OneKE vs Pinecone

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	OneKE RAG	Pinecone RAG
Tagline	Open-source multi-agent framework for schema-guided knowledge extraction from documents.	Managed vector database for production-scale similarity search.
Category	RAG	RAG
Pricing	Free· Free, MIT-licensed; you pay for LLM API calls or self-hosted compute	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads
Model	Multi-model (OneKE-13B, LLaMA3, Qwen2.5, GPT, DeepSeek-R1)	Hosted vector DB (not an LLM)
Editorial score	—	8.8 / 10
Use cases	knowledge-graph-constructionnamed-entity-recognitionrelation-extractionevent-extractiondocument-parsing	managed vector DBproduction RAG
Pros	Covers NER, RE, EE, and triple extraction in one framework Works with API models or fully local LLMs via vLLM Ingests PDF, Word, HTML, JSON, and plain text out of the box Multi-agent schema + reflection loop improves extraction quality MIT license with Docker and Streamlit UI included	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible
Cons	Documentation is primarily Chinese and scattered across Yuque/GitHub Self-hosting and tuning agents is non-trivial for non-researchers No managed cloud offering; you bring the infrastructure Quality depends heavily on the underlying LLM you wire in	Costs scale with vector count Less flexible than self-hosted
Website	openspg.yuque.com	www.pinecone.io

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