Components · Security

Bot management

Tells humans from bots with fingerprints and challenges.

Lesson
Coming soon
Zone
edge
Place it
6 cr on the blank canvas, free inside a problem

At a glance

Did you know
Bot managers score clients by behaviour and fingerprint, not by IP address alone.
Rule of thumb
Challenge suspicious clients instead of blocking them, to spare real users.
Watch out
Bots scrape or stuff credentials; challenges annoy real users.

Its settings in ScaleMySystems

What you can change on it in the lab, and what each number does.

Concurrency (slots)
Calls it can work on at once (per instance when autoscaling). Needed slots ≈ calls per second × seconds each takes. Extra calls wait in the queue, then are rejected.
Queue capacity
Calls allowed to wait for a free slot. A bigger queue absorbs bursts but adds waiting time; 0 rejects straight away.
Latency (p50) (ms)
Typical time one call takes here, before any calls it makes itself. Slots are held for this long.
Latency (p99) (ms)
The slowest 1% of calls. Callers must set their timeout above this or they cut off healthy calls.
Attack block rate
Share of the attacks it covers that it stops. The rest pass through and are served like real users.
False positives (real users blocked)
Share of real users it wrongly blocks. Stricter rules raise the block rate and this together.

Problems that start with it

  • The WAF blocks real users A strict WAF rule set blocks 4% of real users as attackers.
  • ChatGPT: from launch build to real traffic A chat product on launch-week hardware: the GPU prefill and decode pools, the KV cache store, moderation and the chat API are an eighth of the size real traffic needs. 300 chats a second arrive with long prompts. Answers queue for 20 seconds and most fail.

Full lesson coming soon

Lessons are being added one component at a time: how it works, its settings, a live exercise to break and fix it, interview questions and a short check. See the Circuit breaker lesson for what one looks like.