LLM Proxy: Secure & Scalable Access to Large Language Models

Imagine you’re sitting inside a fast-moving product team at a growing company. In a fast-moving company, customers keep asking for new features, leaders push for innovation, and finance keeps an eye on costs. Everyone’s under pressure, customers demand new features, leadership demands innovation, and finance demands cost discipline. Everyone is searching for the next big lever that will help them deliver faster and impress users without ballooning expenses.

AI feels like the perfect answer.

A developer wires the product into OpenAI, and suddenly the application can do things it never could before. Customers can ask natural questions instead of fumbling through menus. They can get instant summaries of long reports, or brainstorm creative ideas in seconds. Leadership sees a demo and immediately wants more: “Can we roll this out in customer support? What about sales enablement? Could HR use it for job descriptions?” The excitement spreads.

Soon enough, every team wants their own AI features. What began as a small experiment in one app quickly spreads  into ten apps, five teams, and hundreds of employees making calls to different models.

But at scale, things start breaking.

What worked beautifully in the pilot stage quickly turns into a mess at scale. The very thing that made AI easy to adopt direct access to provider APIs becomes a liability when adoption spreads. Teams run into problems that feel eerily familiar to anyone who has seen technology go from “cool demo” to “critical infrastructure.”

The Problems That Emerge

Let’s unpack what happens once AI use goes from small-scale experiments to company-wide adoption.

1. Bill Shock

A single API call is cheap—fractions of a cent. But thousands of calls, spread across different apps and teams, add up quickly. At the end of the month, finance suddenly gets a bill much higher than expected. No one can explain exactly where the money went. Was it the chatbot in the support portal? The content team running batch summaries? Or a developer accidentally leaving a loop in place that hammered the API all night?

The finance team is frustrated because they don’t just see “OpenAI $120,000.” They need to know: which department drove this cost? Was it aligned with business value? Without visibility, they can’t forecast or allocate budgets.

2. Security Risks

When adoption spreads informally, developers share API keys in Slack, hardcode them into scripts, or worse, commit them into public repositories. These are mistakes made under pressure: “I’ll just drop the key here for now so we can test quickly.” The risk compounds when employees leave the company—no one knows which keys to revoke, or if those keys are still being used externally.

It only takes one leaked key for outsiders to start running up your bill or worse, stealing sensitive data through your AI provider.

3. Switching Headaches

Leadership inevitably asks: “Can we try Anthropic’s model instead of OpenAI? Or maybe Gemini? I heard it’s better at summarization.”

In theory, trying a new model should be quick. In reality, every provider’s API is just different enough to cause headaches. Parameters are named differently. Authentication works differently. Response formats vary. Developers groan because switching means rewriting integration code, re-testing everything, and redeploying. What should be an afternoon experiment turns into weeks of work.

4. Finance Frustration

Even if the finance team accepts the giant invoice, they run into another problem: attribution. The accounting software only sees a single line item—“Anthropic $85,000.” They want to break that down by team, by product, even by individual feature. Without it, there’s no way to decide whether the spend was worthwhile.

Imagine running servers without tagging them by team or workload—you’d never do that in cloud infrastructure. But that’s exactly what happens with AI calls when there’s no central control.

5. Compliance Worries

In many industries, sending data to external services requires strict controls. Healthcare companies worry about patient data. Banks worry about financial records. Governments worry about citizen privacy.

Without centralized oversight, sensitive data may be leaving the company without anyone realizing it. Legal and compliance teams are left in the dark, and the organization risks regulatory penalties or reputational damage if something goes wrong.

6. Scaling Chaos

Finally, there’s the problem of scale. A single small bug can cause a request loop that sends thousands of queries overnight. Or a marketing campaign suddenly spikes traffic and floods the AI provider.

Costs skyrocket, customers see degraded performance, and no one realizes until the next morning. What was manageable in early experiments becomes chaos under real-world load.

Why Direct Use Works for Experiments, But Not at Scale

For small experiments, connecting directly to OpenAI or Anthropic makes perfect sense. It’s fast, lightweight, and empowering. But as adoption spreads, the lack of guardrails becomes dangerous.

It’s the same story we’ve seen across decades of technology adoption. Early in the cloud era, developers swiped credit cards to spin up AWS resources—and finance teams freaked out at the uncontrolled costs. In networking, teams built direct connections between services until the complexity became overwhelming, leading to the rise of API gateways and service meshes.

AI is no different. Once it becomes central to your product strategy, you need structure, visibility, and control.

The Missing Piece: An AI Gateway

At some point, every growing organization runs into the same painful realization: the way we’ve been doing AI was fine for experiments, but it won’t survive at scale.

Leadership sees the growing invoices. Security notices credentials scattered everywhere. Finance complains about the black box of costs. Developers get bogged down rewriting integrations. Compliance teams lose sleep over what data is leaving the organization. And eventually, someone asks the question that feels obvious in hindsight:

“Why don’t we have a system to manage all this?”

That system already exists. It’s called an AI Gateway—sometimes also called an LLM Proxy.

A Simple Metaphor: Traffic Without a Traffic Light

Imagine a busy city without traffic lights. At first, when there are just a handful of cars, everyone manages. Drivers wave each other through, stop politely, and it works. But as more cars show up, chaos begins. Intersections jam, accidents spike, and everyone wastes time honking instead of moving forward.

Now add traffic lights. Suddenly, the chaos becomes manageable. Cars move in order. Pedestrians cross safely. Emergency vehicles get priority. The system feels smoother, fairer, and safer.

An AI Gateway is the traffic light system for AI adoption. It doesn’t take away freedom—it organizes it so everyone can move faster without crashing into each other.

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Here’s a simple way to visualize it:

What an AI Gateway Actually Does

An AI Gateway sits between your apps and the AI providers. Instead of every app talking directly to OpenAI, Anthropic, or Gemini, all requests go through the gateway first.That gateway:

• Standardizes requests so developers don’t have to rewrite code for every new provider.
• Secures credentials so keys aren’t floating around in Slack or code repos.
• Enforces rules like budgets, rate limits, or content policies.
• Tracks usage and costs so finance and product leaders have visibility.
• Adds reliability with load balancing and failover when providers are down.

In other words, it doesn’t just forward traffic—it manages it intelligently.

The Office Manager Analogy

Another way to picture it: imagine running a company without an office manager.

• Every employee buys their own software licenses with company credit cards.
• Every team negotiates separate contracts with vendors.
• Nobody knows who has access to what.
• At the end of the month, the CFO just sees a pile of random receipts.

It might work when you have five employees. But when you have five hundred, it’s chaos. An office manager brings order—centralizing purchases, managing access, and providing transparency.

The AI Gateway is the office manager for your AI usage. It doesn’t stop teams from innovating—it ensures they innovate responsibly, with visibility and accountability.

Why a Gateway Isn’t Just for Enterprises

A common misconception is that only giant enterprises need a gateway. But history shows that the need for governance always arrives earlier than expected.

• Cloud infrastructure: Startups adopted AWS with no governance, then hit $100k bills before hiring FinOps.
• APIs: Small dev teams hardwired integrations until the complexity broke production, then added API gateways.
• SaaS tools: Even 50-person startups realized they needed centralized IT when employees were expensing dozens of overlapping tools.

AI is no different. Today, even a 20-person startup can rack up $20,000 in LLM bills in a single month if usage isn’t tracked. A runaway script doesn’t care how big your company is.

That’s why AI gateways are not just an “enterprise luxury”—they’re a survival tool for any company serious about scaling AI.

Why TrueFoundry’s AI Gateway Stands Out

There are several gateways on the market, but TrueFoundry takes a uniquely enterprise-focused approach. It doesn’t just solve the basic “proxy” problem. Instead, it’s designed for scale, governance, observability, and security—the things that matter most when AI is being used across teams and products.

Let’s break down how TrueFoundry fixes the challenges we described earlier.

(a) Unified API Across Providers

• A single OpenAI-compatible API works with OpenAI, Anthropic, Bedrock, Gemini, Cohere, AI21, Mistral, and many more.
• No code changes required when switching providers.
• This is a huge improvement for teams. Without a gateway, every new model requires engineering work. With TrueFoundry, switching is seamless. Want to test Gemini for summarization while keeping OpenAI for chat? Just change configuration, not code.

Result: Easy multi-model adoption without lock-in.

Where TrueFoundry’s LLM Gateway Earns Its Stripes

Now, onto the good stuff: how TrueFoundry’s LLM Gateway solves real-world problems—and what the numbers say.

(a) Unified API Across Providers

Instead of multiple provider-specific SDKs, TrueFoundry gives you one OpenAI-compatible API that works across OpenAI, Anthropic, Gemini, Mistral, Cohere, AI21, Bedrock, DeepInfra, Ollama, Perplexity AI, Nomic, and more TrueFoundry+3TrueFoundry+3TrueFoundry+3. Switch providers or mix them—no code changes required.

(b) Rock-Solid Speed & Scalability

Speed matters. Benchmarks show:

• Compared to direct OpenAI calls (~73 ms latency), calling through TrueFoundry adds only 3–4 ms — staying stable even at 250 RPS, and only ~4 ms overhead above 300 RPS TrueFoundryTrueFoundry+2TrueFoundry+2.
• TrueFoundry consistently scales up to ~350 RPS on just 1 vCPU / 1 GB RAM before CPU usage hits 100% TrueFoundry Blog+6TrueFoundry+6TrueFoundry+6.
• For serious scale, a t2.2xlarge AWS spot instance (~$43/month) can handle ~3000 RPS with no performance degradation TrueFoundry+1.

By comparison, LiteLLM starts lagging—88–99 ms latency at just 50 RPS, and can't scale beyond that TrueFoundry+6TrueFoundry+6TrueFoundry+6.

That means near-zero latency impact, and performance that scales gracefully with modest infrastructure.

Result: Consistent, near-zero overhead performance at scale.

(c) Security & Governance

Security is often the biggest blind spot in early AI adoption. TrueFoundry addresses it directly:

• Centralized API key management prevents credential sprawl. No more secrets in Slack, code, or shared docs.
• Access controls, limits, and content filtering allow admins to enforce policies across teams.
• Audit trails log every interaction for compliance and review.

This not only reduces risk but also gives security and compliance teams confidence that AI usage is under control.

Result: Security and compliance are built-in, not bolted on later.

(d) Observability & Cost Control

AI usage without observability is like running servers without monitoring—you only notice issues when things go wrong. TrueFoundry provides:

• Real-time tracking of usage, costs, latency, and performance.
• Budget alerts to prevent overspending before it happens.
• Audit logs so every request can be traced back to a source.

This allows product leaders to see exactly which features drive costs, finance to allocate budgets correctly, and developers to debug performance problems.

Result: No more blind spots—every request is visible, measurable, and attributable.

(e) Reliability & Load Balancing

Enterprise systems can’t afford downtime or unpredictable performance. TrueFoundry ensures stability through:

• Rate limiting to prevent runaway loops or abuse.
• Load balancing to automatically distribute requests across providers.

That means even if one provider experiences an outage, traffic can be redirected, and apps continue working smoothly.

Result: Stable performance even under unpredictable demand.

Putting It All Together

Let’s revisit the original problems of our growing product team—and how TrueFoundry addresses each:

A Minimal Architecture View

Here’s how it looks when TrueFoundry is in place:

It’s simple, clean, and scalable—without overwhelming technical detail. Importantly, this diagram reflects a reality many teams face: multiple apps, multiple providers, one central control point.

Why This Matters

Without a gateway, organizations risk:

• High costs that shoot up without clarity
• Security vulnerabilities from unmanaged keys and uncontrolled usage.
• No visibility into what models are used, where data is going, or who is spending money.

TrueFoundry provides:

• Developers → Unified API that works across providers.
• Security teams → Centralized controls and audit logs for compliance.
• Finance teams → Real-time cost insights and budget protection.

In short, it transforms LLM usage from chaotic to secure, cost-effective, and scalable.

Closing Thoughts

The AI wave is no longer experimental—it’s central to product strategy. But without controls, what begins as innovation can quickly turn into liability.

TrueFoundry’s LLM Gateway provides that missing layer of infrastructure. By combining:

• Near-zero latency overhead
• Unified API across providers
• Enterprise-grade governance and observability

If your company is already juggling multiple AI providers, or if finance is worried about mounting costs, the time to act is now. Put a gateway in place before the problems grow. Chances are, a year from now, you’ll wonder how you ever managed without it.