AI Dev Tools

The most widely used LLM API. GPT-4o and GPT-4o mini cover most use cases. Best-in-class function calling and JSON mode.

Claude models excel at long documents, coding, and instruction following. 200k context window is the largest available.

Run open-source models (Llama, Mistral, Stable Diffusion) via API without managing infrastructure. Pay per second.

Blazing fast inference for open-source models. If latency matters, Groq runs Llama and Mixtral at speeds nothing else matches.

The easiest way to add streaming AI to a Next.js app. Provider-agnostic — works with OpenAI, Anthropic, and more out of the box.

The most popular framework for building LLM pipelines. Great for RAG, agents, and chaining complex workflows.

Purpose-built for RAG and document Q&A. If your use case is "chat with your data", LlamaIndex handles it more cleanly than LangChain.

The gold standard for vector search. Fully managed, scales to billions of vectors, and has a generous free tier to get started.

If you're already using Supabase, pgvector gives you vector search without adding another service. Good enough for most apps.

Open-source vector database you can self-host or use managed. Great performance and a clean API.

The easiest way to deploy Next.js. Free tier is generous, edge functions are fast, and the DX is unmatched.

Deploy backends, databases, and workers with almost no config. Great for when you need more than serverless functions.

Postgres database, auth, storage, and edge functions in one. The fastest way to add a backend to an AI app.

Trace, debug, and evaluate LLM calls in production. Essential once you have real users hitting your AI features.

Lightweight proxy that logs all your LLM API calls. Easy to drop in and gives you cost tracking and latency metrics immediately.