When was the last time you deployed a rate limit change without fearing you’d break something?
Everyone “knows” how to rate limit…
Basic rate limiting is easy:
A single line in nginx, a middleware in Express, or a throttling rule in AWS API Gateway.
But here’s what happens next:
- A single client accidentally fires 1000 requests per second and burns your budget.
- You deploy a new limit — and break an integration with a key partner.
- You realize you have no idea which rules are working, and which are dead code.
…but no one knows what’s really going on
Most rate limiting is:
- ❌ Opaque – you don’t know who was limited or why
- ❌ Static – same rule for everyone
- ❌ Hardcoded – changes require code + deploy
- ❌ Disconnected from business – all users treated equally, even if they pay very differently
Rate limiting should be a product feature
Imagine this:
- You can dry-run a new limit and see exactly who would be affected — before rolling it out.
- You define a rule like:
>100 requests/hour on POST /chat — but only for free-tier users - You configure this without code and without redeploying.
- You get a dashboard showing which rules are actively protecting you — and which aren’t.
This is rate limiting as a business function
A real system should:
- 🔒 Protect your resources in line with product strategy, not just infrastructure constraints
- ⚖️ Enforce fair usage — without punishing your best customers
- 📊 Make API behavior predictable, testable, and governable
What’s next?
I’m building a tool to make this kind of rate limiting possible.
But that’s for another post.
For now, I’d love to hear:
💬 How do you test or roll out rate limiting rules in your systems?
Do you dry-run them? Segment by customer type? Track effectiveness?
🧠 Drop your thoughts in the comments — I’m collecting real-world patterns for API governance and abuse control.