I’m tired of seeing AI infrastructure treated as if it needs a whole new discipline.
It doesn’t. It’s the same infrastructure engineering we’ve been doing for decades, applied to a workload that happens to involve model inference. The latency problems are the same. The cost problems are the same. The reliability problems are the same. And the solutions are the same.
And yet every week I review a team’s architecture and find they’ve reinvented service meshes, badly, because they assumed AI needed something different.
The Demo-to-Production Gap Is Infrastructure
Here’s what happens: a team builds a demo. It works great at one request per minute. Then real traffic arrives and everything falls apart. Latency spikes. Costs explode. The system goes down when the provider rate-limits them.
None of these are AI problems. They’re infrastructure problems that we solved years ago in every other context. The teams that scale AI successfully are the ones that apply those solutions without reinventing them.
Put a Gateway in Front. Please.
I’m genuinely baffled by how many production AI systems I see where every service calls the model provider directly. No centralized routing. No rate limiting. No budget enforcement. No observability.
This is like building a web application in 2024 without a load balancer. Nobody would do that. But somehow AI gets a pass.
A gateway – call it whatever you want, broker, proxy, control plane – does the boring work:
- Routes requests to the right model based on task type
- Enforces rate limits and budgets per user, per feature, per environment
- Caches deterministic responses
- Provides a single point for observability and tracing
- Handles provider failover when one API goes down
You can build a basic version in a day: a YAML config and a reverse proxy. It doesn’t need to be fancy. It needs to exist.
Separate Your Workloads
Interactive requests and batch processing shouldn’t share the same execution path. I keep saying this, and teams keep ignoring it until interactive latency tanks because a batch job saturated the rate limit.
Interactive work gets tight latency budgets and priority access. Batch work gets queued and retried patiently. The split is trivial to implement and painful to retrofit after the fact.
Cache. Everything. Deterministic.
If you’re sending the same prompt with the same inputs to the same model and not caching the response, you’re burning money. Literally.
Exact-match caching for deterministic requests is table stakes. Similarity-based caching for near-duplicate requests is a bonus. Even a simple TTL-based cache with invalidation on prompt updates can cut costs significantly.
One team was spending $40k/month on model inference. After adding exact-match caching for their classification pipeline, it dropped to $15k. Same outputs. Same quality. Less waste.
Cost Controls Aren’t Optional
“We’ll optimize costs later” is the AI equivalent of “we’ll add tests later.” You won’t. And when the bill arrives, it becomes an emergency.
Budget enforcement belongs in the gateway. Hard caps with clear error messages. Soft limits that degrade to cheaper models or slower paths. Per-user and per-feature attribution so you know where the money goes.
I’ve seen teams discover that a single feature was responsible for 70% of their AI spend because nobody was tracking attribution. The feature wasn’t even high-value. It was just chatty.
Reliability Isn’t Heroics
Retry with backoff. Circuit breakers. Graceful degradation. Provider failover.
These aren’t advanced patterns. They’re baseline production engineering. If your AI system doesn’t have them, it isn’t production-ready. It’s a demo with a billing account.
Graceful degradation is a product decision, not an ops feature. If the full response is unavailable, a simpler response or a cached response or even a “try again in a moment” is better than an error page. Design for this upfront. Don’t bolt it on during an incident.
The Unsexy Truth
AI infrastructure at scale is boring. That’s the point. Boring means predictable. Predictable means reliable. Reliable means you can actually build products on top of it.
The gateway, the cache, budget enforcement, workload separation, circuit breakers: none of it is novel. All of it is necessary. The teams that treat AI infrastructure like regular infrastructure, applying patterns that already exist, are the ones that scale without drama.
Stop reinventing. Start reusing. Your SRE team already knows how to do this. Let them.