Build Scalable GenAI Infrastructure in 48 Hours

Getting a GenAI system to production in 48 hours sounds unbelievable. We get it. Most teams measure this process in months, sometimes even a full year. The delay isn't the model; it's the immense effort required to build the infrastructure around it. Integrating tools like vLLM and Langfuse, securing the stack with proper access controls, and implementing meaningful observability is a slow, painful process. This is the gap where great prototypes stall. We built Cake to bridge that gap. It’s not another framework that forces you to rewrite everything. It’s the composable layer that makes building scalable GenAI infrastructure fast and repeatable.

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

What happens when your genai prototype can'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.

The 48-hour transformation

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 building genai infrastructure usually takes 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

Handling the unique privacy challenges of GenAI

Generative AI introduces a new set of privacy concerns that go beyond typical access control. The real challenge is managing the data itself—ensuring that sensitive information isn't inadvertently used for training or exposed in model outputs. This gets complicated when developers are juggling multiple AI services, which can create security blind spots and make it difficult to enforce consistent data policies. Without a clear view of your data's lifecycle, you risk turning your powerful AI system into a potential liability, where private customer data could leak into public-facing responses.

A well-designed architecture is the key to managing this risk. Inspired by systems like Meta's Privacy Aware Infrastructure, the goal is to create a system that can track where data comes from, where it goes, and how it's used. This allows you to set up "policy zones" that act as guardrails, blocking sensitive data from moving into areas where it shouldn't be. Cake provides the secure, observable foundation needed to build these controls. By pre-integrating security components like a service mesh and RBAC, we give you the tools to enforce data governance without having to engineer a complex privacy framework from scratch.

The secret to future-proofing your genai infrastructure

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.

The best way to build for the future is to assume you can’t predict it. A rigid infrastructure designed for today’s models is a liability for tomorrow’s. Instead of locking yourself into a single pattern, the goal is to create a flexible foundation that can adapt as your needs change. This means designing a system where you can easily swap out models, add new data sources, or change routing logic without having to re-architect everything from scratch. A composable approach, where different components can be combined and reconfigured, is key. This allows your infrastructure to evolve alongside the fast-paced world of AI, ensuring you’re always using the best tools for the job.

Use a two-tier gateway for complexity management

One of the most effective ways to build in flexibility is with a two-tier gateway. Think of it as a smart traffic controller for your AI system. The first tier acts as the main entry point for all incoming requests. Its job is to decide where to send the request—either to an external, third-party AI service like one from Anthropic or Google, or to your own internal AI models. This separation is crucial because it lets you manage a hybrid environment cleanly. You can route simple queries to a cost-effective external model while sending sensitive or specialized tasks to your fine-tuned internal models, all without complicating your application logic.

When you're working with generative AI, privacy can't be an afterthought. The models are powerful, but they learn from the data you give them, making it critical to control what information they see. Bolting on privacy controls after the fact is often ineffective and leads to data leaks. The most robust approach is to build privacy directly into the fabric of your infrastructure. This means thinking about data governance from the very beginning, creating systems that automatically enforce your privacy rules. Instead of relying on developers to remember a long list of policies, you can create an environment where doing the right thing is the default, not the exception.

Map data flows with data lineage

You can't protect data if you don't know where it is or where it's going. This is where data lineage becomes essential. It’s like creating a detailed map that tracks every piece of data as it moves through your system—from its origin to its final destination and every transformation in between. According to Meta's engineering team, this visibility is the foundation of their privacy infrastructure. By understanding the complete journey of your data, you can pinpoint exactly where sensitive information is being used and ensure it’s being handled correctly, making it possible to innovate responsibly without putting user data at risk.

Automate privacy enforcement with policy zones

Once you have a clear map of your data flows, you can start automating enforcement. This is where "Policy Zones" come in. Think of these as protected digital areas for your most sensitive data. Using the insights from data lineage, you can define these zones and attach specific rules to them. For example, you could create a rule that prevents any data within a "PII Zone" from ever being sent to an external model or logged in plain text. This approach shifts privacy from a manual checklist to an automated, system-level guarantee, ensuring your policies are enforced consistently across the entire platform without slowing down development.

Your gateway shouldn't just be a simple traffic cop; it should be the brain of your AI operations. By centralizing key functions at the gateway level, you can solve a host of problems related to cost, security, and compliance in one place. This makes life significantly easier for your developers, as they no longer have to implement these controls in every single application. A smart gateway can provide a unified control plane for managing everything from API keys to spending limits, giving you a powerful lever to manage your entire AI ecosystem efficiently and securely. It becomes a strategic asset rather than just a piece of plumbing.

Manage costs with automated circuit breakers

Third-party model APIs are powerful but can lead to surprisingly high bills if left unchecked. An intelligent gateway can act as a financial safety net by implementing automated "circuit breakers." You can configure rules that set limits on token usage or the number of requests sent to an external provider over a certain period. If usage exceeds these predefined thresholds, the circuit breaker trips, temporarily halting requests and preventing runaway spending. This gives you predictable control over your costs without requiring someone to manually monitor billing dashboards around the clock.

Streamline security key management

Managing API keys for multiple AI providers is a common headache for development teams. It's not only tedious but also creates security risks if keys are misplaced or improperly stored. A sophisticated gateway can solve this by handling key management centrally. Instead of developers embedding keys in their code, the gateway can automatically inject the correct security key based on where a request is being routed. This abstracts away the complexity, reduces the risk of exposed credentials, and allows you to rotate keys or change providers without having to update and redeploy multiple applications. It’s a simple change that dramatically improves both security and developer productivity.

Enforce compliance with built-in guardrails

A gateway is the perfect place to enforce compliance policies consistently across all your AI applications. You can configure it to act as a central checkpoint, inspecting both requests and responses for potential issues. For example, you can implement guardrails that automatically scan for and redact personally identifiable information (PII) before it reaches a model, or check model outputs for toxicity or other harmful content. This approach, similar to Meta's philosophy of building privacy into its systems, ensures that your compliance rules are always enforced. It turns your gateway into an active defender of your data policies, providing a crucial layer of protection for your organization.

The open-source community has produced an incredible array of powerful tools for building GenAI applications. The challenge isn't a lack of options, but rather the complexity of making them all work together seamlessly in a production environment. Getting tools for inference, scaling, and monitoring to communicate effectively often requires a significant amount of custom "glue code" and configuration. This is where a platform approach makes a huge difference. At Cake, we provide a cohesive infrastructure layer where best-in-class open-source components are already pre-integrated, allowing you to focus on building your application, not wrestling with the underlying plumbing.

Deploy and scale models with KServe

Once you have a model ready, getting it deployed and making sure it can handle real-world traffic is the next major hurdle. This is where a tool like KServe is invaluable. Built on top of Kubernetes, KServe is designed to simplify the entire process of model serving. It handles many of the complex, behind-the-scenes tasks, such as automatically scaling resources up or down based on traffic, performing health checks, and supporting different types of models out of the box. By using KServe, your team can abstract away much of the operational complexity, enabling you to deploy and manage your models much more efficiently.

Monitor performance with OpenLLMetry

You can't improve what you can't measure. For any production-grade GenAI system, robust monitoring is non-negotiable. You need clear visibility into key metrics like latency, throughput, and time-to-first-token to understand how your system is performing and identify bottlenecks. OpenLLMetry is emerging as a standard for collecting this kind of data from LLM applications. Within Cake, we make it easy to monitor your entire stack by integrating tools like Prometheus for metrics, Grafana for dashboards, and Langfuse for tracing. This gives you a comprehensive, out-of-the-box observability solution to see exactly how your system is behaving in real time.

What's next for your genai stack?

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.

Frequently asked questions

How can Cake really get a GenAI system to production in just 48 hours? The secret is that we’ve already done the hard integration work for you. Instead of your team spending months writing custom code to connect tools for inference, security, and monitoring, we provide a foundation where they already work together perfectly. This lets you skip the tedious setup and plug your prototype directly into a production-grade system.

Is Cake another framework that will force me to rewrite my code? Not at all. We designed Cake specifically to avoid the pain of starting over. It integrates with the stack you already have, whether you're using vLLM, Langfuse, or other popular tools. Think of us as the support system that makes your current application scalable and secure, not a replacement that changes how you work.

What does it mean that Cake is a "composable infrastructure layer"? It’s a simple way of saying we provide a solid foundation, and you choose the building blocks. We offer a menu of pre-integrated, production-ready components for things like model serving, tracing, and security. You can pick and choose the ones you need to support your specific application, connecting them easily without having to build everything from scratch.

My team already uses open-source tools. Why would we need Cake? While you can certainly integrate these tools yourself, the process is often slow and full of unexpected challenges. Getting them to work together reliably under production loads with proper security can take months of specialized engineering effort. Cake provides that expertise out of the box, saving you that time and letting your team focus on building your AI features, not the plumbing.

How does Cake handle the security and privacy of our data? We build security directly into the infrastructure from the start. Cake comes with essential features like role-based access control (RBAC), a service mesh for secure communication, and integrations for your company's identity provider. This gives you the tools to enforce data governance policies from day one, helping you manage sensitive information responsibly without having to engineer a complex privacy framework yourself.

Key Takeaways

• Focus on the infrastructure, not just the model: The biggest hurdle in deploying GenAI isn't the model itself, but the complex infrastructure needed for security, observability, and scaling. Addressing this early is the key to avoiding months of delays.
• Integrate a composable layer to accelerate deployment: You don't need to start from scratch. Adding a pre-integrated infrastructure layer to your existing stack provides production-grade features, helping you go live in days without rewriting your code.
• Design your architecture for future changes: The AI world moves fast. Build a flexible foundation with adaptable patterns like a two-tier gateway and built-in privacy controls so you can easily swap models and evolve your strategy over time.

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