Haystack

Haystack connects documents, language models, and retrieval systems into production-ready NLP pipelines, handling the plumbing so you can focus on the AI logic. It's plumbing for AI apps: connecting your data sources to language models to output. Apache 2.0. Built by deepset. The pipeline architecture lets you chain components (retrievers, readers, generators, rankers) into workflows. Supports OpenAI, Hugging Face, Cohere, and local models. Native integrations with vector databases like Qdrant, Weaviate, and Chroma.

Fully free and open source. deepset offers a managed platform (deepset Cloud) for enterprise deployments with collaboration and evaluation features, but the framework itself has zero restrictions. Self-hosting is straightforward. pip install, define your pipeline in Python. The ops burden depends on what you connect: a simple RAG pipeline is trivial, a multi-step agent with retrieval and re-ranking needs more infrastructure.

Solo to small teams: free, great developer experience. Medium teams: evaluate deepset Cloud when you need shared pipelines and evaluation dashboards. Large: deepset Cloud or your own orchestration layer.

The catch: Haystack is a framework, not a product. You still need to pick your models, vector store, and deployment strategy. And if you're already deep in the LangChain ecosystem, migrating has real cost. The abstractions are different enough that it's not a drop-in swap.