Jina AI vs PropensityAI

Jina AI provides embeddings, rerankers, readers, and small language models for businesses and developers building search applications. Reader turns a supplied URL into clean text for language-model, retrieval-augmented generation, and agent systems; it can also search the web and return five results with URLs and content. The v5 omni embedding models accept text, images, audio, video, and PDFs in a shared vector space. Reranker v3 and v3.5 compare candidate documents with listwise scoring in one forward pass. Integrations include MongoDB, Qdrant, Pinecone, Weaviate, Milvus, LlamaIndex, Haystack, LangChain, and Dify. Model deployment is listed for AWS SageMaker, Microsoft Azure, and Google Cloud. Jina AI is available by API, on the web, and self-hosted. Free API keys allow 100 RPM and 100,000 TPM for Embeddings and Reranker; paid keys allow 500 RPM and 2,000,000 TPM. The free Toy Experiment plan includes 10 Million tokens for non-commercial use only under CC-BY-NC. One-time bundles cost $50 as a one-time purchase for Prototype Development and $500, paid once for Production Deployment.

PropensityAI combines retrieval and reasoning to answer questions across web pages, documents, and media with concise inline citations. It links claims to sources, flags uncertainty, and can decline when evidence is insufficient. Image analysis includes OCR, captioning, visual question answering, and structured extraction. Audio tools transcribe, identify speakers, summarize, and extract tasks. Video search locates scenes and spoken content with time-range citations. Users can query research PDFs and citations, then compare methods, results, and limitations. The maker describes connectors for shared drives, wikis, ticketing systems, and internal documents. Starter costs nothing, needs no sign-up, and allows up to 16 sources and one file per query. Local packages cost €8,000 as a one-time purchase for 30B Science & Logic or 30B Software Engineering, and €20,000, paid once for 130B General Intelligence. Team and Enterprise plans add shared connectors, controls, analytics, on-premises or VPC deployment, governance, and support. Enterprise can run local systems inside customer infrastructure, including air-gapped environments. The site says private queries and uploads are not used to train foundation models.