When I started building an AI gateway, I thought the hard part would be integrating multiple LLM providers.
It wasn't.
Calling an LLM API is relatively straightforward.
The real challenges begin once that gateway starts serving real traffic.
Over the past few months while building NIRIA, I've realized that production AI systems are fundamentally infrastructure problems rather than model problems.
Here are a few lessons that completely changed how I think about AI systems.
- Reliability is more important than another model
Adding another provider is easy.
Making requests reliable across multiple providers is not.
Production systems need to answer questions like:
What happens when a provider is slow?
What happens when a provider is unavailable?
Should requests automatically fail over?
How do you prevent cascading failures?
This is where concepts like circuit breakers, retries, and health checks become essential.
- AI costs are an infrastructure problem
When you're experimenting, token costs don't seem important.
At production scale, they're impossible to ignore.
A system should help answer questions such as:
Which model is the cheapest acceptable option?
Is a larger model actually improving outcomes?
Are we sending expensive requests that don't need to be expensive?
Without visibility, optimization becomes guesswork.
- Observability is critical
When an API request fails, you need more than an error message.
You need to know:
Which provider handled the request?
How long did it take?
How many tokens were used?
What was the latency?
Why was a routing decision made?
The more AI systems grow, the more important observability becomes.
- Failure is normal
One mindset shift surprised me.
Production systems shouldn't assume everything works.
They should assume something is always failing.
Designing for failure changes everything:
retries
timeouts
rate limiting
queues
graceful degradation
Reliability isn't about avoiding failures.
It's about handling them well.
- Building infrastructure changed how I think about software
Before this project, I focused mostly on writing features.
Today I spend much more time thinking about:
trade-offs
scalability
monitoring
system behavior
operational complexity
Building infrastructure forces you to think differently.
Final thoughts
I'm still learning every day.
Building NIRIA has introduced me to distributed systems, reliability engineering, observability, caching, routing, and production architecture.
This article isn't a guide to building the perfect AI gateway.
It's simply a collection of lessons I've learned while building one.
I'd love to hear:
What's one lesson you've learned while deploying AI into production?
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