Solving the LLM Infrastructure Bottleneck: Enabling Scale

The promise was simple: throw more AI at your business problems and watch productivity soar. The reality? Your infrastructure is on fire, your cloud bill looks like a phone number, and half your agents are timing out while the other half sit idle.

Welcome to the hidden challenge of production LLM deployments at scale.

The big problem nobody talks about

When one of our customers recently needed to migrate millions of assets using AI agents—each requiring 10+ inference calls—they weren't just facing a technical challenge. They were staring down a fundamental infrastructure paradox: how do you maximize throughput on dynamically throttled LLM providers without either leaving capacity on the table or triggering cascading failures? Or without having to buy dedicated inference for peak demand. 

Here's what makes this particularly challenging with modern LLM providers like Bedrock and Vertex AI: you don't actually know your capacity limits. These platforms use dynamic quota systems that allocate resources based on:

• Current regional demand
• Other customers' usage patterns
• Your account's history and reputation
• Time of day and overall system load

You're essentially flying blind. The capacity available to you at 2 PM might be 10x what's available at 3 PM, with no warning.

Enter: the air traffic controller for agents

Vertesia's platform reimagines how AI agents interact with LLM infrastructure through an air traffic control pattern. Instead of agents blindly attempting to make LLM calls and hoping for the best, they request clearance first. If the runway is clear, they proceed. If not, they wait efficiently until capacity becomes available.

The agent architecture pattern

In Vertesia, every AI agent is built as a durable, long-running workflow using Temporal under the hood. This isn't just an implementation detail—it's a fundamental architectural decision that changes everything about how we handle scale:

The traditional approach:

• Agent makes LLM call
• Hits rate limit
• Retries with exponential backoff
• Wastes compute spinning or crashes

Vertesia's approach:

• Agent requests a ticket from our rate limiter
• If capacity is available: proceeds immediately
• If not: the entire workflow suspends (sleeps)
• When capacity opens up: workflow wakes and continues
• Zero wasted compute, perfect state consistency

This suspension isn't busy-waiting or consuming resources. The workflow literally pauses execution, freeing up all resources until it can actually do useful work. Whether it sleeps for 10 milliseconds or 10 minutes, when it wakes up, it continues exactly where it left off with all context intact.

The intelligence layer: learning to surf unknown waves

Our rate limiting system doesn't just manage queues—it continuously learns and adapts to the actual available capacity, which is constantly changing and invisible to us.

Dynamic capacity discovery

The system uses an adaptive algorithm that:

1. Probes for more capacity when things are running smoothly
2. Backs off intelligently when hitting limits
3. Remembers successful capacity levels for fast recovery
4. Isolates failures to prevent cascade effects

Think of it like a surfer who can't see the waves but can feel them. We don't know if Bedrock will give us 10 requests per minute or 10,000—we discover it in real-time and adapt instantly.

Circuit breaker pattern with fast resume

When things go wrong (consecutive failures indicating a real capacity crunch), our circuit breaker opens to prevent system meltdown. But unlike traditional circuit breakers that reset to zero, ours remembers the last healthy capacity level and resumes at 85% of that when conditions improve. This means recovery in seconds, not minutes.

Per-model, per-environment intelligence

Each combination of model and environment maintains its own capacity model:

• Your GPT-4 capacity doesn't affect Claude Opus throughput
• Production learns independently from staging
• Different regions adapt to their own capacity constraints

This isolation ensures that capacity issues in one area don't cascade to others, and each model can run at its optimal throughput.

Real-world results that matter

Circling back to that same customer migration, in production, this architecture achieved: 

• 2,000+ requests/minute sustained throughput for a single customer environment - but fluctuating from 500 to 2,000+
• Millions of images processed with 10+ inference calls each
• Zero changes required to input systems—they could send requests as fast as they wanted
• 85% infrastructure utilization despite not knowing actual capacity limits
• Error rate dropped from 12% to 0.3% while tripling throughput

The migration that was projected to take weeks was completed in days. That’s the promise of AI realized.

Why this architecture matters for enterprise AI

The shift to dynamic quota systems by major LLM providers has fundamentally changed the infrastructure game. You can no longer:

• Plan capacity based on fixed limits
• Rely on simple rate limiters
• Assume yesterday's throughput will work today

Our air traffic control pattern solves this by treating capacity as something to be discovered, not configured. Your agents become self-regulating, automatically adjusting to whatever capacity is actually available.

The business impact

For engineering leaders, this means:

• Predictable performance despite unpredictable infrastructure
• Maximum utilization of whatever capacity you're given
• Reduced operational overhead—no manual tuning required
• Cost optimization—you use every bit of capacity you're paying for

For the business, it means:

• Faster time to value—migrations and batch processes complete sooner
• Better reliability—fewer failures and retries
• Scale without surprises—handle millions of requests without infrastructure panic

The Vertesia difference

While other platforms focus on high-level orchestration or simple routing, we've built infrastructure specifically designed for the reality of modern LLM deployments:

• Traditional Orchestrators assume you know your capacity and can configure accordingly. They break when limits change dynamically.
• Simple Rate Limiters apply fixed limits, leaving capacity on the table when more is available and failing when less is available.
• Multi-Provider Routers work around limits by switching providers, but don't solve the fundamental problem of maximizing each provider's dynamic capacity.

Vertesia's platform combines durable workflow execution with intelligent rate limiting to create self-optimizing infrastructure. Your agents sleep when they need to, run when they can, and always make forward progress—regardless of what the LLM providers decide to give you today.

Looking forward: infrastructure that adapts

The future of enterprise AI isn't just about better models or smarter prompts. It's about infrastructure that can adapt to the reality of shared, dynamic resources. As LLM providers continue to move toward dynamic allocation models, the ability to surf these invisible waves of capacity becomes critical.

With Vertesia, you stop worrying about rate limits and start focusing on building remarkable AI applications. We can ensure your agents will find a way to run efficiently, no matter what capacity constraints they face.