Scaling AI Systems: Lessons from Processing Millions of Requests

scaling AI from thousands to millions of requests... sounds simple, right?

wrong. it's one of the most complex technical challenges i've tackled. when i joined Writesonic, we were processing about 15k AI requests daily. by the time i left, we were handling over 2 million requests per day.

here's what nobody tells you about scaling AI systems: it's not just about adding more servers.

the brutal reality of AI scaling

let me start with a story that'll save you months of pain...

three months into scaling Writesonic, our AWS bill hit $47k for a single month. our CEO called an emergency meeting. "either fix this or we're shutting down the AI features."

that's when i learned that scaling AI isn't just about performance - it's about making the economics work.

the three scaling killers

after working with dozens of AI systems, i've identified three things that kill scaling efforts:

1. naive API usage: treating every request as independent
2. infrastructure sprawl: adding resources without optimization
3. monitoring blindness: not knowing where your bottlenecks actually are

fix these three, and you're 80% of the way there.

the caching strategy that changed everything

here's the single most impactful optimization we made at Writesonic:

semantic caching (not just response caching)

traditional caching looks for exact matches. but AI requests are rarely identical. users ask the same questions in different ways:

• "write a blog post about AI"
• "create an article on artificial intelligence"
• "help me write content about AI technology"

same intent, different words. semantic caching uses embeddings to identify similar requests and serve cached responses.

result: 67% cache hit rate, $23k monthly savings in API costs

the three-layer caching architecture

we implemented caching at three levels:

1. CDN layer: static responses and common queries (99.9% hit rate)
2. application layer: semantic caching with Redis (67% hit rate)
3. model layer: GPU memory caching for model weights (eliminated cold starts)

load balancing that actually works for AI

traditional load balancing doesn't work well for AI workloads. here's why:

• AI requests have wildly different processing times (100ms to 30s)
• model loading creates significant cold start delays
• GPU memory usage varies dramatically by request type

intelligent request routing

we built a routing system that considers:

• request complexity: simple requests go to lightweight instances
• model affinity: route to instances with the right model already loaded
• queue depth: avoid overloading any single instance
• geographic proximity: reduce latency for global users

impact: average response time dropped from 3.2s to 1.1s

the cost optimization playbook

reducing our AWS bill from $47k to $16k per month while increasing capacity 10x required a systematic approach:

1. right-size your infrastructure

we were running GPU instances 24/7 because "what if we need them?" turned out, we only needed full capacity 4 hours per day.

solution: auto-scaling based on queue depth and response time targets

savings: $18k/month

2. optimize model serving

running separate instances for each model was killing us on costs.

solution: multi-model serving with dynamic loading

• load models on-demand based on request patterns
• share GPU memory across multiple models
• implement model quantization for smaller memory footprint

savings: $8k/month

3. smart batching

processing requests one by one is incredibly inefficient for AI workloads.

implementation:

• group similar requests into batches
• dynamic batch sizing based on GPU memory and latency targets
• timeout mechanisms to prevent batch delays

result: 4x throughput increase with same hardware

the mistakes that cost us $30k

let me share the expensive lessons so you don't repeat them:

mistake 1: over-provisioning for peak load

we sized our infrastructure for Black Friday traffic... every single day. turns out, our peak was 10x our average, but only lasted 2 hours.

lesson: build for average load with burst capacity, not constant peak capacity

mistake 2: ignoring request patterns

we treated all requests equally. but analysis showed:

• 40% of requests were variations of the same 100 prompts
• 20% of users generated 60% of requests
• request complexity followed a power law distribution

lesson: optimize for your actual usage patterns, not theoretical uniform distribution

mistake 3: premature optimization

spent 3 weeks optimizing model inference speed... only to discover that network latency was our real bottleneck.

lesson: measure first, optimize second. always.

monitoring that actually helps

standard application monitoring doesn't work for AI systems. you need different metrics:

AI-specific metrics that matter

• token throughput: tokens processed per second (not just requests)
• GPU utilization: actual compute usage vs idle time
• cache hit rates: by request type and user segment
• model drift: output quality degradation over time
• cost per request: real-time cost tracking

the alerting system that saved us

we built alerts for:

• queue depth exceeding 100 requests (scale up trigger)
• response time 95th percentile above 5s (performance degradation)
• cost per request increasing 20% week-over-week (cost anomaly)
• cache hit rate dropping below 60% (cache effectiveness)

these alerts caught issues before they became customer-facing problems.

the architecture evolution

our architecture evolved through three distinct phases:

phase 1: monolithic (0-50k requests/day)

simple, but hit limits quickly. response times became unpredictable, and costs were linear with usage.

phase 2: microservices (50k-500k requests/day)

better, but still treating each request independently. we were missing optimization opportunities.

phase 3: intelligent orchestration (500k+ requests/day)

this is where the magic happened. requests are analyzed, batched, and routed intelligently.

the final results

after 18 months of optimization, here's what we achieved:

practical implementation roadmap

if you're scaling an AI system, here's the order i recommend:

weeks 1-2: measurement foundation

• implement comprehensive monitoring
• establish baseline performance metrics
• analyze request patterns and user behavior

weeks 3-4: quick wins

• implement basic response caching
• add request deduplication
• optimize instance sizes and types

weeks 5-8: intelligent optimization

• implement semantic caching
• build smart request routing
• add batch processing capabilities

weeks 9-12: advanced scaling

• implement multi-model serving
• add predictive auto-scaling
• optimize for geographic distribution

the bottom line

scaling AI systems is fundamentally different from scaling traditional web applications. the techniques that work for REST APIs don't necessarily work for AI workloads.

but here's the thing: once you get it right, AI systems can scale more efficiently than traditional systems. the key is understanding the unique characteristics of AI workloads and optimizing specifically for them.

start with measurement, focus on the biggest bottlenecks first, and don't be afraid to rethink fundamental assumptions about how systems should work.

when to call for help

honestly? scaling AI systems is hard. really hard. consider getting expert help if:

• your infrastructure costs are growing faster than your user base
• response times are becoming unpredictable
• you're spending more time on infrastructure than product features
• your team lacks experience with high-scale distributed systems

the cost of getting scaling wrong far exceeds the cost of getting expert help.

scaling AI systems in your organization?

i'd love to hear about your challenges and what techniques you've found effective. every system is different, and there's always more to learn from other practitioners.