Every Tool You Need to Build an LLM App in 2026 (One List)

I spent the last 2 weeks compiling every production-ready LLM tool I could find. Not research papers. Not demos. Tools you can actually deploy today. The result: a curated list organized by what you actually need to build. The Stack Here's the minimal stack for a production LLM application: 1. Inference → How you run the model 2. Vector DB → How you store embeddings (for RAG) 3. Framework → How you orchestrate prompts and chains 4. Monitoring → How you track quality and costs 5. Testing → How you ensure output quality Let me break down the best tool in each category. 1. Inference: vLLM (self-hosted) or Groq (API) Self-hosted: vLLM gives you the highest throughput for open models. Pair it with a quantized Llama 3 model and you get enterprise-grade inference at GPU rental cost. API: Groq has the fastest inference speeds I've seen — tokens come back nearly instantly. Their free tier is generous enough for prototyping. 2. Vector DB: pgvector (if you already use Postgres) Don't add another