As AI systems mature beyond prototypes, teams often run into a predictable problem:
they start mixing technologies that operate at completely different levels of the stack — treating them as interchangeable, overlapping, or competitive.
This leads to architecture that is:
- slower than it should be
- harder to maintain
- difficult to scale
- impossible to debug
- unnecessarily expensive
The real issue isn't the tools themselves — it's the misunderstanding of which part of the AI system each tool actually belongs to.
This post breaks down the AI stack from bottom to top, and clarifies the roles of:
- vLLM / inference engines
- LangChain / orchestration frameworks
- Vector databases
- Embedding models
- Routing & classification models
- Application logic
Once you understand these layers, the entire ecosystem makes sense — and your architecture becomes dramatically cleaner, faster, and easier to scale.
1. The Modern AI Stack at a Glance
Here's the real structure of a production-grade AI system:
┌───────────────────────────────────────────────┐
│ Application Layer │
│ - business logic, UX, permissions, APIs │
└───────────────────────────────────────────────┘
┌───────────────────────────────────────────────┐
│ Orchestration Layer │
│ (LangChain, LlamaIndex, custom pipelines) │
│ - multi-step workflows │
│ - RAG logic │
│ - chaining, memory, tool calling │
└───────────────────────────────────────────────┘
┌───────────────────────────────────────────────┐
│ Retrieval & Indexing Layer │
│ (vector DBs: Milvus, PGVector, Weaviate) │
│ - embeddings │
│ - chunking │
│ - semantic search │
└───────────────────────────────────────────────┘
┌───────────────────────────────────────────────┐
│ Inference Engine Layer │
│ (vLLM, TensorRT-LLM, TGI, SGLang) │
│ - fast token generation │
│ - batching & GPU optimisation │
│ - model loading & scheduling │
└───────────────────────────────────────────────┘
┌───────────────────────────────────────────────┐
│ Model Layer │
│ (Llama, Mistral, Gemma, Qwen, custom models) │
│ - the weights themselves │
└───────────────────────────────────────────────┘
Each layer has one job.
Problems occur when teams ask a tool to perform a job one layer above or below its purpose.
2. The Inference Layer (vLLM, TensorRT-LLM) — Performance & GPUs
Role: Run the model as fast as possible on GPU hardware.
This layer handles:
- token generation
- batching
- KV cache
- memory management
- parallelisation
- hardware scheduling
Tech in this layer:
- vLLM (best general-purpose performance)
- TensorRT-LLM (NVIDIA-optimised)
- HuggingFace TGI (easy distributed serving)
- SGLang (fast, multi-model serving)
What it does NOT do:
- retrieval
- RAG
- agents
- multi-step workflows
- prompt templating
It is simply the engine.
It turns model weights into tokens — nothing more, nothing less.
3. The Orchestration Layer (LangChain, LlamaIndex) — Logic & Workflows
Role: Coordinate multiple steps in a reasoning pipeline.
This layer provides:
- prompt templates
- multi-step chains
- memory
- tool calling
- response parsing
- agent logic
- RAG pipelines
Tech in this layer:
- LangChain
- LlamaIndex
- Haystack
- Custom FastAPI pipelines
What it does NOT do:
- fast inference
- GPU management
- model optimisation
This is the control plane.
It decides which models to call and in what order, but it does not generate tokens efficiently — it delegates that to the inference layer.
4. The Retrieval Layer (Vector DBs) — Search & Grounding
Role: Provide the model with relevant context.
This layer includes:
- chunking
- embedding
- vector indexing
- semantic search
- hybrid retrieval (keyword + vector)
- filtering (metadata, document type, freshness)
Tech here:
- Milvus
- pgvector
- Weaviate
- Pinecone
- Chroma
Retrieval prevents hallucinations by injecting real data into the LLM context.
What it does NOT do:
- reasoning
- content generation
- workflow orchestration
- GPU-optimized inference
It's simply the database for semantic knowledge.
5. Routing & Classification Models (Small Models) — Intelligence Before Intelligence
Role: Decide how a request should be processed before it hits an LLM.
Examples:
- Is this query factual or reasoning-based?
- Should it go through RAG or a summarisation model?
- Does it require a larger model?
- Does it require grounding?
- Is it safe? (moderation)
These are typically 2B–8B parameter models running locally — extremely fast.
This layer often saves:
- cost
- latency
- GPU usage
- LLM load
What it does NOT do:
- heavy reasoning
- long generation tasks
It's an intelligent router that makes the pipeline efficient.
6. The Application Layer — The Real Product
Finally, the top layer:
Role: Deliver the actual customer experience.
This is:
- your API
- your web interface
- your backend logic
- your permissions
- your identity flow
- your dashboards
- your business rules
This is where AI becomes a real product — not just a model.
Mistake many teams make:
They try to put LangChain or vLLM logic directly into the application itself, instead of letting each layer do its job.
The cleanest architectures separate them clearly.
7. Why Understanding These Layers Matters
When you know what each layer does, you avoid critical architectural mistakes:
1. Using LangChain for inference → slow & expensive
LangChain is not an inference engine; it's a workflow tool.
2. Using vLLM for RAG → impossible
vLLM doesn't know what a database is.
3. Using vector DBs for storage → terrible idea
They're search engines, not transactional stores.
4. Building custom pipelines when orchestration frameworks exist
Wastes time unless performance-critical.
5. Making the LLM the centre of the architecture
The LLM should be a component, not the whole system.
8. A Clean Example Architecture That Uses All Layers Correctly
Frontend App
↓
Backend (FastAPI/Node)
↓
Orchestrator (LangChain/LlamaIndex)
↓ ↓
Router RAG Pipeline
↓ ↓
small model vector DB
↓ ↓
inference request
↓
vLLM / TensorRT-LLM
↓
final answer → user
Every layer has a job.
Nothing leaks into layers where it doesn't belong.
This yields:
- predictable performance
- clean debugging
- lower cost
- scalable pipelines
- modular components
- better developer experience
Common Architectural Mistakes
Mistake 1: Treating Everything as One Layer
Problem: Using LangChain to call models directly, bypassing inference engines.
Impact: Slow performance, high costs, poor scalability.
Solution: Use LangChain for orchestration, vLLM for inference.
Mistake 2: Confusing Retrieval with Storage
Problem: Using vector DBs as primary data storage.
Impact: Data loss, poor transactional guarantees, expensive operations.
Solution: Use vector DBs for search, traditional DBs for storage.
Mistake 3: Skipping the Routing Layer
Problem: Sending all requests to the largest, most expensive model.
Impact: Unnecessary costs, slow responses, GPU waste.
Solution: Add routing models to classify and route requests intelligently.
Mistake 4: Mixing Application Logic with AI Logic
Problem: Business rules embedded in prompt templates or orchestration code.
Impact: Hard to maintain, test, and evolve.
Solution: Keep application logic separate from AI orchestration.
Mistake 5: Ignoring the Inference Layer
Problem: Using basic model serving without optimization.
Impact: Poor throughput, high latency, inefficient GPU usage.
Solution: Use specialized inference engines like vLLM for production.
How Each Layer Scales Independently
Application Layer
- Horizontal scaling via load balancers
- Stateless services
- Caching at API level
Orchestration Layer
- Stateless workflow execution
- Can scale horizontally
- State stored in external systems
Retrieval Layer
- Vector DBs scale with sharding
- Embedding models can be cached
- Index updates can be batched
Inference Layer
- GPU clusters with load balancing
- Model sharding across GPUs
- Batch processing for efficiency
Model Layer
- Model versioning and A/B testing
- Multiple model variants
- Gradual rollout capabilities
Performance Characteristics by Layer
Inference Layer
- Latency: 10-100ms per token
- Throughput: 1000-10000 tokens/sec
- Optimization: GPU utilization, batching
Orchestration Layer
- Latency: 50-500ms (depends on steps)
- Throughput: Limited by inference layer
- Optimization: Parallel execution, caching
Retrieval Layer
- Latency: 10-100ms per query
- Throughput: 1000-10000 queries/sec
- Optimization: Indexing, caching, filtering
Routing Layer
- Latency: 1-10ms per request
- Throughput: 10000+ requests/sec
- Optimization: Model quantization, local inference
Cost Optimization Through Layer Understanding
Use the Right Model for the Job
- Routing: 2B-8B models (cheap, fast)
- Simple tasks: 7B-13B models (moderate cost)
- Complex reasoning: 30B+ models (expensive, use sparingly)
Cache Aggressively
- Embeddings can be cached
- Retrieval results can be cached
- Common prompts can be cached
Batch When Possible
- Inference layer benefits from batching
- Multiple requests can share context
- Vector searches can be batched
Route Intelligently
- Not every request needs the largest model
- Classification prevents unnecessary expensive calls
- Fallback chains reduce costs
Building Production-Ready AI Systems
Phase 1: Foundation
- Set up inference layer (vLLM/TensorRT-LLM)
- Implement basic orchestration (LangChain)
- Set up vector DB for retrieval
Phase 2: Optimization
- Add routing layer for efficiency
- Implement caching strategies
- Optimize inference batching
Phase 3: Scale
- Add monitoring and observability
- Implement load balancing
- Set up A/B testing for models
Phase 4: Maturity
- Multi-model routing
- Advanced caching
- Predictive scaling
- Cost optimization
When to Use Each Technology
Use vLLM/TensorRT-LLM when:
- You need high-throughput inference
- You have GPU resources
- You're serving production traffic
- Latency is critical
Use LangChain/LlamaIndex when:
- You need multi-step workflows
- You're building RAG systems
- You need agent capabilities
- You want prompt management
Use Vector DBs when:
- You need semantic search
- You're building RAG systems
- You have large document collections
- You need hybrid search
Use Routing Models when:
- You have diverse query types
- You want to optimize costs
- You need to reduce latency
- You have multiple models
Final Thought: Build Systems, Not Just Model Calls
Building production AI systems is no longer about "call the LLM and return text."
It's about constructing layered systems, where each component:
- is responsible for one thing
- is optimised for its layer
- is not overloaded with responsibilities
When you understand where vLLM, LangChain, vector DBs, and routing models truly belong, your architecture becomes:
- easier to reason about
- faster to run
- cheaper to operate
- more reliable under load
- much easier to evolve as models improve
That's the difference between an AI demo and a production AI platform.
The tools aren't competing — they're complementary.
The architecture that recognizes this distinction is the one that scales.
If you're building production AI systems and need help designing the right architecture for your use case, get in touch to discuss how we can help structure your AI stack correctly.