AI App Blueprint Guide
Most "AI app" architectures collapse into the same shape: a request hits your backend, you optionally retrieve relevant context (RAG), you call an LLM API, and you stream the response back while logging tokens for cost tracking. The interesting decisions are which LLM API to call, how much retrieval infrastructure you actually need versus how much you are over-building, and where to put rate limiting so one user chat loop does not blow your monthly bill.
Start with the smallest RAG pipeline that answers your actual question — many apps ship successfully with a single vector index and no reranking step at all — then add retrieval sophistication only when evaluation shows the simple version failing. Deploy the orchestration layer somewhere that supports streaming responses well (Vercel, Cloudflare Workers, or a standard Node/Python backend on Cloud Run or Fargate), since chat UIs depend on token-by-token streaming for perceived latency.
Choosing an LLM API
The three major hosted options are the OpenAI API (GPT-4o family, strong general reasoning and tool use), Anthropic API (Claude family, strong at long-context and instruction-following), and Google Gemini API (via AI Studio or Vertex AI, competitive pricing and native multimodal input). If you need model flexibility without managing multiple SDKs, a gateway like OpenRouter or AWS Bedrock (which hosts Claude, Llama, and others behind one API) lets you swap models without rewriting integration code. Do not default to the most expensive flagship model for every call — route classification, extraction, and short-answer tasks to a smaller/cheaper model tier and reserve the frontier model for the step that actually needs deep reasoning.
Vector database selection
For most apps, pgvector as a Postgres extension (available on AWS RDS, GCP Cloud SQL, Supabase, and Neon) is the right starting point — you avoid running a separate database system, and it is plenty fast under a few million vectors. If you need purpose-built scale or advanced filtering, Pinecone, Qdrant, and Weaviate are the leading managed vector stores; Cloudflare Vectorize is the edge-native option if your app already lives on Workers and you want the vector store co-located with your compute. Do not reach for a dedicated vector database before you have measured that pgvector is actually your bottleneck.
Designing the RAG pipeline
A baseline RAG pipeline is: chunk source documents (500–1000 tokens with overlap), embed with a model like OpenAI text-embedding-3-small or an open embedding model, store vectors plus metadata, then at query time embed the user question, retrieve top-k chunks, and stuff them into the LLM prompt with clear delimiters. Add a reranking step (Cohere Rerank or a cross-encoder) only if evaluation shows retrieval precision is the failure mode, not generation quality — the two are easy to confuse and require different fixes. Cache embeddings for unchanged source documents; re-embedding on every deploy is a common silent cost leak.
Cost control and rate limiting
Token costs are your main variable expense, so instrument every LLM call with token counts and cost estimates from day one — log input/output tokens per request tagged by user ID and feature, not just aggregate spend. Set hard per-user rate limits (requests per minute and tokens per day) enforced at the API gateway layer — Cloudflare Workers with KV-backed counters, or a Redis-based limiter (Upstash Redis works well on serverless) — before requests reach the LLM call. Cache identical or near-identical prompts to avoid paying for repeated generations, and set a monthly budget alert with your LLM provider directly (OpenAI, Anthropic, and Google Cloud all support billing alerts).
Deployment shape
Run the orchestration/RAG backend on a platform that supports long-running streaming HTTP responses — Vercel and Netlify functions handle this well for Next.js apps, Cloudflare Workers support streaming natively and add low-latency edge placement, and Cloud Run/Fargate work if you need longer execution windows or background embedding jobs. Keep the embedding/ingestion pipeline as a separate background job (a queue-triggered function or a scheduled task) rather than inline in the request path, so a large document upload does not block user-facing chat latency.
Frequently asked questions
Do I need a dedicated vector database like Pinecone to start?
No. pgvector on Postgres (RDS, Cloud SQL, Supabase, or Neon) handles most early-stage RAG workloads fine and keeps you on infrastructure you already run. Move to Pinecone, Qdrant, or Weaviate only when you have measured a real scale or latency limit.
How do I stop LLM costs from spiraling?
Instrument token usage per request from day one, enforce per-user rate limits at the gateway (Cloudflare Workers + KV, or Redis/Upstash), route cheap tasks to smaller models, cache repeated prompts, and set billing alerts directly with your LLM provider.
Should I fine-tune a model or use RAG?
Use RAG first — it is faster to iterate, cheaper, and keeps your knowledge base updatable without retraining. Fine-tuning makes sense for consistent style/format adherence or when RAG prompt-stuffing approach hits context-length or latency limits.
Which LLM API should I default to?
There is no universal answer — OpenAI, Anthropic, and Google Gemini are all strong general-purpose choices with different strengths (tool use, long-context instruction-following, multimodal input). If you want flexibility to switch, use a gateway like OpenRouter or AWS Bedrock rather than hardcoding one SDK deeply into your app.