The AI Infrastructure Stack: What You Need to Build Production AI Apps

Beyond the API Call

Every tutorial makes AI look easy: call an API, get a response, done. But production AI applications need an entire infrastructure stack that nobody talks about.

The Full Stack

┌─────────────────────────────┐
│     Application Layer       │
│  (Your app, API, frontend)  │
├─────────────────────────────┤
│     Orchestration Layer     │
│  (Agent frameworks, chains) │
├─────────────────────────────┤
│      Model Layer            │
│  (APIs, local models, MCP)  │
├─────────────────────────────┤
│      Data Layer             │
│  (Vector DB, cache, store)  │
├─────────────────────────────┤
│    Observability Layer      │
│  (Logging, tracing, evals)  │
└─────────────────────────────┘

1. Vector Databases

Store and search embeddings for RAG applications:

• Supabase pgvector — Great if you are already on Postgres, zero new infrastructure
• Pinecone — Managed, fast, simple API
• Weaviate — Open-source, hybrid search (vector + keyword)
• ChromaDB — Lightweight, perfect for prototyping

2. Caching Layer

AI API calls are expensive. Cache aggressively:

• Semantic caching — Cache based on meaning, not exact match
• Response caching — Store full responses for repeated queries
• Embedding caching — Avoid recomputing embeddings for unchanged documents

Redis with a vector similarity plugin handles all three patterns well.

3. Orchestration Frameworks

Coordinate multi-step AI workflows:

• LangChain/LangGraph — The most popular, extensive tool ecosystem
• Claude Agent SDK — Purpose-built for Claude, clean abstractions
• CrewAI — Multi-agent orchestration with role-based agents
• Custom — For simple use cases, a well-structured async loop often beats a framework

4. Observability

You cannot improve what you cannot measure:

What to log:

• Every prompt sent (with template variables separated)
• Token usage per request
• Latency (time to first token + total)
• Tool calls made and their results
• User feedback signals

Tools:

• Langfuse — Open-source LLM observability
• Helicone — Proxy-based logging with analytics
• Braintrust — Evals + logging in one platform

5. Evaluation (Evals)

The most underrated part of the stack. Without evals, you are flying blind:

# Example eval: does the response contain accurate information?
def eval_accuracy(prompt, response, ground_truth):
    score = llm_judge(
        f"Rate 1-5 how accurately this response answers the question. "
        f"Question: {prompt} "
        f"Response: {response} "
        f"Ground truth: {ground_truth}"
    )
    return score >= 4

Run evals on every model change, prompt change, and RAG index update.

6. Safety and Guardrails

Production AI needs boundaries:

• Input validation — Reject prompt injection attempts
• Output filtering — Block PII, harmful content, off-topic responses
• Rate limiting — Per-user and per-endpoint limits
• Cost controls — Budget alerts and automatic cutoffs

The Minimum Viable Stack

Starting out? Here is the simplest production-ready setup:

1. Supabase — Database + pgvector + auth + edge functions
2. Claude API — Model provider
3. Simple logging — Store prompts/responses in a Supabase table
4. Basic evals — A handful of test cases you run before deploying prompt changes

You can build a remarkably capable AI application with just these four components. Add complexity only when you have evidence you need it.