A zero-cost entry point into LLMOps using industry-standard open-source tools and generous free tiers.
Learning LLMOps often feels expensive due to high API costs and infrastructure overhead. You need a way to build and test end-to-end pipelines without a massive monthly bill.
You will have a functional RAG (Retrieval-Augmented Generation) pipeline running in production, capable of version-controlled updates and automated deployments.
Primary code editor for writing LLM chains and application logic.
Managing code versions and triggering automated deployment pipelines.
Framework for building complex chains, agents, and retrieval systems.
Storing and retrieving document embeddings for RAG pipelines.
Native integration triggers a new build on every git push.
LangChain has built-in support for Postgres/pgvector via SQLAlchemy.
This stack focuses on the core fundamentals of LLMOps: writing chains, managing vector data, and deploying serverless functions without any upfront financial commitment.
