Uptime isn’t just for websites anymore
Over the past two decades, teams have built a robust culture around keeping websites online. We obsess over page load times, HTTP status codes, and real-time alerts—because a minute of downtime can cost thousands in lost revenue and user trust. Uptime is non-negotiable.
But as AI-driven applications become a bigger part of production systems, a new form of reliability challenge is emerging: model uptime. These apps aren’t just pulling data from a database—they’re interacting with probabilistic systems that can time out, fail silently, or produce wildly unpredictable outputs.
And yet, most teams still approach AI as if it’s just another API call.
It’s not.
Just like websites need uptime monitoring and routing systems to stay resilient, AI systems need their own layer of observability and protection. And that’s where the lessons of traditional web infrastructure become surprisingly relevant.
What website monitoring got right
When it comes to web infrastructure, we’ve built strong muscle memory around reliability. We monitor ping responses, track server uptime, set up alerting for slow page loads or 500 errors, and use status pages to keep users informed. If traffic spikes, we add autoscaling or CDNs. If a server crashes, we add a failover. It’s all second nature now.
This didn’t happen overnight. Teams learned—sometimes the hard way—that without visibility and redundancy, things break. And when they do, users notice. The main lesson? You don’t just build a system and hope it works. You monitor, route, retry, and constantly optimize.
That approach has served us well in the world of websites and APIs.
But AI systems are now demanding the same level of operational maturity, just in a completely different shape.
Why AI systems fail differently
AI systems don’t fail like traditional web services. There’s no clear-cut “server down” moment. Instead, failures in AI apps are often fuzzy, hidden, and unpredictable.
A model might time out mid-response. It might hit a rate limit without throwing a clear error. It might return an irrelevant or hallucinated answer that sounds plausible but is completely wrong. Sometimes, a prompt tweak that worked yesterday starts failing today due to model updates or input variability.
And because these failures aren’t always binary, they’re harder to catch—and much harder to fix reactively.
Worse, the impact isn’t just technical. One model error can lead to broken user experiences, legal issues from misinformation, or runaway API costs from a bad loop. And without proper observability or fallback systems, teams are left blind to these issues until it’s too late.
That’s why AI systems require a new approach to reliability—one that understands their unique failure modes and builds in protection by design.
Enter the AI Gateway
Just like we built CDNs, load balancers, and observability tools to make websites more resilient, AI systems now need their own reliability layer — and that’s exactly what an AI Gateway provides.
Think of it as the router and control plane for all your model traffic. Instead of your app calling OpenAI, Anthropic, or Azure directly, every request goes through an AI Gateway. That gives you the power to monitor, route, and safeguard every interaction.
Here’s how it parallels traditional uptime tools:
- Error monitoring: Just as you’d track HTTP 5xx errors on a website, the AI Gateway logs failed prompts, latency spikes, and abnormal responses, with full traceability.
- Routing and fallbacks: When a model fails, times out, or hits a rate limit, the Gateway can automatically retry or route the request to a different provider.
- Smart traffic control: Just like a load balancer, you can distribute traffic across models based on cost, latency, or reliability — even test new models in production via shadow mode.
- Cost and quota protection: Set rate limits and budgets per team or use case, and throttle before usage spirals out of control.
- Visibility: Dashboards, logs, alerts — all in one place, so your AI stack isn’t a black box anymore.
Building reliable AI systems with the right infrastructure
AI is powering real user experiences — summarizing emails, answering questions, writing code, analyzing documents. To build trustworthy AI applications, you need more than just a good model. You need the infrastructure around it to make it reliable, observable, and cost-efficient.
That’s where Portkey comes in — a full-featured AI Gateway built for production-grade GenAI apps.
With Portkey, teams can:
- Monitor every LLM request across models and providers, with detailed logs and traces
- Automatically route traffic based on latency, cost, or reliability
- Set rate limits and budgets by team, use case, or key
- Define retries, fallbacks, and timeouts to prevent silent failures
- Manage and version prompts, and deploy updates safely
- Get real-time visibility into usage, errors, and performance
If you’re scaling AI usage across your org, an AI Gateway like Portkey is the foundation for staying in control.
AI systems need their own uptime layer
We’ve spent years perfecting how we monitor, scale, and protect web infrastructure. AI systems are heading down the same path but their failure modes are different, and their impact is often more unpredictable.
As GenAI moves into the core of user-facing products, teams need to treat reliability as a first-class concern. That means observability, routing, cost control, and protection from silent failures the same principles that make websites stable, now applied to AI.
And the easiest way to do that? Use an AI Gateway like Portkey to monitor, manage, and scale your model traffic with confidence.
