I’m going to be blunt: the state of AI infrastructure heading into 2024 is embarrassing.
We have models that can write poetry, generate code, and analyze images. We don’t have enough GPUs to run them reliably. We don’t have pricing that makes sense at scale. And we definitely don’t have the operational maturity to treat these systems like the production dependencies they have become.
I’ve spent December watching AI features I helped build at a fintech company run into every scaling problem distributed systems teams have been solving for twenty years. Rate limits. Cascading failures. Cost explosions. Latency spikes. The problems aren’t new. The industry is just re-learning them with a fresh coat of hype.
The GPU Situation Is Absurd
You can’t get H100s. You can’t reliably get inference capacity from any major provider unless you sign a months-long commitment or an enterprise contract that costs more than most startups raise in a seed round. The entire industry is building products on top of infrastructure that’s supply-constrained, and nobody wants to talk about what happens when demand doubles next year.
I tried to reserve inference capacity for a production workload last month. The response from one provider was “we can put you on a waitlist.” A waitlist. For compute. In 2023. This isn’t a technology problem. It’s a supply chain problem wearing a technology costume.
Rate Limits Are a Production Constraint
Every AI API has rate limits. At low volume, you don’t notice them. At production scale, they become the hardest ceiling in your architecture.
I hit OpenAI’s rate limit during a load test and watched requests queue up until the entire feature became unusable. Not degraded – unusable. The fix wasn’t clever engineering. It was a priority queue, backpressure, and load shedding. Distributed systems 101. The fact that most AI teams are learning this for the first time worries me.
Your Demo Won’t Survive Real Traffic
Here is what happens when your AI feature goes from 100 requests per day to 10,000:
Latency goes from “acceptable” to “users are closing the tab.” Costs go from “rounding error” to “someone just Slacked asking why the API bill tripled.” A provider outage that used to affect a handful of test users now takes down a production feature that the sales team just promised to a client.
I’ve seen all three of these happen at the same company. In the same month.
What You Actually Need
Queues and backpressure. Treat your AI traffic as a managed stream, not an open pipe. Priority queues for critical requests. Backpressure when the system is saturated. Load shedding for low-priority work. This isn’t optional once you have real users.
Circuit breakers. Your model provider will have bad hours. Mine had a bad day last week. Circuit breakers stop a provider outage from cascading through your entire system. They’re boring. They’re essential. I’ve been building systems with circuit breakers since my telecom days. The pattern hasn’t changed. The dependency has.
Graceful degradation. When GPT-4 is down, what happens? If the answer is “the feature breaks,” you don’t have a production system. You have a demo with users. Fall back to cached responses. Fall back to a smaller, faster model. Fall back to a static message that says “this feature is temporarily unavailable.” Anything is better than a spinning loader.
Cost controls that are actually enforced. Per-tenant budgets. Per-feature budgets. Daily caps. If you don’t enforce them, you’ll get a surprise invoice that triggers an emergency meeting. I’ve seen a single prompt change – adding two paragraphs of context – increase monthly costs by 35%. Token pricing is deceptively simple until you multiply it by production volume.
Caching. Exact-match caching is trivial to implement and saves real money. Same question, same context, same answer – serve it from cache. Semantic caching is fancier and worth exploring, but start with the easy wins.
This Is Distributed Systems Work
None of this is novel. Queues, circuit breakers, graceful degradation, cost controls, caching – these are patterns from every distributed systems textbook ever written. The only thing that’s new is the dependency type.
What frustrates me is that the AI community is treating infrastructure as a solved problem while building on top of infrastructure that’s anything but solved. The models are impressive. The plumbing is held together with optimism and rate limit retries.
Build your AI features like you would build any production system that depends on an unreliable, expensive, supply-constrained external service. Because that’s exactly what it is.