Why Does OpenAI Keep Going Down?

Imagine you’re in the middle of a debugging session with important business implications. You open ChatGPT, expecting quick, intelligent responses to keep your work moving.

Instead, you see an error:

Frustrating? Sure.

But it's also a reminder that infrastructure underpins everything.

Over the past year, ChatGPT has experienced a number of major outages. In March 2025, the rollout of image generation drove usage to new highs, pushing infrastructure past its limits (and Sam Altman's tweets along with it).

But this isn't a failure story.

It's a scale story — and an unprecedented one.

What we're seeing now is OpenAI's Fail Whale moment, similar to when Twitter would crash under its own popularity. When infrastructure can’t keep up with user excitement, it’s kind of a good problem to have. But it also means that engineers have to roll up their sleeves and think fast.

While OpenAI's growing pains are visible, they offer critical insight into what it takes to operate GenAI systems reliably — and what the rest of us can learn from that journey.

Today, we’ll explore:

• OpenAI’s struggles and System Design bottlenecks

• What OpenAI has tried so far (and where those efforts are leading)

• Critical changes OpenAI may need to adopt to prevent future failures.

Let's get started.

OpenAI is scaling in real-time (and it shows)

ChatGPT has gone down repeatedly, with March 2025 marking the third major outage this year alone. While OpenAI hasn’t publicly disclosed all the specifics of its outages, we can look at the timeline of significant global outages below:¹

When people talk about "scale problems," this is what they mean.

What OpenAI is doing is unprecedented. No one has ever deployed GPU-bound, session-aware AI systems at this scale, in real time, for a global audience.

Unlike legacy tech giants that built their infrastructure over decades, OpenAI has had to scale two fronts simultaneously, from scratch:

1. Building state-of-the-art AI, and

2. Operating global systems capable of delivering that AI reliably

These scaling pains are to be expected when you turn an R&D lab into a global utility. All things considered though, OpenAI is doing a fantastic job.

OpenAI’s System Design bottlenecks

Let’s dig deeper into the bottlenecks that are plaguing OpenAI's infrastructure.

Load balancing issues

OpenAI’s request routing appears to still have central chokepoints — some servers overload, others idle. Even small imbalances at this scale could create major slowdowns, especially when models are GPU-bound and every millisecond counts.

Unlike what’s publicly known about Google or Meta, which use global load balancers with real-time health checks and latency routing, OpenAI’s routing logic may still be catching up. To sustain performance at global scale, OpenAI may need to further invest in regional routing optimizations, per-request telemetry, and edge-aware scheduling.

Caching and GenAI complexities

Caching works great for static content.

But for GenAI? It’s basically rocket science. Everything’s user-specific. Everything’s dynamic. And if you cache too aggressively, you risk surfacing stale, irrelevant, or even incorrect results.

OpenAI seems to be slowly improving this, but prompt-level caching, inference reuse, and model output deduplication across similar requests are likely still in the R&D phase.

High inference costs and GPU constraints

LLMs require real-time inference, meaning a GPU is needed for every user interaction. Unlike traditional web apps that scale horizontally with CPUs and caching, GenAI workloads are expensive, interactive, and tightly bound to specialized hardware.

OpenAI isn’t just running models. They’re renting the world’s most in-demand hardware on Azure, at massive scale.

Yes, model compression helps (quantization, distillation), but the bigger gains may come from architectural shifts — possibly smaller models running at the edge, or hierarchical inference pipelines that offload simpler tasks to lighter-weight systems.

Until then, OpenAI’s ability to scale may still be limited by GPU availability and how efficiently they can utilize each GPU cycle.

Scaling AI models at this level is ultimately a game of computational power and optimization per token.

Microservices vs. monolithic architecture

OpenAI has started breaking down monoliths by containerizing workloads, separating storage, and building around services like conversation-state and chat-inference. But deep dependencies between components likely still exist.

There are signs of microservices integration with projects like RAVEN, a natural language cognitive architecture, though legacy systems and infrastructure complexity may continue to slow full adoption.²

Unlike companies that started as microservice-native (think Amazon), OpenAI is essentially rewiring the plane while flying it: migrating to Kubernetes, implementing canary deploys, and gradually building observability layers.

And they’re basically doing it on live television. Respect.

Stateful vs. stateless design

ChatGPT isn’t stateless. It remembers context, which means every request is tied to conversation history — and that history needs to be fast, available, and consistent across regions.

This makes regional failover more complex. If one region goes down, rerouting users would require migrating session state in real time or risking broken conversations.

Companies like Amazon handle this with session-aware load balancing. OpenAI may follow a similar path — potentially introducing globally distributed conversation caches and smarter context handoff protocols.

What OpenAI has done so far

While OpenAI doesn’t share every detail of its infrastructure, there’s a lot we do know. Even more, we can reasonably infer from documentation and observed behavior.

Let’s take a look at what they’ve done to address their infra challenges, and where they’re headed.

Infrastructure scaling via Azure and multi-region deployment

OpenAI has reportedly increased GPU and TPU capacity to meet growing demand, leveraging Microsoft Azure’s cloud infrastructure.³ The company has expanded beyond a single-region setup, likely deploying services across multiple data centers and regions to reduce congestion and enhance availability.

This multi-region approach helps improve service resilience and fault tolerance.

Where this is headed:

This kind of scaling is foundational, but regional failover alone isn’t the endgame. We’ll likely see more advanced orchestration: think smart traffic steering, state-aware routing, and cross-region session replication. The hard part now isn’t more regions — it’s making them act like one.

Load balancing and request handling improvements

OpenAI has worked to refine load balancing and request handling to reduce bottlenecks. ⁴

Key improvements may include:

• Query prioritization

• Batching

• Better queue management

• Stabilizing performance during traffic spikes

After incidents like the March 2023 data leak, OpenAI strengthened security around third-party dependencies.⁵

Where this is headed:

What’s next is dynamic, global load balancing. We’re talking latency-aware, health-sensitive, cost-optimized routing that adjusts in real-time. Today, OpenAI still deals with hot zones and uneven load. Tomorrow? A system that routes around failure before you even feel it.

Caching and proxy optimization at the edge

These efforts seem to have improved performance and alleviated some computational load: ⁶

• Optimized caching for static elements like authentication details and frequently accessed model outputs.

• Proxy servers at the edge route requests more efficiently, helping to reduce strain on core AI models.

Where this is headed:

The next evolution is dynamic, session-aware caching, which is far complex than static file caching. Think prompt-level caching, partial response reuse, and possibly even edge inference using smaller model variants. Google Coral and Meta have made moves in this space. OpenAI may not be far behind.

Migration toward microservices and containerization

OpenAI is using a microservices approach to enhance scalability and fault isolation.

• By decoupling components like chat interfaces, conversation storage, and model inference, OpenAI can scale services independently and contain failures more effectively.

• This shift is supported by containerization and orchestration tools, such as Kubernetes.⁷

Where this is headed:

Resilience, fault isolation, and fast deploys without side effects. They're not fully there yet, but the trajectory is clear. The deeper they go into microservices, the more resilient their platform becomes.

Redundancy improvements, failover mechanisms, and enhanced monitoring

In response to significant outages in late 2024, OpenAI invested in redundancy and failover mechanisms, distributing critical services across multiple data centers.⁸

• Rate-limiting is used to ensure priority access for enterprise users during high-traffic periods.

• OpenAI is believed to have enhanced its monitoring and observability with advanced logging and automated alerting, likely aiming to improve reliability further.

• A dedicated Site Reliability Engineering (SRE) team continuously monitors system health with fail-safes to prevent cascading failures and ensure smooth service during outages.

Where this is headed:

Right now, most of these systems are reactive. The next level is proactive. Predictive autoscaling. Feedback-driven capacity planning. Automated rollback with context-aware triage. The goal? Catch the fire before it hits production — or at least contain it like a champ.

Where OpenAI goes from here (and what it means for you)

OpenAI is doing something no one has done before: operating world-scale, real-time generative AI systems with millions of concurrent users, under constant and unpredictable demand.

And while the pressure is immense, the progress has been significant. OpenAI is learning in public, adapting in real time, and pushing the boundaries of both AI and System Design.

That makes OpenAI’s experience deeply relevant to developers today.

Whether you’re building your own generative AI stack or relying on OpenAI’s API, these same architectural challenges will find you. Reliability, performance, caching, observability, traffic routing — these aren’t just infra concerns. They’re product and UX concerns.

And as demand for real-time AI continues to surge, understanding how to build systems that scale under pressure will become one of the most valuable skill sets in tech.

Up Next: Your 5-Part AI Infrastructure Blueprint

This newsletter is Part 1 in a 2-part installment.

👉 Read Part 2: The AI Infrastructure Blueprint: 5 Rules to Stay Online.

You'll learn:

• 5 core rules that keep AI systems online at companies like Google, Meta, and Amazon despite unpredictable load and GPU bottlenecks.

• How to apply those lessons to your own AI stack, no matter your team size or budget.
  

This blueprint is your AI infrastructure playbook. It's not just relevant to LLM builders — it’s essential reading for anyone trying to ship reliable AI features.

👉 Check it out: The AI Infrastructure Blueprint: 5 Rules to Stay Online.

Happy learning!