How to Build Scalable GenAI Infrastructure in 48 Hours (Yes, Hours)

Learn how Cake helped a team scale GenAI infrastructure in just 48 hours. Secure, observable, and composable, no glue code or rewrites required.

People are often surprised (and sometimes, understandably, a little skeptical) when they hear that with Cake, you can scale a secure, observable GenAI system to production in as little as 48 hours when traditionally this process is measured in weeks.

What’s better? You can do it sans rewrites, without vendor lock-in, and with zero glue code. Just production-grade infrastructure that works with the stack you already have.

This kind of speed is unheard of in enterprise AI. Most systems take months (sometimes a full year) to become production-ready. Not because the models are slow, but because the infrastructure is:

• Hard to integrate: Tools like vLLM, Langfuse, and Ray Serve weren’t built to work together out of the box.
  
  
• Difficult to secure: Setting up RBAC, ingress policies, and audit logs across services takes real effort.
  
  
• Missing observability: You can’t fix what you can’t see, and tracing across orchestration layers is notoriously painful.
  
  
• Slow to scale: Running inference locally might work in dev, but not when traffic spikes or routing gets complex.

Cake simplifies all of this. It’s a composable infrastructure layer built to help AI teams move from prototype to production fast without ditching the tools they love.

Skeptical? Fair enough. Let me show you how one team did it.

BLOG: Deploy AI models to production nearly 4X faster with Cake

A working prototype, but a stack that couldn’t scale

Recently, a team came to us with a familiar setup: they were self-hosting vLLM for inference, had some basic orchestration in place, and were preparing for a broader rollout. On paper, they were in good shape: no off-the-shelf platforms, no black-box dependencies, and full control over their models and serving logic.

But as usage grew, so did the friction:

• Inference bottlenecks under load
  
  
• No structured observability or latency insight
  
  
• Internal auth and security requirements
  
  
• Disconnected model routing logic

They weren’t looking for a new framework—they had one that worked well enough. What they needed was infrastructure that could support it: secure, observable, and resilient under real-world conditions.

Instead of forcing them to adopt a new stack, Cake integrated directly with what they already had. Here’s what we stood up:

We didn’t replace their tools or rewrite their pipelines; we turned their existing stack into a production-grade system with security, observability, and scalability built in. And we did it in less than 48 hours.

Results:

Live in production in <48 hours

 No major rewrites

$1M+ in engineering time saved

The underlying logic didn’t change. The stack just grew up—fast.

Why does this usually take months?

Most GenAI teams start fast. But once you move past the prototype phase, the gap between demo and deployment gets real.

Here’s what usually happens:

Individually, these are solid choices. But together, they require deep integration work—the kind that doesn’t scale, isn’t secure, and isn’t easy to maintain.

Cake isn’t another framework. It’s the glue infrastructure layer that makes open and proprietary components work together under production constraints.

Here’s what we solve:

You keep your models, your prompts, and your preferred stack. Cake provides the missing infrastructure to make them run reliably at scale.

What makes Cake different is how much of the hard work it handles for you:

• Everything is pre-integrated. Tools like Langfuse, Prometheus, Istio, and LiteLLM come wired together with production-ready defaults—no glue code or ad-hoc adapters required.
  
  
• Built for orchestration. Whether you’re scaling a single model or routing across multiple providers, Cake gives you runtime controls that make it easy to adapt your serving strategy over time.
  
  
• Production blueprints included. Need secure tracing across agentic workflows? Fine-grained RBAC for internal teams? Autoscaling with latency targets? Cake has pre-built configurations that get you there in hours—not weeks.
  
  
• Runs in your cloud. No vendor lock-in, no forced rewrites. Cake deploys as Kubernetes-native infrastructure in your environment, so you maintain full control.

This is what we mean by glue infrastructure: not another abstraction layer, but a composable foundation that connects the tools you already use and makes them work under real-world constraints—securely, observably, and fast.

IN DEPTH: Cake achieves SOC 2 Type 2 with HIPAA/HITECH certification

Why composability is your only safe bet

Most infrastructure bets are fragile. They assume you’ll stick with a single LLM provider, a single vector store, or a single orchestration pattern.

But the GenAI landscape moves fast. And if your stack can’t adapt, you’ll fall behind.

With Cake, you can:

• Swap OpenAI for Mistral (or anything else)
• Shift from basic RAG to agentic workflows
• Run side-by-side provider comparisons
• Trace impact of every change on latency, quality, and cost

And you can do all of it without rewriting your pipelines or hacking together one-off observability for each experiment.

Final thoughts

This isn’t about GenAI teams failing. It’s about how even great prototypes hit a wall without the right infrastructure.

Cake is what we wished we had: a secure, observable, and composable foundation that adapts as fast as the ecosystem evolves.

If you’ve already built something worth scaling, we can help you get it there in just 48 hours.