Most LLM tutorials stop after making the first API call.
That's where the real work actually begins.
After building enterprise AI applications, I realized that the language model itself is only one component of a production system.
The real challenge is designing the infrastructure around it.
In this article, I'll share the architecture I use when designing production-ready LLM platforms.
The Architecture
Step 1 — Start with an API Gateway
Never expose your LLM directly.
Every request should first pass through an API Gateway responsible for:
- Authentication
- Rate limiting
- Logging
- Request validation
- API versioning
Example technologies:
- Azure API Management
- Kong
- NGINX
- Envoy
Step 2 — Add a Prompt Router
Not every request needs GPT-4.
Examples:
- FAQ → Small model
- Code generation → Coding model
- Long reasoning → Large model
- Internal documents → Local model
Routing requests can significantly reduce inference costs.
Step 3 — Build a Dedicated Embedding Service
Don't generate embeddings inside your application.
Create a separate service responsible for:
- Chunking
- Metadata
- Embeddings
- Versioning
This makes re-indexing much easier later.
Step 4 — Store Vectors
Popular choices include:
- Qdrant
- pgvector
- Azure AI Search
- Pinecone
- Weaviate
Choose based on scale and operational needs.
Step 5 — Add an LLM Gateway
Instead of calling OpenAI directly from your application:
Application
↓
LLM Gateway
↓
OpenAI / Claude / Local Models
Benefits include:
- Provider abstraction
- Retry logic
- Failover
- Usage tracking
- Cost reporting
Step 6 — Never Skip Observability
Track:
- Latency
- Token usage
- Cost
- Prompt failures
- Cache hit rate
- Retrieval quality
Without these metrics, optimizing your AI platform becomes difficult.
Common Mistakes
I often see teams making these mistakes:
❌ Hardcoding OpenAI calls
❌ No prompt routing
❌ No monitoring
❌ No caching
❌ Embeddings mixed into business logic
These choices may work for prototypes but usually become painful in production.
My Recommended Production Stack
- API Gateway
- Authentication
- Prompt Router
- Prompt Cache
- Embedding Service
- Vector Database
- LLM Gateway
- Monitoring
- Logging
Keeping these responsibilities separate makes the platform easier to maintain and evolve.
Final Thoughts
The LLM is only one part of the system.
The infrastructure around it determines whether your AI application is scalable, secure, and maintainable.
How are you designing your production AI stack?
I'd be interested to hear what components you've found essential—or which ones you wish you'd added sooner.
Further Reading
🌐 Official Website: https://aitechpartner.blog/
📖 Original article: https://medium.com/@patriwala/the-llm-infrastructure-architects-guide-part1-d725f9ceef23
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