You donโt need a huge budget to build serious AI systems.
Here is the 0$ AI Architecture Stack 2026 Edition.
Thanks to Rathnakumar Udayakumar for sharing this!
This AI stack covers the core pieces of agentic applications:
1. Frontend Layer
Next.js or Streamlit can build the interface, with Vercel handling hosting.
2. Agent Orchestrator
LangGraph and CrewAI can manage agents, state, decisions, and workflows.
3. RAG Pipeline
Notion can provide knowledge, while Chroma and Qdrant handle vector storage.
4. LLM layer
Ollama lets developers run compatible open models directly on local machines.
5. Tool use via MCP
MCP allows agents to interact with GitHub, Slack, databases, files, and more.
6. Code Agent
Claude Code CLI and Aider can assist with building and debugging software.
7. Data Layer
SQLite, DuckDB, Supabase, and Phoenix can support data, tracing, and evaluation.
8. Deployment Layer
Docker, Cloudflare Workers, and Hugging Face offer flexible deployment options.
The real lesson is not building everything at low cost.
It is understanding each layer before investing in expensive infrastructure.
Strong AI systems come from smart architecture, not massive spending.
Start small, learn the architecture, then scale what works.
P.S. Which layer would you prioritize when building your first agent?
โป๏ธ Repost to help builders create more with less infrastructure.
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