AI-native companies ship features weekly.
Their monetization infrastructure usually can’t keep up. Every pricing change means more custom code and more engineering time. 🛠️
So we partnered with @Airwallex. 🧵
✅ Setup is minimal. Connect Airwallex directly from the Stigg UI, with very little integration work on your side.
This works for a PLG motion with credit top-ups and for complex enterprise contracts alike.
You can change your monetization as fast as you ship, without the engineering overhead.
Full announcement on our blog 👉 stigg.io/blog-posts/stigg-ai…
Almost every team builds rate limiting and billing as two separate systems. Different owners, different code paths, different problems entirely.
AI collapses them into one.
Here's what OpenAI learned rebuilding the system that decides, in real time, whether to serve your request. 🧵
The irreducible core, for any team shipping AI:
Your DB must give you transactions (single-shard, multi-record, non-negotiable)
Unify product usage events + billing into ONE system
Bonus: usage events prove you delivered the service (hello, ASC 606)
And the closing warning: don't over-engineer for every imaginable future.
Name the knobs you'll actually need in the next 6-12 months. Build those with correctness first.
"Watch out for those trapdoors. That's where you ask the hard questions."
stigg.io/blog-posts/inside-o…
An agent stuck in a retry loop can burn a customer's entire monthly AI budget in an hour.
Most teams' fix: pad the limits above what customers paid for, and hope. That's not governance.
Enforce on any dimension: model, team, region, agent. Cap the expensive model at 100 req/min, let the cheap one run at 10,000. No shared bottleneck.
The same decision-waterfall the frontier labs built in-house. One API call instead of staffing it forever.
stigg.io/blog-posts/introduc…
