS SO YOU CAN STOP BURNING THROUGH YOUR CLAUDE CODE BUDGET. if you code with AI you already know: the session fills up, starts failing, and on top of that you're overpaying there's a tool that fixes this: it shrinks the context before the model even sees it same model, same response, a fraction of the cost in a real session: from $154 to $43. a 72% drop and right now: → extend your Fable sessions in Claude Code → 100M free tokens to try it out you don't switch models you don't touch your code you just stop paying to repeat yourself link below ⬇️


Ornymo通过语义缓存减少LLM查询成本和延迟。
u/ornymo_official · Reddit
how to reduce ai costs there are lots of way to reduce costs but there all complex to setup i know this cause i tried one in production so i built ornymo we cache meaning not the exact string allowing us to give same awnsers thus reducing llm costs and latency check it out at ornymo.com free for a limited time and let me know your feedback submitted by /u/ornymo_official to r/buildinpublic [link] [comments]

为代码智能体工作流管理 API 密钥和使用预算。
u/Zyron_X · Reddit
I built a service for people to use Codex API without 5-hour limit disruption I built a small service for people who use the OpenAI Codex API regularly and want more predictable usage without the 5-hour or weekly limits. It currently provides: Frontier OpenAI models (GPT 5.6 family included) Managed API key Monthly usage budgets depending to plan No 5-hour limit No weekly limit Under the hood, it is built on top of an open-source project and proxies requests to

通过实时压缩提示词和检测重复工具调用,将AI代理的Token成本降低40-70%。
@DeveloperL92487 · X
I built my first app in 60min And now I got $500 MRR in one month Check here if you are interested It’s a tool to reduce agent token consumption, speed up agent response, and clean up memory cache

Continuum是统一的AI聊天与编程应用,支持Claude、Grok等100+模型。
@ContinuumCode · X

开源提示词压缩,在API调用前压缩输入以降低LLM成本。
@asgujjuasitgets · X

用一个 API 访问 500+ Hugging Face 开源模型,免费注册无需信用卡。
@pengsonal · X
500+ Hugging Face models through a single free API no credit card just an email signup 😳 launched on July 3 as a unified gateway for Hugging Face models one API key one endpoint 500+ models what you get for $0: • 500+ open-source models through an OpenAI-compatible API • no separate API keys for different providers • works with Cursor, Claude Code, Hermes, OpenCode, and anything that supports a custom base URL • email signup only setup takes about 2 minutes: 1. Go to 2. Sign up with your email 3. Generate an API key 4. Set your base URL to 5. Choose any model from the catalog and start building a few things worth knowing: • is a third-party gateway, not an official Hugging Face product • the free tier is rate-limited, but the exact limits haven't been published yet • i wouldn't build production apps that depend entirely on a free aggregator what i like is the simplicity instead

统一API网关,审计Token消耗并分析多个AI提供商的成本。
@amiuchat · X
我做了一个桌面工具:Token Switch。 给 Codex / Claude Code / OpenCode 重度用户统一管理 Provider、模型、Token、Base URL、连通测试和消耗统计。 多个 Agent,一个 Token 工作台。欢迎试用,也欢迎吐槽你最烦的配置问题。

查看LLM模型在10个基准问题上的评分和排名。
fristovic · HN
She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol