LLM Integration Patterns for Enterprise

I reviewed three failed LLM pilots last month. None failed because GPT-4 wasn't smart enough. They failed because the architecture treated a probabilistic model like a deterministic database.

That mistake shows up early and quietly. A simple API call here. A prompt template there. A successful demo in front of leadership.

Then governance arrives. Then audit. Then cost reports. Then production traffic.

And that's when the system collapses — not because the model failed, but because the enterprise assumptions finally caught up with it.

After building and dismantling multiple LLM integrations across regulated industries, public sector programs, and large engineering organizations, one pattern keeps repeating:

LLMs do not integrate into enterprises like APIs.
They integrate like critical infrastructure.

Direct API Integration: Fast Success, Faster Failure

Almost every team starts the same way. The application calls an LLM endpoint directly. Secrets live in configuration. Logs are minimal. Everything feels lightweight.

It works. Right up until the first security review.

Direct integration fails the moment you need centralized identity, cost attribution, or request traceability. Once you need to explain who sent what data, from where, under which policy, you're already too late.

The Azure documentation doesn't explicitly warn you about this, but direct calls create operational risk faster than they create value.

The LLM Gateway Pattern Is Where Enterprise Reality Begins

Every serious enterprise deployment I've seen converges on a gateway.

Not because it's elegant, but because it's unavoidable.

In Azure, this almost always leads to API Management. Not for routing. For policies.

Being able to inject a validate-jwt policy, enforce tenant-level throttling, or apply a custom PII-scrubbing regex before a request ever touches an OpenAI quota is the only way I've seen deployments survive a CISO review.

This gateway becomes the control plane for everything that matters:

• Identity enforcement
• Cost attribution
• Rate limiting
• Request inspection
• Model abstraction

Once applications talk to capabilities instead of models, architectural conversations finally become possible.

RAG Is Not an Integration Pattern. It's a Distributed System

I see Retrieval-Augmented Generation described as "LLM integration" all the time.

That framing is wrong.

RAG is a distributed system with an LLM component, and most failures happen far away from the prompt. If you want the full breakdown of what breaks in production RAG, I wrote about that in detail: Building Production RAG Systems: Lessons Learned.

In production Azure environments, the pattern that holds looks boring and deliberate:

• Azure Functions for deterministic document normalization
• Azure Data Factory for controlled ingestion runs
• Storage accounts segmented by data classification
• Vector stores deployed strictly in approved regions
• Identity propagated from caller through retriever to model

Asynchronous Processing Is Not an Optimization Choice

Synchronous LLM calls work for chat interfaces. They fail for enterprise workflows.

Anything involving document analysis, classification, enrichment, or summarization needs to be asynchronous from day one.

In practice, this means event-driven pipelines built with Service Bus, Durable Functions orchestrating retries and partial failures, and idempotent processing everywhere.

Latency impresses product managers. Predictability keeps systems alive.

Tool Calling: Where Pilots Go to Die

Tool calling looks powerful in demos. In production, it's where governance collapses.

We killed a "Support Agent" pilot because the model occasionally decided to issue a refund instead of opening a ticket. The compliance team didn't care about prompt probability. Their view was simpler:

If the system can hallucinate a refund, it will eventually hallucinate a fraud case.

Once framed that way, the discussion ended.

In regulated environments, tool invocation must be tightly bounded: whitelisted operations, deterministic schemas, and explicit approval layers for anything state-changing. In some cases, the correct decision is to not allow tool calling at all.

The business still gets value. The system just behaves like an enterprise system, not a demo.

EU Data Residency: The Control Plane Is the Risk

In EU deployments, model quality is rarely the deciding factor.

Control planes are.

I recently blocked a deployment of a technically superior vector database. Their "EU Region" managed service was essentially a control plane in Virginia pointing to a data plane in Frankfurt.

Under NIS2, that shared telemetry path is a non-starter for critical infrastructure. I wrote more about this in Designing for EU Data Sovereignty & GDPR Compliance.

Cost: The Most Dangerous Line Is "Pay-As-You-Go"

The most dangerous line in an enterprise budget is "pay-as-you-go."

LLM costs don't explode because people ask bad questions. They explode because architecture allows unbounded behavior.

We enforce hard caps at the subscription level now. Not soft alerts. Hard limits.

Successful patterns treat cost as a design constraint, not a reporting problem. For a deeper dive into how I approach cost governance: FinOps & cost governance.

Version Rot Is Real, and It Breaks Quietly

The other failure mode teams underestimate is model drift.

We built a prompt chain that worked perfectly on a pinned GPT-4 snapshot. When an alias moved underneath us, the JSON output structure changed just enough to break downstream parsers.

Nothing crashed immediately. Data quality degraded silently.

Observability Is About Behavior, Not Prompts

Logging prompts is easy. Understanding system behavior is not.

In production, we track token usage per workload, retrieval hit rates, latency distributions, failure modes, and cost per business capability.

Azure Monitor and Application Insights handle the mechanics. The discipline is deciding what questions you must always be able to answer.

When leadership asks why costs increased, "users are asking more questions" is not an acceptable response.

What I'd Do Again, and What I'd Avoid

The pattern is consistent.

LLMs succeed in enterprise when they are treated like infrastructure.

They fail when they're treated like magic.

Generative AI is magic.
But in the enterprise, magic is just another word for unmitigated risk.