Why AI Infrastructure Matters
Building an AI application is only half the battle. Getting it to production — reliably, efficiently, and cost-effectively — requires thoughtful infrastructure design.
Core Components
Model Serving
The foundation of any AI application is how you serve your models. Common approaches include:
- API-based: OpenAI, Anthropic, Google — easiest to start, pay-per-use
- Self-hosted: vLLM, Ollama, TGI — more control, higher upfront cost
- Serverless: AWS Lambda + model endpoints — scalable, cold start concerns
Vector Databases
For RAG applications, you'll need a vector store. Here's a quick comparison:
| Database | Best For | Hosting |
|---|---|---|
| Pinecone | Production, managed | Cloud |
| Weaviate | Flexible, open source | Self-hosted / Cloud |
| ChromaDB | Local dev, prototyping | Local / Embedded |
| Qdrant | High performance | Self-hosted / Cloud |
Example: Simple RAG Pipeline
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings, persist_directory="./db")
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
retriever=vectorstore.as_retriever(search_kwargs={"k": 3})
)
result = qa.run("How do I set up vector embeddings?")
print(result)Cost Optimization
Key strategies for keeping costs under control:
- Cache responses — avoid re-computing for repeated queries
- Use smaller models — not everything needs GPT-4
- Batch requests — reduce API overhead
- Monitor usage — set up alerts for unexpected spikes
- Optimize prompts — shorter prompts = lower costs
Monitoring
Track these key metrics:
- Latency (p50, p95, p99)
- Error rates
- Token usage per request
- Cost per query
- Cache hit ratio