Adopt new pricing models — tiered, volume, stairstep, usage-based, flat-fee, or any custom model, without outgrowing your billing system. Trusted by 6,500+ businesses globally, including world-class companies like Zapier, CodeRabbit, Gorgias, and DeepL.
This is the 6th launch from Chargebee. View more
AUDR by Chargebee
Launched this week
AUDR (Agent Usage Detail Record) is an open standard, for capturing who initiated an agent run and what it cost across every system that run touches. Inspired by the telecom industry's Call Detail Record, AUDR defines a common JSON schema that any harness, router, or billing system can emit and ingest. Three core rules make it work: a shared run ID minted by the harness, clear field ownership, and strict merge rules where conflicts are rejected and corrections are new records.
Launch Team
EneyProactive AI for Mac. Free test, 20,000 credits.
Promoted
Hadn’t thought about this problem before but I can see why it’s coming up now.
thanks for the support, and they welcome contributions. see the public repo: github.com/openaudr/audr
@haozhe_li if you're launching agentic capabilities, chances are you're better off measuring the costs per run. Even if you are choosing to do a flat fee today, in the longer run it will pay off to have this instrumentation.
Congrats on the launch, we created a service that has both x402 calls and non-x402 calls (mcp, api). The non-x402 calls are credit based. I'm trying to understand the advantage of using AUDR versus not using it (problem it solves for us). Thanks, in advance!!
usage based billing is such a headache to build from scratch. glad someone simplified this.
yes, and @Chargebee is a pioneer in this category!
Hi PH community, we’re really excited to launch AUDR.
The telecom industry has always had a standard, carrier-agnostic way of recording usage for each call. Today’s long-horizon agents need a similar standard that works across model routers, gateways, agent frameworks, and the wider AI stack. AUDR (Agent Usage Detail Record) is an open standard specification for recording agent usage and costs in granular detail.
At Chargebee, we help scale monetization for some of the fastest growing AI startups and enterprises. And one thing they all have in common is a granular understanding of AI costs. That understanding informs how creatively and sustainably they price. Whether it’s charging for active agent minutes, resolved outcomes, or an extremely hybrid model, they all share a deep grounding in tokenomics. AUDR is built to enable the same depth for everyone.
We’re thankful to teams at @Merge, @Mastra, and @openhumanfor their early reviews and contributions. We’ve already shipped adapters for NVIDIA NeMo Relay, Merge Gateway, Mastra, Vercel’s AI SDK, and LiteLLM.
Help us improve it at openaudr.dev
@cbkrish S/O for this new launch! psyched to see @Chargebee back on @Product Hunt. looking forward to what's next.
love it, observability becomes more important with scale, something simple like this was definitely missing.
@inferense thank you. Do let us know if you'd like any support from our end in adopting this framework.
@sidhdharth thank you so much. Do join in adopting this framework and give us your feedback on making it better. here's the Git link
A very important problem to address - Most teams I talk to build their own dashboards consolidating cost data from multiple sources but still don't get a clear picture of the actual cost to serve a customer. Knowing which cohort of customers is the most (or least) profitable can help make a lot of decisions in the product/GTM.