Tutorials Prompt Engineering Tutorial
AI Load Balancing — Complete Guide
AI Load Balancing — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
On this page
Prompt Engineering Tutorial · Lesson 88 of 100
AI Load Balancing
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
Load balancing spreads LLM requests across replicas, regions, or providers — with health checks and failover when one endpoint degrades.
Why should you care?
PromptVerse LLM Gateway round-robins Azure OpenAI deployments and fails over to backup region on 503.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
pools = [{ region: "eastus", weight: 60 }, { region: "westeurope", weight: 40 }]
for req in queue:
target = weighted_pick(pools)
try: return call(target, req)
catch 503: mark_unhealthy(target); retry nextWhat happened?
- Weighted pick respects capacity.
- 503 marks pool unhealthy temporarily — traffic shifts without manual deploy.
Practice next
- Configure two API base URLs.
- Simulate 503 on primary.
- Verify failover.
- Add circuit breaker after 5 failures.
- Route embed jobs to separate pool from chat.
Remember
Multi-pool with weights. Automatic failover. Health recovery probes.
Regional outage
East US Azure blip 12 minutes.
Outcome: Gateway shifts EU pool; error rate < 0.1%.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
